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GPT Proto Expands AI Model Catalogue with Support for Google’s Gemini 3.1 Pro Preview

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Former OpenAI & Google AI Experts Launch HyperDev

GPT Proto Launches Unified AI API Platform for Seamless Multi-Model Access

Hong Kong-based API platform adds Google’s latest multimodal model to its growing roster, expanding developer access to frontier AI through a unified gateway

GPT Proto, a multi-model AI API platform operated by Talent Tech Global Limited, today announced the integration of Google’s Gemini 3.1 Pro Preview into its developer API gateway. The addition marks the platform’s latest expansion of its model catalogue, which now spans offerings from Google, OpenAI, Anthropic, Meta, and other leading AI labs.

Gemini 3.1 Pro Preview, released by Google in early 2026, is the company’s most capable multimodal model to date. It supports extended context windows, multi-step logical reasoning, and the processing of text, code, and structured data within a single API call. The model has drawn attention from the developer community for its performance on complex reasoning tasks and its applicability to agentic workflows and large-scale document analysis. For a detailed integration guide, see the Nano Banana 2 Guide on the GPT Proto developer blog.

Expanding Access to Frontier Models

GPT Proto’s platform provides a unified RESTful API compatible with OpenAI’s SDK, allowing development teams to access multiple AI models through a single integration point. The platform currently supports dozens of models and is designed for use cases ranging from software engineering and scientific research to content generation and business intelligence pipelines.

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According to the company, the inclusion of Gemini 3.1 Pro Preview responds to growing demand from its developer user base for access to Google’s latest generation of models. GPT Proto states that all models on the platform are accessible through a standardised API interface, eliminating the need for teams to manage multiple provider accounts or API keys.

Executive Commentary

“Our goal has always been to reduce the barriers between developers and the tools they need to build,” said Sammi Cen, Founder and CEO of Talent Tech Global Limited. “Adding Gemini 3.1 Pro Preview to our platform means that teams working with our API can immediately incorporate Google’s most advanced reasoning model without a separate onboarding process or billing relationship.”

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Platform Capabilities

The GPT Proto gateway supports streaming responses, function calling, batch processing, and model routing. The platform is used by developers building applications including retrieval-augmented generation (RAG) pipelines, code generation tools, automated content systems, and AI-powered analytics dashboards. Documentation for the Gemini 3.1 Pro Preview integration is available on the GPT Proto developer portal.

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Seekr and GDIT Collaborate to Accelerate Development of Secure, Trusted Agentic AI Solutions for Government

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Seekr and GDIT Collaborate to Accelerate Development of Secure, Trusted Agentic AI Solutions for Government

Companies will accelerate digital transformation, enhance decision-making and increase efficiencies across federal agencies

Seekr, a leading generative and agentic AI technology company, announced that it will collaborate with General Dynamics Information Technology (GDIT) to develop agentic AI solutions for government missions. Through this collaboration, Seekr will combine its differentiated, secure AI offerings with GDIT’s deep mission and integration expertise. The companies will leverage the SeekrFlow™ Enterprise AI Platform to rapidly develop and deploy solutions that will enable enhanced decision-making and resilience, increased efficiencies, and cost savings across federal agencies.

SeekrFlow™ is a complete end-to-end AI operating system that unifies model hosting, fine-tuning, agent orchestration, and full agent observability in a single platform purpose-built for the most demanding environments, including air-gapped, disconnected, and tactical edge settings. Deployed across the U.S. Army, U.S. Navy, and other defense agencies, and awardable through the CDAO Tradewinds Solutions Marketplace, Seekr has established itself as a trusted AI provider for mission-critical government operations. Unlike fragmented solutions that require stitching together multiple tools, SeekrFlow Agents give organizations a secure, specialized, and fully deployable solution on-premises and in the cloud, enabling faster decision-making and reducing the time and overhead required to operationalize AI at scale.

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“Our collaboration with GDIT brings secure, transparent, and mission-ready AI to the heart of government operations,” said Rob Clark, President of Seekr. “By combining Seekr’s agentic AI with GDIT’s proven leadership in federal mission delivery, we’re enabling agencies to move faster, operate smarter, and achieve outcomes once thought impossible.”

“Federal agencies need cutting-edge emerging technology capabilities to meet the pace and complexity of today’s missions,” said Ben Gianni, GDIT senior vice president and chief technology officer. “Our collaboration with Seekr will enable us to deliver differentiated, agentic AI solutions that enable our customers to advance missions faster, smarter and more securely.”

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Together, Seekr and GDIT are advancing high-impact emerging capabilities for federal civilian, state and local, and defense customers, including prototyping innovative, AI-powered solutions that streamline processes and enhance delivery of essential government services. These use cases deploy AI agents to optimize case management; detect, evaluate, and prioritize risk and fraud; and comb through disparate and disconnected data sources to identify and prioritize policy-aligned courses of action.

Seekr is a proud participant in GDIT’s full-suite ecosystem of Digital Accelerators and Centers of Excellence, working closely with GDIT technologists and mission owners to research, develop, and scale innovative and repeatable solutions. For example, Seekr is helping to bring autonomous and adaptive AI capabilities into the Security Operations Center (SOC) of the future leveraging GDIT’s Eclipse and Luna AI Digital Accelerators.

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Emporix and ACR Deploy AI-Driven Commerce Automation – Reducing B2B Order Processing Time by Up to 87%

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Emporix and ACR Deploy AI-Driven Commerce Automation - Reducing B2B Order Processing Time by Up to 87%

An AI-powered orchestration layer now interprets, validates, and autonomously processes PDF-based purchase orders – cutting handling time from ~8 minutes to under 60 seconds

Emporix, the cloud-native provider of a next-generation digital commerce platform with orchestration and AI-driven intelligence at its core, has successfully deployed an AI-powered order automation solution with ACR (formerly AmerCareRoyal), a single stream resource for essential packaging and preparation products used in the foodservice, janitorial, sanitation, industrial, hospitality, and healthcare industries.

At the heart of the initiative is an AI-driven orchestration layer that autonomously interprets unstructured purchase order documents, validates business logic, and triggers downstream ERP actions – without human intervention. The result: order processing times reduced from approximately 8 minutes to under 60 seconds in early deployment scenarios, representing a time savings of up to 87%.

The initiative represents a key execution milestone within ACR’s broader enterprise AI strategy. As part of the company’s AI Framework Program and Center of Excellence — led by Chief Information Officer Thai Vong  — it demonstrates how structured enterprise AI can move beyond isolated efficiency gains to become a foundational capability for scalable, autonomous commerce operations. The solution went live in Q1 2026 and is already delivering measurable improvements in speed, accuracy, and operational efficiency.

“This initiative reflects how we’re applying enterprise AI to drive operational precision while strengthening the customer experience,” says Thai Vong, Chief Information Officer at ACR. “Reducing manual friction improves reliability across the value chain and allows our teams to focus on higher-impact work.”

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From Manual Processes to Intelligent Automation

Following years of growth through acquisition, ACR operated across a diverse and evolving digital landscape while managing a high volume of email-based purchase orders. Although many transactions flowed through established EDI channels, a portion still required manual entry into the ERP system. This created additional workload for the customer service team and introduced opportunities for occasional downstream adjustments and added coordination across teams.

The Emporix platform – built on a modular architecture combining orchestration, composable commerce, and agentic AI – provided the foundation for a scalable, intelligent approach. Thanks to its headless, API-first architecture and integrated orchestration engine, Emporix enabled ACR to automate the entire order intake process without requiring a disruptive replatforming effort.

“We didn’t need an RFP. What we needed was a partner who could move quickly, integrate cleanly, and support our roadmap,” Vong added. “Emporix checked every box.”

Rapid Implementation, Measurable Results

Despite the complexity of ACR’s multi-system environment, the project was delivered within six months. Weekly syncs, close coordination with internal IT, and Emporix’s solution-first approach ensured a smooth rollout. A phased implementation gave ACR time to test, adapt, and build confidence in the new process while minimizing operational risk.

Early KPIs show remarkable improvements:

  • Order processing time reduced from ~8 minutes to <1 minute
  • Error rates significantly reduced
  • Customer service workload shifted from manual tasks to value-added interactions
  • Automation coverage expanding across additional workflows

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Building the Foundation for Autonomous Commerce

Emporix currently underpins a range of ACR capabilities, including a customer portal providing real-time visibility into orders, invoices, and pricing; return management automation; a customer-facing product catalog; and a centralized digital asset management layer. Plans are in place to expand into cart, checkout, and account self-service, with further integration of AI agents into orchestration workflows.

“This isn’t just about solving today’s problems. It’s about building an agile digital foundation that supports where we’re going next — scaling automation, integrating acquisitions, and evolving toward true digital commerce maturity”, Vong concluded.

This approach aligns with the BOAT concept (Business Orchestration and Automation Technologies) as defined by Gartner — the convergence of RPA, business process automation, iPaaS, and workflow technologies. ACR is building on top of that foundation: with intelligent agents operating within orchestrated workflows, the company is moving from process automation toward agent-driven commerce execution, where operational decisions are handled autonomously across systems.

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Loyalzoo Unveils Integrated CRM and Advanced Loyalty Functionality for New MX™ POS Smart Terminals

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Loyalzoo Unveils Integrated CRM and Advanced Loyalty Functionality for New MX™ POS Smart Terminals

Loyalzoo, the pioneer in digital customer engagement for independent retailers, today announced a strategic partnership with Priority to introduce a natively embedded CRM system into Priority’s new MX™ POS point-of-sale platform. This offers merchants a sophisticated yet simple way to manage customer relationships and activate loyalty programs as their business grows.

In an industry where customer data is often fragmented, this partnership provides MX POS merchants with a unified, “built-in” CRM from day one. This foundation allows businesses to seamlessly activate powerful loyalty and reward features as an integrated add-on.

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Empowering Merchants with Smart Growth Tools:

  • Integrated CRM with Seamless Scalability: All MX POS merchants gain immediate access to a robust, built-in CRM that tracks buying behavior and purchase history from day one. This provides a clear, no-setup path to understanding customer value, allowing businesses to start with core data management and easily layer on advanced reward structures and tiered loyalty as they grow.
  • Data-Driven Precision & AI Customer Segmentation: The system empowers merchants with a sophisticated engine to build their own customer segments using AI. Using granular data – from specific products purchased and total spend to visit frequency and individual preferences – merchants can segment their audience with total flexibility. This allows for highly targeted outreach via SMS, email, or push notifications, ensuring that every message is tailored to the exact buying behavior and information that matters most to the customer.
  • In-Store and Mobile Convenience: Customers enjoy a frictionless experience, with the ability to receive points updates and store their loyalty profile directly via email, SMS or in Apple or Google wallets – no separate app downloads required.

“Since 2014, our goal has been to provide independent businesses with the sophisticated tools usually reserved for major chains,” said Massimo Sirolla, CEO of Loyalzoo. “By acting as the core CRM within MX POS, we aren’t just giving merchants a loyalty program; we’re giving them a growth engine. Our smart features do the heavy lifting, allowing owners to reward behavior and reach out to customers based on real insights, all through a single, seamless interface.”

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“At Priority, we believe that payments should be more than just a transaction—they should be a catalyst for growth,” said Greg Spatola, Vice President of POS Operations at Priority. “Integrating Loyalzoo’s embedded loyalty and CRM directly into the MX POS ecosystem allows our merchants to build lasting brand affinity and reward their customers’ unique buying behaviors effortlessly.”

The integration focuses on rewarding specific buying behaviors and purchase milestones, ensuring that every marketing effort is relevant and data-driven. For MX POS merchants, this means less time managing software and more time building authentic connections with their community.

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Snapshot and MindsDB Announce Strategic Partnership to Deliver AI‑Powered Solutions for the NetSuite Ecosystem

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Snapshot and MindsDB Announce Strategic Partnership to Deliver AI‑Powered Solutions for the NetSuite Ecosystem

Snapshot, a leading NetSuite technology consulting firm based in Detroit, Michigan and active NetSuite ecosystem partner, and MindsDB, the San Francisco-based AI platform for enterprise data and analytics, announced a strategic partnership aimed at bringing powerful, practical AI-driven conversational analytics solutions to organizations operating within the NetSuite ecosystem.

The partnership combines MindsDB’s Enterprise AI platform with Snapshot’s deep operational expertise in NetSuite environments, enabling companies to apply AI directly to their business data and unlock new levels of business intelligence across their operations with a window into both NetSuite data, and multiple connected systems.

At the core of the collaboration is MindsDB’s Minds Enterprise platform acting as the AI backbone, enabling models, agents, automation, and AI-driven conversational analytics to connect seamlessly with enterprise data sources. Snapshot will build on top of this foundation by layering its extensive NetSuite domain knowledge, ERP data modeling expertise, and applied AI capabilities, translating complex NetSuite data structures into AI-ready intelligence.

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Together, the companies are building solutions designed specifically for organizations running NetSuite while also enabling intelligence across multiple connected systems and enterprise data sources. The platform will allow companies to combine NetSuite ERP data with other operational datasets including commerce platforms, supply chain systems, CRM platforms, warehouse systems, and external market data to power AI-driven operational decision making.

“Our partnership with MindsDB allows us to deliver AI capabilities, autonomous AI agents, and accurate statistical analytics that are deeply connected to how NetSuite customers actually operate,” said Tania Sottrel, SVP at Snapshot. “Snapshot has spent years working inside NetSuite environments and understanding the structure of ERP data, business processes, and reporting requirements. By combining that expertise with MindsDB’s powerful AI backbone, we can deliver solutions that transform NetSuite data along with other operational data into meaningful intelligence for businesses.”

As partners within the NetSuite ecosystem, Snapshot is focused on building solutions that integrate naturally with NetSuite implementations and extend the platform’s analytical capabilities. The collaboration aims to move organizations beyond traditional ERP reporting by introducing AI-assisted analytics, predictive modeling, and intelligent automation directly connected to NetSuite workflows and operational data.

“MindsDB was built to bring AI directly to where enterprise data lives,” said Brad Gyger, Chief Revenue Officer at MindsDB. “By partnering with Snapshot, we’re combining our conversational AI analytics platform with a team that deeply understands NetSuite environments and the operational challenges companies face. Together we’re enabling NetSuite customers to unlock AI insights across their ERP data and the broader systems that power their business.”

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The partnership will initially focus on industries where NetSuite is widely used for operational and supply chain management, particularly within complex distribution environments.

  • Fastener and industrial component distribution
  • Automotive parts and aftermarket supply chains
  • Food manufacturing and distribution
  • Agriculture and landscaping supply
  • HVAC distribution
  • Plumbing supply
  • Electrical distribution

Companies in these industries manage large product catalogs, complex supply chains, and rapidly changing demand conditions. By combining NetSuite ERP data with other operational datasets, Snapshot and MindsDB will enable AI-driven capabilities such as:

  • Predictive demand forecasting
  • Inventory and supply chain optimization
  • Pricing and margin analysis
  • Operational anomaly detection
  • AI-assisted reporting and decision support
  • Cross-system operational intelligence
  • AI-powered insights embedded within NetSuite-driven workflows

Snapshot will lead the development of NetSuite-informed AI solutions, including purpose-built models, configurable AI agents, ERP knowledge layers, and connectors that translate NetSuite data structures into AI-ready frameworks.

“Our goal is to empower the broader NetSuite community with AI that understands how their businesses actually operate,” added Sottrel. “By combining Snapshot’s ERP expertise with MindsDB’s AI platform, we’re creating a powerful intelligence layer that connects NetSuite data with the rest of the enterprise.”

The NetSuite connector for MindsDB is available now and additional solutions and pilot programs will roll out to NetSuite customers and partners throughout 2026.

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Guideline Expands Its Ad Intelligence Insights with Local Dynamics, Bringing Transaction-Level Benchmarking to Local Ad Markets

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Guideline Expands Its Ad Intelligence Insights with Local Dynamics, Bringing Transaction-Level Benchmarking to Local Ad Markets

New Insights subscription delivers category-level benchmarks across 175+ DMAs across the United States, covering OOH, TV, radio and digital, with analysis across 100+ local advertising sub-categories.

Guideline announced a new expansion of its Ad Intelligence Insights with the launch of Local Dynamics, a new subscription report delivering recurring analysis of advertising investment across local markets.

These new insights provide visibility into spending patterns and benchmarking national ad spend against more than 175 designated market areas (DMAs) and major media channels including out-of-home (OOH), television, radio and digital. Designed for agencies, publishers and station groups, it highlights category trends, shifts in media mix and emerging revenue opportunities across categories and DMAs. Unlike traditional market intelligence built on estimates or panels, Local Dynamics Quarterly is powered by real transaction-level data.

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As budgets move across platforms and regions, many organizations lack reliable benchmarks to understand where demand is growing and which categories are driving investment. Much of the available market intelligence relies on estimates or survey-based data, leaving significant gaps in how local advertising activity is measured.

Local Dynamics Quarterly addresses this challenge with verified advertising investment data and consistent quarterly reporting. The report tracks spending across more than 100 product and service sub-categories investing in local media, helping organizations identify growth areas and align sales strategies with evolving market demand.

Drawing on Guideline’s proprietary advertising intelligence dataset, the report provides a detailed view of how advertising investment is distributed across markets and media channels, offering greater transparency into category drivers and spending trends.

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Subscribers can:

  • Track category-level advertising demand across local ad markets
  • Pinpoint which categories are driving local revenue growth
  • Analyze investment trends across OOH, TV, radio and digital media
  • Arm sales teams with data-backed pricing narratives
  • Monitor shifts between local and national advertising investment
  • Evaluate spending patterns across 100+ advertising sub-categories
  • Identify underpriced inventory vs market benchmarks

Each quarterly edition provides standardized analysis across markets, channels and categories, enabling organizations to track changes in advertising demand and make more informed revenue and planning decisions.

“Local advertising is often at the forefront of the changing media landscape from geotargeting to emerging economic trends. But these markets often lack effective ways to measure or analyze these dynamics,” said Sean Wright, Chief Insights and Analytics Officer at Guideline. “With Local Dynamics Quarterly, we’re bringing greater insight into how advertising dollars are moving across categories, channels and markets. The goal is simple: give agencies and publishers a clearer signal on demand to get ahead of the market and focus sales efforts on the next growth opportunity.”

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The Directions Group Launches Linara, an Integrated Intelligence Platform Transforming Research into Always-On Decision Support

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The Directions Group Launches Linara, an Integrated Intelligence Platform Transforming Research into Always-On Decision Support

The Directions Group announced the launch of Linara™, an AI-powered integrated intelligence platform that will transform static research into always-on decision support for insights, marketing, and leadership teams. Its first capability, Linara Personas, gives organizations conversational access to their customer personas and lays the foundation for a broader suite of tools to come.

Organizations are operating in markets that are more volatile, cluttered, and competitive than ever, yet many still rely on static reports, point-in-time studies, and opinion-driven debates to make critical decisions. Linara is designed for this moment, helping teams move faster while staying grounded in real customer understanding.

“Brands don’t always need more data. They need clearer answers, faster, and in the context of their business,” said Elizabeth (Beth) Finn, President & CEO of The Directions Group. “This platform gives our clients a way to have a real conversation with the intelligence they already own, so they can move from ‘what do we know?’ to ‘what should we do?’ with more confidence.”

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Not Another Generic AI Tool

In a moment when everyone has an AI tool, most are still generic, trained only on public data and detached from the realities of a specific business and the fundamentals of data integrity. Linara takes a different path. It’s built on each client’s proprietary research and insights, and will be molded to reflect real-world customers, competitive context, and strategic questions.

At The Directions Group, artificial intelligence is used to amplify human intelligence, and Linara reflects that philosophy. AI is always applied intentionally and with care, grounded in rigorous research and human expertise so teams can move faster without sacrificing thoughtfulness, context, or integrity in their decisions.

Instant Access to Customer Insights

Linara begins with Personas, a conversational interface that transforms segmentation from a static framework into an active, ongoing dialogue that teams can access anytime. With Personas, clients can:

  • Get instant, conversational answers about who each persona is, what drives them, and how they differ.
  • Evaluate scenarios and “what if” questions for new propositions, concepts, and messaging angles.
  • Explore segment-level implications for campaigns, portfolio decisions, and customer experience design.

“Segmentation is one of the most powerful, and most underused, assets inside an organization,” said Finn. “Linara Personas keeps that investment alive and in use every day. It’s like having a strategist who knows your segments inside and out, available on demand whenever a new question comes up. And when a question or business problem goes beyond what Linara Personas can do, our team of consultants and research experts is ready to step in.”

Over time, The Directions Group will expand Linara with additional capabilities. Future development areas will include, but not be limited to, additional marketplace intelligence and connecting insights across multiple research studies.

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Building with Clients

Linara is being developed in close partnership with early client adopters. Organizations that come on board in the initial phases will have immediate value from Linara Personas at launch, input into the Linara innovation roadmap, and opportunities to participate in beta testing as new capabilities are released.

“I’ve been working with The Directions Group team to pilot Linara and have seen first-hand how it helps sharpen messaging to specific segments,” said Shane Harrison, Senior Strategy Analyst at Compeer Financial. “It’s also a powerful strategic tool helping us to think differently about when, how, and where to invest our resources, and the conversations around new products and service ideas have been especially insightful. We don’t have anything else like this.”

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KnowBe4 Launches AIDA Orchestration as the First Fully Autonomous Agent for Human Risk Management

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KnowBe4 Launches AIDA Orchestration as the First Fully Autonomous Agent for Human Risk Management

New AI Agent From KnowBe4 Cuts Security Training Administration From Hours to Seconds

KnowBe4, the world-renowned platform that comprehensively addresses human and agentic AI risk management, has announced the launch of AIDA Orchestration, the eighth AI-powered agent in KnowBe4’s suite of AI agents for human risk management known as AIDA (Artificial Intelligence Defence Agents).

The AIDA Orchestration agent is an autonomous, AI-powered system for human risk management. It independently creates, schedules and manages personalised phishing security tests (PSTs) and security awareness training (SAT) at a user level that dynamically adapts to each person’s risk profile. This eliminates manual campaigns, reduces administrative burden and efficiently lowers organisational risk.

By reducing the time required to create personalised training from hours to seconds, the Orchestration agent frees security teams to focus on strategic initiatives while ensuring every individual receives the right training at the right time to reduce organisational risk.

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The agent brings the following to organisations:

  • Individual-Focused Personalisation: Departing from group-wide campaigns, the agent delivers unique phishing tests and training experiences based on real-time user performance.
  • Always-On Operations: The system continuously monitors evolving threat landscapes and user engagement, dynamically adjusting strategies without human intervention.
  • Intelligent Ecosystem Integration: AIDA Orchestration leverages the full suite of AIDA agents, including Template Generation and Remedial Training, to create a cohesive, data-driven security culture.
  • Plan-Based Oversight: While the agent handles tactical execution, administrators maintain strategic control through “Plans”, which define high-level constraints and guardrails for specific user groups.

This year marks ten years of the beta version of AIDA. With eight specialised agents available in-market, KnowBe4’s position of training humans and agents is reinforced as the only agentic human risk management provider in the industry.

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According to the KnowBe4 State of Human Risk Report 2025, cybersecurity leaders rank AI-powered threats as their top security risk, with 45% citing constantly evolving AI threats as their greatest challenge. AI enables adversaries to remove traditional indications of an attack, generate realistic language at scale and craft messages tailored to specific roles, industries and even individuals.

“The launch of AIDA Orchestration represents a fundamental shift in how organisations approach human risk,” said Bryan Palma, CEO at KnowBe4. “By moving from static, one-size-fits-all campaigns to an always-on, autonomous system, we are enabling security teams to deliver the right training at the right time. This saves hours of administrative work and it reduces organisational risk by treating every employee as an individual with unique security needs.”

An anonymous customer who has already been using KnowBe4’s AIDA Orchestration highly recommends the agent saying, “AIDA Orchestration is a game changer and time saver!”

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Palantir and Moder Partner to Transform Mortgage Industry

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Palantir and Moder Partner to Transform Mortgage Industry

Moder and Palantir are co-building an AI-powered mortgage operations platform, with first pilot customer Freedom Mortgage, a new milestone toward empowering the dream of homeownership

Palantir Technologies Inc., a global leader in operational artificial intelligence platforms, and Moder, the fastest growing AI and technology outsourcing company in mortgage, announced a strategic partnership to co-build an AI-powered mortgage operations platform with first pilot customer Freedom Mortgage, bringing Palantir’s capabilities around data and AI to the forefront of the mortgage industry alongside Moder’s domain expertise.

The co-built platform leverages Palantir’s Ontology to provide an agentic AI framework to integrate with existing systems of record today. By translating guidelines and operational policies into configurable, testable, and auditable rules, the new platform is designed to help teams execute critical processes with greater precision and scale.

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In early deployments with Freedom Mortgage, the platform has already transformed and is live with several key processes, driving meaningful value through improved speed and accuracy—benefiting operating agents and homeowners.

“This strategic partnership will reshape the future of our industry,” said Michael Middleman, Chairman of Moder. “Together, we’re building technology that can help improve affordability, lower borrowing costs, and expand access to homeownership for millions of Americans.”

“Combining our deep expertise in the mortgage industry with Palantir’s data and AI capabilities, we’re already seeing measurable results improving the homeownership experience and helping mortgage servicers run more efficiently,” said Moder President and CEO Erik Anderson. “This latest technology will accelerate at scale our ability to deliver customized and automated solutions to all our clients in multiple industries.”

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“Freedom Mortgage is excited about the tremendous impact this strategic partnership between Moder and Palantir will have on the way we operate and the speed and ease by which we service our customers across the nation,” said Mike Patterson, Senior Executive Vice President and Chief Operating Officer, Freedom Mortgage.

“We’re energized by the opportunity to collaborate with the team at Moder, who share our mission-first mindset and our belief in the transformative power of smart, scalable innovation,” said Elias Davis, Office of the CEO, at Palantir. “Homeownership is a cornerstone of the American dream, and through this partnership and our Ontology, we can now unify data through the full mortgage cycle and orchestrate governed AI workflows end-to-end to serve more homeowners, more efficiently, and more accurately.”

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Keeper Security Introduces KeeperDB, Integrating Zero-Trust Database Access into KeeperPAM

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Keeper Security Introduces KeeperDB, Integrating Zero-Trust Database Access into KeeperPAM

New capability embeds a secure, zero-trust database interface directly into the Keeper Vault, eliminating exposed credentials, unmanaged tools and insecure access paths

Keeper Security, the leading zero-trust and zero-knowledge identity security and Privileged Access Management (PAM) platform, announces KeeperDB, a new vault-embedded database access capability that enables secure, policy-controlled database interactions directly from the Keeper Vault. KeeperDB enables developers, database administrators and security teams to work with sensitive data through a unified interface that simplifies workflows while maintaining strict access governance. KeeperDB will be officially launched at RSA Conference 2026.

Enterprise databases are among the most sensitive assets in any organization, yet access is often managed through a mix of desktop tools, shared credentials and network tunnels, which provide limited visibility and control. Databases are frequent targets of cyber attacks and insider misuse, and fragmented tools substantially increase risk of credential exposure, data exfiltration and audit gaps while inhibiting least-privilege access.

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KeeperDB broadens KeeperPAM with a beautiful, vault-native interface that unifies database session management within the zero-trust and zero-knowledge platform. Access is governed by centralized policies and fully recorded for audit and compliance purposes. By embedding database access directly into the Vault, KeeperDB helps reduce credential sprawl, standardize database access workflows and strengthen audit readiness across cloud and on-prem environments.

“Database access has historically been one of the most used yet least-governed areas of enterprise security,” said Darren Guccione, CEO and Co-founder of Keeper Security. “KeeperDB brings database management into the vault – allowing organizations to apply the same zero-trust controls, visibility and auditing they rely on for privileged access – without introducing new tools, credentials or attack paths.”

KeeperDB enables users to launch database sessions directly from a database record in the Keeper Vault, with the option to connect through either a Graphical User Interface (GUI) or Command-Line Interface (CLI). Initial support includes MySQL, PostgreSQL, Oracle and Microsoft SQL Server.

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Key benefits include:

  • Eliminating credential exposure by ensuring database credentials are never revealed to users or stored on endpoints.
  • Reducing data exfiltration risk through granular controls such as read-only access and governed data transfer policies.
  • Strengthening audit readiness with full visual session recording of database activity,
  • Standardizing and centralizing database access within the Keeper Vault, replacing fragmented tools and unmanaged workflows.
  • Improving usability for technical teams by providing a modern, browser-based interface without sacrificing zero-trust controls.

For organizations that continue to rely on existing database clients, KeeperDB will be complemented by KeeperDB Proxy, which enables secure connections through Keeper while maintaining centralized policy enforcement, credential protection and session visibility. Additional details on availability will be provided alongside upcoming Keeper Gateway and Keeper Vault releases.

“Most database access today happens through disparate tools that sit outside security controls,” said Craig Lurey, CTO and Co-founder of Keeper Security. “We built KeeperDB so teams can work the way they’re used to, but inside a zero-trust environment. It’s a simpler, safer way to manage database access that enhances productivity.”

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Netlify Turns AI Prompts Into Production-Ready Software

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Netlify Turns AI Prompts Into Production-Ready Software

About Netlify

Netlify, the platform where agents and developers build together, announced a new way to create software on Netlify. Teams can now start a new project from a prompt at netlify.new using Agent Runners. Choose from a leading coding agent (Claude Code, Codex or Gemini CLI) and get a live web app on Netlify in minutes.

It’s not enough to help a builder get something live quickly. You have to give them a real project on infrastructure that’s ready for production.”

— Matt Biilmann, co-founder and CEO of Netlify

AI has changed how software gets built, but most tools still force a tradeoff. You can move fast with prompts, or you can build on a platform designed to support you all the way to production. Netlify is designed to be both. Whether a project starts with AI or in code, it starts on the same platform teams can keep using as they scale. For developers, that means a faster path from idea to real software without setup overhead or rework later.

“Coding agents are becoming the new way people start software. That changes what a platform needs to do. Netlify has to offer the best agent experience in the market,” said Matt Biilmann, co-founder and CEO of Netlify. “It’s not enough to help a builder get something live quickly. You have to give them a real project on infrastructure that is ready for production.”

Marketing Technology News: MarTech Interview with Miguel Lopes, CPO @ TrafficGuard

Unlike AI tools that often break down as projects get more serious, Netlify lets teams keep building on the same project. Code and prompts run on the same project, the same infrastructure, and the same workflow. Developers work in code while teammates iterate with prompts. No rebuild or migration later.

“If your project takes off and goes to production, everything you need is already there,” said Clark Wimberly, design engineer and co-founder of Superfun, a studio that builds websites, design systems, and apps.

Netlify’s built-in platform capabilities, like serverless functions, Identity, Blobs, Forms, and AI Gateway are available from the start. That means teams can move beyond a prototype without stopping to stitch together the infrastructure they need to ship.

Marketing Technology News: Disrupt or Be Disrupted: The AI Wake-Up Call for B2B Marketers

This launch also opens a new path for enterprise teams adopting AI-assisted building across functions. Netlify’s new Internal Builder seat offers governance and role-based access built in, allowing product managers, designers, marketers, and other internal teams to build with agents inside their Netlify organization. Engineering keeps oversight of what reaches production, making it easier for teams to prototype internal tools and build internal apps together, without stepping outside the workflows and controls they already use.

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Suplari Launches Spend Analytics for the AI Era

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Suplari Launches Spend Analytics for the AI Era

Suplari Logo

Solution delivers actionable procurement intelligence from day one backed by nine years of AI investment and a data platform built for scaling AI agents

Suplari, the procurement intelligence company, announced Spend Analytics, a complete reimagining of spend analysis built for the AI era. Unlike legacy tools that require months of data preparation and produce static reports, Suplari’s solution automatically unifies, cleanses, and enriches spend data from any source and delivers actionable savings opportunities, compliance gaps, and supplier risks from day one.

We’ve been building for AI for nine years. Adding our LLM-powered agent was a very natural, easy fit. Our APIs are rich, and AI can leverage them to deliver a higher quality output.”

— Jeff Gerber, CEO and Co-founder, Suplari

The release marks a decisive step in Suplari’s mission to move procurement teams beyond traditional analytics and into what co-founder and CEO Jeff Gerber describes as “the agentic operating system for procurement.”

The Spend Analytics Problem Nobody Has Solved

Enterprise procurement teams have long struggled with a fundamental paradox: they need clean, trustworthy data to generate insights, but cleaning data takes so long that the insights arrive too late to act on. With information scattered across ERPs, P2P systems, contract repositories, and spreadsheets, often with conflicting supplier records and inconsistent classifications, teams spend more time fixing data than analyzing it.

Marketing Technology News: MarTech Interview with Miguel Lopes, CPO @ TrafficGuard

According to Gerber, this is the single biggest barrier he sees customers face. “A lot of companies feel like their data’s so bad they couldn’t even start. That’s really never the case,” he said in a recently. “We’ve built out a product and a process that gets you to that first milestone quickly, and then gives you the roadmap to perfect your data in a journey that delivers more value over time.”

Start with the Answer, Then Prove It

Suplari Spend Analytics inverts the traditional approach to procurement analytics. Rather than presenting procurement teams with dashboards, charts, and tables and asking them to find the opportunities, Suplari’s AI agents surface prioritized savings opportunities, anomalies, and risks automatically — then provide the full analytical trail to validate each finding.

“The idea was we’d start with the answer and let you go to the analytics to prove that it was a real opportunity, versus having to sift through charts and graphs and tables to find these opportunities,” Gerber said. “That was the real inspiration for Suplari.”

Marketing Technology News: Disrupt or Be Disrupted: The AI Wake-Up Call for B2B Marketers

This approach is powered by an AI data platform that Suplari has been developing since 2016 — years before the current wave of generative AI. The platform treats data quality as a continuous, automated process rather than a prerequisite, ingesting data from multiple systems, resolving conflicts, normalizing supplier records, and enriching the results with third-party risk, ESG, and market data to create a single governed source of truth.

Nine Years Building for AI — Not Retrofitting It

In a market where virtually every procurement vendor now claims AI capabilities, Gerber draws a clear distinction between platforms purpose-built for AI and those that have added it as an afterthought.

“We were doing AI before AI was cool,” Gerber said. “We’ve been building for AI for nine years, and so now adding our LLM-powered agent was a very natural, easy fit. Our APIs are rich, and AI can leverage them in really intimate ways and deliver higher quality output than if you had just retrofitted AI on the platform.”

The result is a platform where AI agents don’t just answer questions — they continuously monitor spend patterns, detect anomalies, identify consolidation opportunities, flag contract leakage, and even help orchestrate the actions needed to capture value. Suplari’s agents adapt to each customer’s priorities, whether that’s tail spend reduction, category fragmentation, regulatory compliance, or supplier risk management.

From Cost Center to Strategic Function

Gerber sees Suplari Spend Analytics as more than a technology upgrade. In an environment where procurement teams face shrinking headcount and growing mandates, he believes the solution helps individual procurement leaders demonstrate strategic value to the C-suite.

“Procurement oftentimes is looked at as a roadblock,” Gerber said. “We can help organizations understand that actually, there is a lot of value you can drive from this function. If you’re slinging spreadsheets and PowerPoints around, that’s probably not what you should be doing nowadays if you can automate that with AI.”

Some Suplari customers are already pushing the boundaries of traditional spend analytics, connecting procurement data with CRM and revenue data to identify balance-of-trade patterns and even drive sales outreach — turning procurement from a cost center into a growth enabler.

Key Capabilities of Suplari Spend Analytics
– Automatic data ingestion and unification from ERP, P2P, contract, and financial systems
– AI-powered cleansing, classification, and continuous enrichment with third-party data
– Prioritized savings opportunity detection across tail spend, category fragmentation, contract leakage, and pricing variance
– Natural language analytics that let teams answer complex spend questions in seconds
– AI agents that continuously monitor, detect, and help orchestrate procurement actions
– One-to-two-week implementation to first milestone — no months-long data preparation required

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Kubit Integrates with Snowflake to Deliver Warehouse-Native Product Analytics in the Snowflake AI Data Cloud

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Kubit Integrates with Snowflake to Deliver Warehouse-Native Product Analytics in the Snowflake AI Data Cloud

Joint customers can unify product analytics and business intelligence directly within their Snowflake environments—without moving or duplicating data.

Kubit, the digital analytics company, announced an integration with Snowflake, the AI Data Cloud company, to deliver warehouse-native digital analytics powered by Snowflake. Through this integration, joint customers can analyze customer behavior and business performance on governed Snowflake data—eliminating silos and accelerating time to insight.

Modern enterprises increasingly rely on Snowflake as their single source of truth. However, product analytics and business intelligence tools often operate in separate systems, creating duplicated pipelines, inconsistent metrics, and governance challenges.

Kubit addresses this by executing queries directly within customers’ Snowflake environments. By natively querying the Snowflake platform, Kubit enables product, analytics, and growth teams to analyze customer journeys, behavioral events, and core business metrics—including revenue, acquisition cost, and lifetime value—using a single, governed data foundation.

“We built Kubit to help organizations move faster with analytics they can trust,” said Alex Li, Founder and CEO, Kubit. “By running directly in the Snowflake AI Data Cloud, we deliver warehouse-native digital analytics and explainable AI on governed data. Teams can connect customer behavior with business impact without introducing new silos or black-box systems.”

Marketing Technology News: MarTech Interview with Miguel Lopes, CPO @ TrafficGuard

Governed AI Agents Built on Snowflake Data

As organizations make their data AI-ready, Kubit extends its warehouse-native architecture with AI agents that generate and execute SQL directly in Snowflake. These agents operate within existing role-based access controls and apply consistent metric definitions through a dynamic semantic layer.

These AI agents enable teams to:

  • Detect anomalies across behavioral and business metrics
  • Diagnose root causes of metric shifts
  • Generate reports from natural language prompts
  • Deliver narrative summaries grounded in verifiable Snowflake queries

Because every AI-generated insight is executed live within Snowflake, organizations maintain visibility, governance, and auditability—supporting enterprise AI adoption with trust.

Marketing Technology News: Is the Traditional CDP Already Out of Date?

Customer validation: Serko

Serko, a global travel technology company, uses Kubit on Snowflake to power self-serve product analytics for its Booking.com for Business platform.

“We had the data in Snowflake, but getting answers took weeks. Kubit helped us turn warehouse data into self-serve product analytics so product teams could move faster without changing our Snowflake-first strategy.”
— Karol Wojciechowski, Product Operations Manager, Serko

By running analytics directly within the Snowflake AI Data Cloud, Serko eliminated duplicated pipelines and enabled product managers and analysts to access governed behavioral insights on demand.

“Kubit’s integration reflects the power of the Snowflake AI Data Cloud to support enterprise-ready analytics and AI,” said Matt Hill, Director of Platform and ISV Partnerships, Snowflake. “We look forward to driving deeper value for Snowflake’s AI Data Cloud ecosystem through collaboration with Kubit to provide governed, explainable analytics directly through Snowflake’s single, integrated platform.”

Industry-leading applications are powered by Snowflake. By building tools and solutions on Snowflake, product and engineering teams are able to develop, scale, and operate without operational burden while delivering differentiated analytics capabilities to their customers.

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Glow.B Unveils AEO And GEO Solutions for the Generative AI Search Era

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Glow.B Unveils AEO And GEO Solutions for the Generative AI Search Era

BYAHT Inc. Official Site

BYAHT Inc. launches Glow.B’s AEO and GEO services to help brands secure visibility in AI-generated answers from platforms like SearchGPT and Perplexity.

BYAHT Inc., the innovator behind the AI-powered creator marketing agentic SaaS ‘Glow.B,’ officially announced the commercialization of its proprietary Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) solutions. This strategic launch marks a significant shift in digital marketing, moving beyond traditional Search Engine Optimization (SEO) to ensure brand visibility within the rapidly evolving generative AI search ecosystem.

As generative AI platforms such as SearchGPT, Perplexity, and Google’s AI Overviews redefine how consumers discover information, the traditional battle for search engine rankings is undergoing a fundamental transformation. In this new era, the primary challenge for brands is no longer just appearing in a list of links, but being cited as a trusted, authoritative source by AI models.

To address this paradigm shift, Glow.B has completed an intensive five-month R&D phase focused on AEO and GEO-based content strategies. The newly launched service provides a comprehensive technical pipeline that analyzes AI search patterns and deploys creator-led content specifically structured for AI indexing and citation.

Marketing Technology News: MarTech Interview with Miguel Lopes, CPO @ TrafficGuard

“The future of brand marketing depends on becoming a trusted source that AI agents actively recommend,” said Dong-gyu Kim, CEO of BYAHT Inc. “With Glow.B’s AEO and GEO solutions, we are not just driving traffic; we are building brand authority in the generative AI era by combining sophisticated data analysis with high-impact creator content.”

Glow.B’s platform utilizes specialized AI agents to automate the creator marketing process. These agents identify the most compatible creators for a brand and design content structures—including semantic keyword flow and information hierarchy—that are optimized for AI model consumption. This ensures that when a user queries an AI engine, the brand is significantly more likely to be featured in the generated response.

Marketing Technology News: Disrupt or Be Disrupted: The AI Wake-Up Call for B2B Marketers

Currently supporting marketing efforts across 19 countries, including the United States, South Korea, and Japan, BYAHT Inc. aims to lead the global MarTech industry by continuously evolving its AI-driven optimization technologies to meet the demands of the next generation of search.

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Mirasys Appoints Steve Johnson as Computer Vision Manager to Strengthen the Dell Partnership

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Mirasys Appoints Steve Johnson as Computer Vision Manager to Strengthen the Dell Partnership

Home - Mirasys

Mirasys Appoints Steve Johnson as Computer Vision Manager to Strengthen Dell Partnership and Deliver White-Glove Hardware-to-VMS Solutions

Mirasys, a global leader in open, high-performance video management software (VMS), announced the appointment of Steve Johnson as its Dell Computer Vision Manager. In this role, Steve will serve as a strategic liaison between Mirasys and Dell Technologies, strengthening the combined hardware and software offering delivered to enterprise and public-sector customers through a white-glove, value-driven approach.

Steve’s role is critical as customers increasingly demand clarity & confidence across their entire video stack — His ability to bridge Dell’s infrastructure expertise with Mirasys is game-changing.”

— Carl Raubenheimer – CEO Mirasys USA

Steve brings extensive experience across computer vision, AI-driven analytics, and enterprise infrastructure. His focus will be to ensure customers receive tightly aligned, validated solutions that combine Dell’s infrastructure capabilities with Mirasys’ open, high-performance VMS platform.

“I’ve spent my career at the intersection of video, infrastructure, and analytics, and Mirasys sits exactly where the market is shifting,” said Steve Johnson, Computer Vision Manager at Mirasys. “Customers want intelligent video systems that perform at scale without being locked into rigid ecosystems or unpredictable cost models. By aligning best-in-class hardware with Mirasys’ VMS, we can deliver that with confidence.”

Marketing Technology News: MarTech Interview with Miguel Lopes, CPO @ TrafficGuard

Strengthening the Dell–Mirasys Value Proposition
In his role, Steve will act as a technical and strategic bridge between Dell and Mirasys, ensuring customers benefit from:
– Optimized and validated hardware configurations
– Seamless integration between infrastructure and VMS
– White-glove guidance from design through deployment
– Long-term performance, reliability, and predictable TCO

“Our goal is to make hardware and software feel like a single, cohesive solution — one that reduces risk, simplifies deployment, and maximizes long-term value,” Steve added. ”

Role and Near-Term Focus
Over the next 90 days, Steve will focus on:
– Aligning Dell infrastructure capabilities with the Mirasys product roadmap
– Strengthening collaboration with channel partners and integrators
– Developing repeatable, validated solution frameworks for priority verticals
– Enhancing customer experience through prescriptive, white-glove engagement

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Over the next 12–24 months, his role will support Mirasys’ expansion into larger, more complex enterprise environments by ensuring hardware and software scale together without compromising reliability or cost control.

Executive Perspective
Carl Raubenheimer, CEO of Mirasys, commented on the appointment:
“Steve’s role is critical as customers increasingly demand clarity and confidence across their entire video stack,” said Raubenheimer. “His ability to bridge Dell’s infrastructure expertise with Mirasys’ open VMS platform enables us to deliver a true white-glove experience — one that prioritizes performance, reliability, and long-term value.”

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Leading Provider AI.cc Simplifies Enterprise AI Adoption by Consolidating 400 Models into a Single High-Performance API

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Indexly Announces Mission to Build the AI Visibility OS for the Modern Internet

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The global artificial intelligence landscape in 2025 and 2026 has reached a fever pitch, characterized by an explosion of generative models and a paradigm shift in how businesses integrate intelligence into their workflows. As the market moves from experimental pilot programs to full-scale enterprise deployment, the complexity of managing multiple AI providers has become a significant bottleneck. Enter AI.cc (AICC), a comprehensive ecosystem that has evolved from a premium domain into a multi-dimensional infrastructure powerhouse. By consolidating over 400 high-performance AI models into a single, unified API, AI.cc is effectively lowering the barrier to entry for the generative AI era.

The Challenges of the Multi-Model Era

In the current technological climate, Large Language Models (LLMs) are iterating at a monthly pace. For developers and enterprise architects, this rapid evolution presents a dual-edged sword. While capabilities are increasing, the overhead of managing diverse API keys, varying documentation standards, and disparate billing systems is becoming unsustainable. Furthermore, the risk of “vendor lock-in” looms large; a company heavily invested in a single provider’s ecosystem may find itself at a competitive disadvantage if a superior model is released by a rival firm.

AI.cc addresses these pain points directly through its “One API” philosophy. By providing a centralized technical hub, AI.cc allows enterprises to remain agile, cost-effective, and technologically resilient in a volatile market.

Key Takeaways: Why AI.cc is the Standard for Enterprise AI
Unmatched Model Diversity: Access to over 400 models covering text, image, video, 3D, voice, and OCR.
Simplified Integration: A single endpoint (https://api.ai.cc) compatible with standard OpenAI formats.
Cost Optimization: Significant operational savings ranging from 20% to 80% compared to direct-from-vendor pricing.
Enterprise-Grade Performance: Unlimited TPM/RPM (Tokens/Requests Per Minute) with ultra-low latency for high-frequency agentic workflows.
Infrastructure Resilience: Elimination of single-provider dependency through a robust, serverless architecture.
Technical Architecture: The Power of “One API”

Marketing Technology News: MarTech Interview with Miguel Lopes, CPO @ TrafficGuard

The core of the AI.cc value proposition is its high-performance model aggregation platform. Unlike traditional middleware, AI.cc acts as a sophisticated technical abstraction layer. Developers no longer need to learn the nuances of Google Gemini, Anthropic Claude, or Meta’s Llama series individually. Instead, by simply updating the base URL in their existing code to https://api.ai.cc, they gain instant access to a curated library of the world’s most powerful models.

Unified Billing and Compliance

For large organizations, the administrative burden of AI adoption is often overlooked. Procurement departments struggle with managing dozens of individual subscriptions and auditing the usage of various API keys across different departments. AI.cc solves this by centralizing financial management. With a unified billing system and sophisticated permission auditing, enterprises can maintain strict compliance while allowing their developers the freedom to experiment with the latest models from OpenAI, DeepSeek, ByteDance, and more.

Scalability and Performance

AI.cc is built on a high-performance serverless architecture designed for horizontal scaling. In the 2025-2026 landscape, the demand for AI is no longer limited to simple chatbots. We are seeing the rise of high-frequency autonomous agents that require massive throughput. AI.cc supports these requirements with virtually unlimited concurrency, ensuring that enterprise-level “Agentic” workflows never hit a performance ceiling.

Marketing Technology News: Disrupt or Be Disrupted: The AI Wake-Up Call for B2B Marketers

Ready to streamline your AI operations?

The AI industry is currently undergoing a fundamental transformation. In late 2025, the focus shifted from “passive chat interfaces” to “active autonomous agents.” These agents do not just answer questions; they negotiate, exchange information, and close business loops independently. This is known as the Agent-to-Agent (A2A) communication network.

AI.cc is at the forefront of this transition. By integrating next-generation models like GPT-5.2 and Claude 4.5 Opus, which feature enhanced “operational reliability,” AI.cc serves as the essential infrastructure for these agents. The platform acts as a traffic hub where agents can interact across different model backends seamlessly. In this context, AI.cc is no longer just a tool for human assistance—it is the foundational layer for the future’s automated economy.

Data Excellence: The 7.3T AICC Corpus

In the generative AI stack, data is the “new oil,” but quality is the refinement process that determines value. AI.cc is not merely a consumer of models; it is a significant contributor to the global AI research community. Through its AICC (AI-ready Common Crawl) initiative, the platform has constructed a massive multi-lingual corpus consisting of 7.3 trillion (7.3T) tokens.

Scientific Contribution and Model Performance

Utilizing the advanced MinerU-HTML extraction technology, the AICC corpus has set new benchmarks for data quality. Internal testing and external benchmarks demonstrate that models trained on the AICC corpus achieve an average accuracy of 50.82% across 13 key benchmarks—significantly outperforming datasets like RefinedWeb and FineWeb. This emphasizes the value of “Extraction Quality” in building web-scale datasets. By making tools like MainWebBench and MinerU-HTML public, AI.cc has solidified its position as a scientific leader in AI infrastructure, providing a powerful technical endorsement for its commercial web-scraping and data services.

The Future of Compute: AICCTOKEN and DePIN

As the demand for GPU compute continues to outpace supply, the reliance on centralized cloud giants like AWS and Google Cloud presents a strategic risk for AI companies. AI.cc is addressing this through its AICCTOKEN project, which leverages Decentralized Physical Infrastructure Networks (DePIN).

Democratizing High-Performance Computing

The AICCTOKEN ecosystem allows for the democratization of GPU power. By creating a decentralized marketplace for compute, AI.cc offers several critical advantages to its users:

Reduced Costs: Developers can rent compute power on-demand, avoiding the “GPU tax” associated with long-term contracts from centralized providers.
Censorship Resistance: A decentralized network is inherently more resilient and less susceptible to the restrictions of a single entity.
High Availability: By pooling global resources, AI.cc ensures that high-performance training and inference remain accessible even during peak global demand.
Strategic Value: Transforming AI into a Utility

The true genius of the AI.cc model lies in its ability to transform AI from a “proprietary technology” into a “commodity utility.” In the current market, the specific model used is becoming less of a competitive moat than the efficiency, cost, and stability with which that model is deployed. AI.cc provides the “pipes and electricity” for the AI age.

For industry practitioners, the choice is clear. Attempting to build and maintain individual integrations for 400 different models is an exercise in diminishing returns. By leveraging AI.cc, firms can focus on what truly matters: building innovative applications and delivering value to their end-users, while leaving the complex infrastructure management to the experts.

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Announcing peerspot.ai: Your AI Marketing Agent that Creates High-Quality Content Using Customer Proof

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Announcing peerspot.ai: Your AI Marketing Agent that Creates High-Quality Content Using Customer Proof

Peerspot Reviews & Ratings 2026

PeerSpot’s new AI agent instantly transforms voice-of-customer feedback into blogs, one-pagers, social assets, battlecards, and more.

PeerSpot announced the launch of peerspot.ai, a generative AI agent that makes it effortless for B2B marketers to create high-performing content using their most powerful asset: the voice of their customer.

For years, marketing teams have treated reviews primarily as “badges” or static proof points. To extract more value, they had to manually sift through hundreds of quotes to build a single case study or slide deck. peerspot.ai changes this dynamic entirely, giving marketers an “always-on” engine that turns that raw data into finished marketing assets in seconds.

“Marketers are under immense pressure to produce more high-quality content with fewer resources,” said Russell Rothstein, Founder and CEO at PeerSpot. “We built peerspot.ai to generate marketing content with ease using your customers’ actual experiences. We are turning reviews from a passive trophy into active fuel for your daily marketing engine.

High-Quality Content, Zero “AI Fluff” Unlike general-purpose AI tools that often generate generic or hallucinated copy, peerspot.ai is grounded exclusively in PeerSpot’s verified review data. This ensures that every blog post, report, and social caption is not only generated instantly but is also factually accurate and deeply authentic.

Marketing Technology News: MarTech Interview with Miguel Lopes, CPO @ TrafficGuard

The platform acts as an AI strategist that helps marketers:

  • Write detailed blog posts based on specific user use cases and industries.
  • Create social media assets optimized for LinkedIn, turning user sentiment into viral cards.
  • Build PDFs and reports that summarize buyer feedback for sales, SDR, and product teams.

Marketing Technology News: Disrupt or Be Disrupted: The AI Wake-Up Call for B2B Marketers

“UiPath values the depth and quality of the long-form reviews we receive on PeerSpot because they capture authentic customer insight. peerspot.ai unlocks even more value from that foundation by instantly transforming verified customer voice into targeted quotes, social assets, and campaign content. Because the content is grounded in structured review data, it’s also LLM-ready, helping us amplify authentic customer insight across sales, marketing, and AI-driven buyer discovery.” – Dana Ionescu, Senior Customer Marketing Manager, UiPath

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Why MarTech audits are becoming a critical step before scaling digital advertising spend?

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Why MarTech audits are becoming a critical step before scaling digital advertising spend?

Many businesses increase advertising budgets, expecting immediate growth in conversions and revenue. In theory, allocating more funds to digital advertising should translate into greater visibility, higher engagement, and stronger sales performance. However, in practice, simply increasing ad spend does not guarantee improved outcomes.

In many cases, businesses discover that scaling their advertising budgets amplifies existing inefficiencies rather than delivering the growth they anticipated. Without a clear understanding of how campaigns are currently performing, additional investment can reinforce the same problems that were limiting results in the first place.

Digital advertising today operates within a highly complex ecosystem. Marketing teams often manage multiple channels simultaneously, including search advertising, social media campaigns, display networks, and programmatic platforms. Each channel involves its own targeting methods, bidding strategies, analytics dashboards, and creative formats.

At the same time, customer journeys have become more fragmented, with users interacting with brands across devices, platforms, and touchpoints before making a purchasing decision. As a result, determining what is truly driving performance—and what may be hindering it—has become increasingly difficult for marketing teams.

Scaling campaigns in such an environment without proper evaluation can lead to significant inefficiencies. For example, inaccurate conversion tracking may cause businesses to misinterpret which campaigns are actually generating value. Poorly structured advertising accounts can result in overlapping audiences, redundant keywords, or misaligned bidding strategies.

In some cases, ineffective ad creatives or unclear messaging may limit engagement even when budgets increase. When these issues remain unresolved, increasing spend may accelerate the rate at which marketing budgets are wasted rather than improving overall return on investment.

Another challenge is the growing complexity of marketing technology stacks. Modern marketing operations often rely on a wide range of tools and platforms, including analytics systems, customer data platforms, campaign management software, and advertising automation tools. While these technologies offer powerful capabilities, they can also create operational complexity if they are not properly configured or integrated. Data discrepancies between platforms, incomplete tracking setups, and disconnected reporting systems can make it difficult for marketers to gain a clear view of campaign performance.

As a result, organizations are increasingly recognizing the importance of conducting structured evaluations of their marketing infrastructure before expanding advertising investments. Rather than immediately increasing budgets, many businesses are choosing to analyze their existing marketing systems, campaign structures, and data accuracy to identify potential issues that could limit performance. This proactive approach allows marketing teams to uncover hidden inefficiencies and develop a clearer understanding of how their advertising ecosystem is functioning.

This is where MarTech audits are becoming an essential part of modern marketing strategy. A MarTech audit provides a comprehensive review of the technologies, processes, and data systems that support digital advertising activities. Instead of focusing only on campaign performance metrics, these audits examine the broader marketing infrastructure to determine whether tools are configured correctly, whether tracking systems are accurate, and whether campaigns are structured in a way that supports optimal results.

By examining marketing technologies, campaign setups, and analytics frameworks, a MarTech audit helps organizations identify technical gaps and strategic weaknesses that might otherwise go unnoticed. It allows businesses to verify that conversion tracking is functioning correctly, that campaign structures align with performance goals, and that advertising platforms are being used effectively. This level of analysis ensures that marketing teams are not making decisions based on incomplete or misleading data.

Ultimately, conducting a MarTech audit before scaling advertising spend enables businesses to move forward with greater confidence. Instead of relying on assumptions about what may drive growth, organizations can base their investment decisions on accurate insights and clearly identified opportunities for improvement. As digital advertising continues to evolve and marketing ecosystems grow more sophisticated, MarTech audits are increasingly becoming a critical step in ensuring that advertising investments are both efficient and sustainable

The Risks of Scaling Advertising Without Analysis

It sounds easy to develop your digital advertising: just spend more and expect greater results. But if you don’t know what is really making a campaign work, raising the budget can soon lead to wasted money. This is why many businesses now use MarTech insights and audits to make sure their marketing platforms and campaigns are ready to grow.

  • The Pressure to Scale Advertising Quickly

In the fast-paced world of digital marketing, companies often think that spending more on ads would automatically bring in more visitors, more sales, and more money. This assumption is occasionally correct, but increasing campaigns without first checking their performance might cause great difficulties. Companies are increasingly using MarTech solutions to run campaigns, track results, and improve performance as digital ecosystems get more complicated. If you don’t evaluate these systems properly, spending more on advertising may make problems worse rather than fixing them.

Companies often increase their advertising when they notice early success or want to stay ahead of the competition in crowded markets. But if you don’t think carefully about this method, it can easily waste resources. Use MarTech platforms to determine whether campaigns are ready to grow, providing the data and insights you need. Marketers may make investment decisions based on insufficient information if these tools are not checked or improved.

There are several platforms, analytics systems, and automation tools that make up the digital advertising world today. Search advertisements, display campaigns, social media promotions, and programmatic advertising all provide the performance data you need to interpret correctly. A strong MarTech infrastructure makes sure that this data is collected, analyzed, and turned into useful information. It is dangerous to try to grow advertising without this base.

Campaign structures that don’t work well can hurt performance.

One of the biggest dangers of scaling advertising without proper research is running campaigns that aren’t well set up. Even with higher costs, campaigns that aren’t well-organized, lack clear segmentation, or use poor bidding tactics often struggle to perform well.

Marketers can identify structural problems in advertising accounts using a well-configured MarTech environment. Businesses can identify problems that may be limiting performance by reviewing campaign hierarchies, keyword groups, and audience targeting models. Without this research, spending more on ads can just send more people through a system that isn’t working well.

For instance, campaigns that use keywords or audience segments that aren’t related to each other could show ads that aren’t relevant to potential customers. A MarTech audit can identify these problems and suggest changes to make targeting more accurate and campaigns more effective before they grow.

  • Incorrect Conversion Tracking Leads to Misleading Data

Accurate information is the most important part of effective digital marketing. Businesses can use conversion monitoring to identify which efforts drive leads, purchases, or other useful actions. But if tracking systems aren’t set up well, marketing teams could make decisions based on false information.

A well-implemented MarTech stack ensures that conversion events are tracked correctly across all advertising platforms and analytics tools. Without this validation, companies can spend more on efforts that are working but don’t actually get them very far.

For example, tracking inaccuracies may cause conversions to be counted twice or not linked to the correct marketing channels. A full assessment of MarTech helps identify these problems and ensures that reporting tools provide accurate performance information.

  • Poor Targeting and Audience Segmentation

To be effective, advertising needs to reach the proper people. Marketers may now target customers based on their demographics, interests, behaviors, and browsing habits thanks to modern digital platforms. But if your targeting tactics aren’t clear, your ads might not get to the right people.

A structured MarTech analysis examines how audience segmentation is configured across different marketing platforms. Businesses can improve their targeting by analyzing customer data and behavioral insights to identify high-value audiences.

If you don’t do this analysis, raising your advertising budget means that your ads reach people who are unlikely to buy. A well-optimized MarTech environment ensures that advertising dollars go to the people most likely to buy and engage.

  • Budget Allocation Across Underperforming Channels

Another big danger of scaling advertising without study is how funding is distributed. Many companies put money into more than one marketing channel at the same time. These channels include search engines, social media sites, display networks, and marketplaces.

Marketing teams may struggle to determine which channels generate the most revenue without help from MarTech analytics solutions. Because of this, budgets may stay with platforms that aren’t performing well, while channels that are profitable don’t receive enough funding.

A thorough analysis of MarTech lets businesses assess how well their channels are performing by analyzing data from all of them. Businesses can better plan their budgets before expanding their campaigns by reviewing KPIs such as cost per acquisition, return on ad spend, and customer lifetime value.

  • Scaling Inefficiencies Instead of Results

Increasing ad spending can worsen existing problems if underlying issues are not fixed. Campaign structures are still not perfect, tracking technologies still produce inaccurate data, and targeting methods still reach the wrong people.

In some cases, increasing funds doesn’t help performance. Instead, it raises costs without giving you much, if any, return on your investment. Because of this, many marketing directors now see MarTech audits as an important step before spending more on advertising.

Organizations can identify and fix performance issues before scaling campaigns by examining the entire marketing technology ecosystem. This method ensures that extra spending yields real results rather than wasting money.

What a MarTech Audit Is and Why You Should Do It

An audit of MarTech examines the technologies, platforms, and systems that support digital marketing. As companies use more and more new technologies for advertising management, analytics, automation, and consumer engagement, their marketing technology stacks are becoming increasingly complex.

If you don’t check these systems regularly, they can break down or stop working as well. A MarTech audit is a disciplined way to look at the whole marketing technology ecosystem and make sure that each part is helping the campaign do its job well.

  • Evaluating Marketing Platforms and Tools

One of the most important parts of a MarTech audit is examining the platforms used to run marketing campaigns. These could be systems for managing ads, tools for analyzing data, platforms for storing customer data, email marketing software, and solutions for automating campaigns.

Companies can determine whether their MarTech infrastructure supports their current marketing goals by examining how these technologies are configured and connected. Sometimes, audits reveal tools that aren’t needed, outdated platforms, or underused features, all of which slow operations.

Identifying these problems helps companies simplify their technology stacks and ensure that each platform adds real value to marketing operations.

  • Assessing Data Accuracy and Tracking Systems

Data integrity is another important part of a MarTech audit. Marketing strategies depend on precise performance data to inform decisions. If analytics systems or monitoring technologies are set up incorrectly, the information they provide may not be accurate.

A MarTech review ensures that conversion tracking, attribution models, and analytics connectors function properly across all marketing channels. Making sure that data is correct helps marketing teams decide how to spend their money, who to target, and how to improve their campaigns.

  • Identifying Optimization Opportunities

A MarTech audit not only finds problems, but it also shows ways to make things better. Organizations can find ways to improve performance by looking at the structures of their campaigns, the workflows for automation, and the ways they group customers.

For instance, companies might find ways to automate operations that they do again and over, make their personalization tactics better, or add more data sources to their MarTech systems. These changes can make marketing much more efficient and effective.

  • Preparing Marketing Systems for Scalable Growth

The main purpose of a MarTech audit is to get marketing operations ready for long-term growth. Instead of making assumptions about how their technological infrastructure affects performance, businesses can now see how it really does.

With this information, companies can improve their MarTech environment, fix problems, and ensure their marketing systems are ready to handle larger ad budgets. Companies that take this proactive approach are much more likely to get steady returns as they grow their digital marketing activities.

Key Areas Examined in MarTech Advertising Audits

Digital advertising campaigns work in complicated systems of platforms, analytics tools, and automation technology. As businesses rely more and more on marketing technology to run and improve their campaigns, MarTech has become a key part of digital advertising success. A MarTech advertising audit looks at the tools, data systems, and campaign techniques that help ads work in a systematic way.

A MarTech audit looks at the deeper technology and strategic roots of digital marketing operations instead of just the surface-level campaign KPIs. Companies may find problems, make sure their data is correct, and make sure their advertising spending is in line with measurable business results by looking at these systems.

A MarTech advertising audit usually looks at a few important areas. These aspects include tracking conversions, setting up campaigns, choosing the right audience, measuring creative performance, and managing the budget. These parts work together to give a full picture of how marketing technology and advertising tactics work together to generate results.

Conversion Tracking and Data Accuracy

Tracking conversions and making sure the data is correct are two very important parts of good digital advertising. They make sure that marketing teams can precisely track what customers do as a result of campaigns, such as buying something, signing up, or downloading anything. Businesses can make smart choices when they have reliable tracking solutions that give them consistent and accurate performance data across all of their marketing platforms.

  • Verifying That Tracking Systems Measure Conversions Properly

One of the most important parts of digital advertising is tracking conversions. It lets companies track client behaviors that come from advertising efforts, such as purchases, sign-ups, downloads, or form submissions. Checking that these tracking systems are set up correctly is a big part of any MarTech advertising audit.

Marketing companies use conversion data a lot to figure out how well a campaign is doing and how to best spend their money. Organizations may get wrong performance reports if tracking systems are not set up appropriately. This might lead to bad decisions. A MarTech audit checks tracking codes, tag management systems, analytics connectors, and attribution models to make sure that conversions are being recorded correctly.

Another crucial part of this process is making sure that the events being tracked are in line with the goals of the organization. For instance, if a firm values completed sales more than email sign-ups, the MarTech infrastructure should put more emphasis on tracking and evaluating those higher-value conversions.

  • Ensuring Data Consistency Across Marketing Platforms

Search engines, social media networks, and advertising exchanges are all part of modern digital marketing. You need to combine the performance data from all of these platforms into one analytics environment. A MarTech audit makes sure that the data you get from various sources is accurate and consistent.

There may be differences between the data supplied by the platform and the data from internal analytics tools in some circumstances. These inconsistencies could be caused by mistakes in tracking, wrong attribution models, or systems that don’t work together completely. Businesses may find these problems by looking at the whole MarTech ecosystem. This makes sure that their data infrastructure gives them a clear and accurate picture of how well their ads are doing.

It is important to have accurate data to scale campaigns properly. Marketers might put more money into initiatives that don’t work if they don’t get accurate insights.

Campaign Structure and Targeting

The structure and targeting of a campaign decide how well ads reach the correct people. A well-planned campaign structure helps advertisers stay within their budgets, try out different audience segments, and improve performance across all platforms. By going over these things, you can make sure that your advertising efforts are in line with your strategic goals and that you aren’t wasting money on targets that are too similar or not well defined.

  • Evaluating Campaign Architecture and Organization

Checking the structure of a campaign is another important part of a MarTech advertising audit. Campaign architecture is the way that advertising accounts are set up, including the structure of campaigns, ad groups, and targeting settings.

Marketers may better manage their costs, try out different target segments, and improve performance at a very small level with well-structured campaigns. A MarTech audit checks to see if the way campaigns are set up makes it easy to target the right people and keep track of spending.

When campaigns aren’t well-organized, they can reach the same people, run the same commercials, and waste money. For instance, if several campaigns try to reach the same group of people with similar messages, they can bid against each other in advertising auctions, which would raise expenses for no reason. A MarTech audit of campaign architecture can assist in finding and fix these problems.

  • Identifying Redundant Campaigns and Ad Groups

When new techniques are added to huge advertising accounts, campaigns tend to build up over time. If you don’t review these efforts from time to time, they can become useless or not fit with your current marketing goals.

A MarTech audit checks to see if current campaigns and ad groups are still useful and if they help the company reach its overall performance goals. Extra campaigns can waste ad spending and make it harder to figure out how well they are working. Organizations may make their advertising accounts more efficient and clear by finding and getting rid of these problems.

Marketing Technology News: MarTech Interview With Fredrik Skantze, CEO and Co-founder of Funnel

Keyword and Audience Strategy

Keywords and audience strategy are quite important for figuring out how relevant ads are to people who might want to buy something. Businesses may make sure their ads reach the people who are most likely to click on them by choosing the proper keywords and clearly defining their target demographic. A well-thought-out plan makes a campaign work better and cuts down on money wasted on ads.

  • Reviewing Search Queries and Targeting Logic

Keyword targeting and dividing your audience into groups are very important for digital advertising to work. These tactics decide who sees ads and how relevant those ads are to their needs and interests. Marketers meticulously look over keyword lists, search queries, and audience targeting settings during a MarTech advertising audit.

This research helps figure out if campaigns are going after the right search phrases and groupings of people. Sometimes, businesses find that their advertisements are showing up for searches that aren’t relevant, which means they’re wasting money on ads. A MarTech audit looks at these tendencies and suggests changes that will make targeting more accurate.

Targeting the right audience is just as crucial, especially for display and social media ads. Marketers may now use modern MarTech systems to construct complex audience segmentation based on things like demographics, interests, behavior, and past encounters with a brand. By looking at these audience methods, you can make sure that your campaigns target those who are most likely to interact with the ad.

  • Identifying Opportunities to Reduce Wasted Spend

Another reason to look over your keyword and audience tactics is to cut down on wasted ad spending. When targeting is not done well, you may acquire a lot of impressions or clicks that don’t lead to real business results.

A MarTech audit can help businesses find keywords that aren’t working well, audience segments that aren’t appropriate, or targeting settings that are too broad. Changing these things makes campaigns more effective by focusing ad spending on high-value viewers and search queries that are relevant.

Ad Creative and Messaging Performance

The effectiveness of ads in getting people to pay attention and take action depends on how well the ad’s creativity and language work. Headlines, images, and calls to action are all very important for getting people to interact with your site and make a purchase. Marketers may figure out which messages work best for their target demographic and get better campaign outcomes by looking at these things.

  • Analyzing Engagement Metrics

Targeting and bidding tactics are not the only things that impact how well an ad campaign works. Headlines, pictures, calls to action, and the overall message are all important creative parts that help get people’s attention and keep them interested.

An advertising audit for MarTech looks at creative success by looking at engagement metrics including click-through rates, conversion rates, and levels of involvement. These numbers show how people react to different types of ads and creative messages.

Marketers may figure out which creative assets work best with their target consumers by looking at this data in the context of the larger MarTech framework. This information helps businesses enhance their messaging strategy and make their future campaigns more effective.

  • Determining Which Messaging Delivers the Best Results

The message of an ad must fit with both the needs of the audience and the brand’s stance. A MarTech audit checks to see if the present messaging methods are getting across the value of a product or service.

Some messages, for example, may focus on the attributes of a product, while others may focus on the benefits to the customer or special offers. Marketers can find out which message techniques work best by looking at campaign data in the MarTech ecosystem and then using those methods in other campaigns.

In competitive markets, it’s important to keep testing and improving creative assets to keep advertising working well.

Budget Allocation and Bidding Strategies

Budgeting and bidding techniques are very important for getting the most out of advertising and getting the most money back. By carefully spreading funds among campaigns, you can make sure that channels that do well have the resources they need to grow. Checking bidding tactics also helps make sure that the money spent on ads is in line with the goals of the campaign and produces outcomes that can be measured.

  • Assessing Budget Distribution Across Campaigns

To get the most out of your advertising money, you need to plan how to spend it. A MarTech audit looks at how budgets are spread out among different campaigns, channels, and groups of people.

A lot of the time, businesses find out that a big part of their advertising budget goes to initiatives that don’t work very well. Marketers can find chances to move money to campaigns that do better by looking at performance data with MarTech analytics tools. This approach makes sure that money spent on advertising goes toward the best techniques and brings in verifiable company value.

  • Evaluating Bidding Strategies and Performance Goals

Modern advertising platforms have a number of bidding tactics that are meant to improve the performance of campaigns. These methods might be about getting the most clicks, conversions, or return on ad expenditure. A MarTech advertising audit checks to see if the bidding tactics used fit with the company’s overall marketing goals.

For instance, a campaign that wants to make sales right away could need a different bidding strategy than one that wants to get people to know about the brand. When you look at these techniques through the MarTech lens, you can be sure that your campaigns are set up to reach their goals.

The audit process may also reveal chances to use more advanced bidding tactics that are backed by machine learning and automation capabilities. Adding these features to the MarTech ecosystem can make campaigns work much better.

Strengthening Advertising Performance Through MarTech Audits

The goal of looking at these important areas during a MarTech advertising audit is to build a better base for digital marketing success. Businesses may learn a lot about how their advertising systems work by looking at conversion tracking, campaign structures, targeting techniques, creative performance, and budget management.

A good MarTech audit not only finds problems that are happening right now, but it also shows how things could be better in the future. Organizations may spend more on advertising with more confidence and develop their businesses in digital markets that are becoming more competitive if they have accurate data, well-planned campaigns, and effective targeting methods.

How MarTech Audits Help Advertising Return on Investment?

Digital ads are becoming one of the most significant ways for modern businesses to flourish. To obtain more customers and make more money, businesses spend a lot of money on online ads on search engines, social media sites, and programmatic networks. But spending more on ads doesn’t always mean better results. Businesses often see their returns go down because they haven’t fixed the core problems with their campaigns. This is where MarTech audits come in. They are very important for getting the most out of your advertising and getting the most out of your money.

A MarTech audit is a planned look of the technology, campaign structures, tracking systems, and data pipelines that make digital advertising possible. The audit doesn’t just look at the obvious KPIs of a campaign; it also looks at the technological and strategic infrastructure that supports marketing operations. Businesses may make their campaigns better before spending more money on advertising by finding hidden problems and performance obstacles.

One of the main reasons MarTech audits help advertising ROI is that they find problems that are hard to find when running campaigns daily. Marketing teams could spend a lot of time tweaking keywords, changing bids, or trying out new creative assets. However, they might not see bigger structural or technological problems that are hurting the success of their campaigns. A full MarTech review gives you a better idea of how marketing systems work together and how they affect results.

A MarTech audit can also help make sure that marketing technology is in line with corporate goals, which is another major benefit. A digital marketing ecosystem is made up of many different technologies, including as analytics platforms, customer data systems, advertising dashboards, and automation software. When these technologies are not connected or are set up incorrectly, the data they create may not be complete or consistent. A good MarTech audit makes sure that these platforms operate well together and give you reliable information to help you make decisions.

Identifying Inefficiencies That Impact Campaign Performance

A lot of advertising campaigns don’t do well, but it’s not because of bad marketing concepts; it’s because of problems with the way the marketing technology stack is set up. For instance, campaigns could use old targeting settings, broken data systems, or wrong tracking codes. These problems can make ads much less successful without being obvious right away.

Companies may find these problems and fix them before they spend more money on ads by doing a thorough MarTech review. When the infrastructure that supports campaigns works better, the same amount of money spent on advertising can get better results. This is how MarTech audits help advertising expand in a way that lasts.

  • Improving Targeting Accuracy

One of the most important things that affects how well an ad works is how well it targets its audience. Marketers may reach very particular groups of people on digital platforms based on things like their age, gender, where they live, what they buy, and what they like to do online. But these targeting features only work when they are backed up by correct data and well-organized groups of people.

A MarTech audit helps you figure out how marketing platforms acquire, handle, and use data about your audience. Businesses can find out if their targeting tactics are in line with how customers actually behave by looking at customer data platforms, analytics integrations, and segmentation models. When the MarTech ecosystem gives marketers solid information about their audiences, they may show ads to people who are most likely to click on them and buy something.

Better targeting not only gets more people to click on ads, but it also cuts down on wasted ad spending. Instead of sending ads to big, unqualified groups, they are sent to those who are likely to be interested in them. This leads to increased conversion rates and better use of marketing dollars.

  • Optimizing Campaign Structures

The way a campaign is set up has a big impact on how well advertising budgets are used. Campaigns that aren’t well-organized can have the same audience in more than one ad group or use keywords in a way that doesn’t work. These problems might make it hard to judge how well a campaign is doing and make it better.

A MarTech audit looks at the structure of advertising accounts to see if campaigns are set up to work best. As part of this process, you go over campaign hierarchy, audience segmentation, and targeting parameters. Businesses can acquire better insights into performance indicators and make sure that budgets are used properly across multiple advertising activities by reorganizing campaigns under the MarTech framework.

Optimized campaign structures also help marketing teams try out new ideas, look at performance trends, and grow successful campaigns without confusing advertising accounts.

  • Reducing Wasted Advertising Spend

One of the biggest problems in digital marketing is wasting money on ads. This happens when advertising are sent to the wrong people, triggered by unrelated terms, or shown in places that don’t get people to interact with them.

A thorough MarTech audit looks at campaign data from several platforms to find the sources of wasted spending. Marketers can find patterns that show when money is being wasted by looking at search queries, audience groups, and information on where ads are placed. Once these problems are fixed in the MarTech infrastructure, companies may use their advertising expenditures to pay for techniques that bring in more money.

Cutting down on wasted spending has a direct effect on the return on investment (ROI) of advertising. Organizations can generate greater outcomes by optimizing their existing campaigns through better MarTech management instead of raising costs to make up for inefficiencies.

  • Raising the number of conversions

A well-done MarTech audit should also help with conversion rate optimization. Conversions are the main goal of most advertising initiatives, whether they are trying to get people to buy something, get leads, or sign up for something. But low conversion rates are sometimes a symptom of bigger problems in marketing strategies.

For instance, bad tracking systems could report conversions incorrectly, and analytics solutions that aren’t well-connected might not be able to tell where people drop off in the customer journey. An audit of MarTech looks at these technologies to make sure that conversion tracking and attribution models show how real users behave.

Businesses may make their marketing more effective by making sure that the data is correct and targeting the right people with their campaigns. This will help users do what they want to do. These changes over time lead to increased conversion rates and better returns on money spent on advertising.

  • Creating a Strong Foundation for Scalable Growth

In the end, a MarTech audit leads to changes that make advertising campaigns more effective and scalable. Businesses may safely raise their advertising budgets when campaigns are based on precise data, well-designed frameworks, and successful targeting methods. They know that spending more money will lead to real results.

This proactive approach stops businesses from running inefficient ads that don’t work and lets them focus on long-term growth with the help of a strong MarTech ecosystem.

The Strategic Benefit of Pre-Scaling Audits

Many businesses are realizing how important it is to do MarTech audits before spending more on ads as digital marketing gets more complicated. Instead of blindly scaling campaigns, companies that use this method get a strategic edge by knowing exactly how their marketing systems work.

A MarTech audit before scaling gives you a clear plan on how to make things better. Companies can find both technical and strategic changes that need to be made before they spend more on advertising by looking at the whole marketing technology environment.

  • Getting a better look at how well marketing is doing

One of the best things about a MarTech audit is that it gives you a better idea of how well your marketing is working. Digital marketing creates a lot of data from many different platforms, which makes it hard for marketing teams to effectively understand results without the right tools and interfaces.

A well-organized MarTech infrastructure brings all of this data together into unified analytics dashboards that make it easy to see how well a campaign is doing. Businesses can find holes in their data reporting and make sure that their analytics systems give them accurate information by going through the audit process.

Better visibility lets marketing professionals see which initiatives make money, which channels give the highest return on investment, and which techniques need to be improved.

  • Enabling Data-Driven Decision Making

Making decisions based on data is a key part of modern digital marketing. This method only works, though, if the data it is based on is correct and complete. A MarTech audit makes sure that marketing teams have access to good data that helps them plan their strategies and improve their campaigns.

With accurate data insights, companies can make smart choices about how to spend their money, who to target, and how to grow their campaigns. Marketers may use MarTech analytics to help them make decisions and get the most out of their ads instead of relying on guesses or inadequate results.

  • Building Confidence Before Scaling Campaigns

It costs a lot of money to scale up advertising efforts; marketing directors need to be sure that their campaigns are ready for growth in terms of both strategy and technology. A MarTech audit gives you this confidence by checking that your marketing systems are working correctly and that your campaigns are set up to get the best results.

This preparedness lowers the chance of performance problems that come out of nowhere when budgets go up. Companies can be more sure that their advertising efforts will grow if they make sure that their MarTech infrastructure can handle more campaigns.

  • Scaling Campaigns While Minimizing Risks

One of the best things about doing a MarTech assessment before scaling campaigns is that it can help lower risks. Ad platforms work in competitive bidding situations, and campaigns that aren’t effective can quickly use up big funds without getting any results.

A thorough examination of MarTech finds any issues early on, so businesses can deal with them before spending more on advertising. This proactive strategy makes sure that initiatives are developed on a solid technological base and backed up by precise performance statistics.

When campaigns are finally scaled, the MarTech audit process helps marketing teams get better outcomes while keeping a better track of how well their ads are doing.

  • Driving Sustainable Advertising Growth

Businesses that put MarTech audits first before increasing their advertising budgets are setting themselves up for more long-term growth. Instead of reacting to short-term changes in performance, they make marketing plans based on solid data, well-organized campaigns, and technology platforms that work well together.

This strategy makes sure that advertising investments always pay off and help the business as a whole succeed. As digital marketing changes, MarTech audits will play an even bigger role in helping businesses get the most out of their advertising.

Conclusion

As digital advertising changes, companies are putting more money into marketing technology, data platforms, and advertising channels to stay ahead of the competition. But raising the budgets for ads without first knowing how well the campaigns and marketing systems are really working might squander money and make things less efficient. This is why companies that want to get the most out of their digital advertising spending need to do MarTech audits.

A MarTech audit gives you the information you need to understand how marketing technologies, analytics tools, and advertising platforms work together to help campaigns do well. Instead of only looking at surface-level data like clicks, impressions, or traffic volume, organizations learn more about how their whole marketing system works. This broad view helps marketing teams find technological problems, incorrect data, and inefficient structures that can be getting in the way of advertising success.

One of the best things about a MarTech audit is that it can find improvement opportunities that you didn’t know about. As companies get more tools, add more advertising channels, and try out alternative campaign methods, many digital marketing systems change over time. These developments can make marketing better, but they can also make the MarTech landscape more complicated. Platforms may not work well together, tracking systems may give different results, and campaign structures may become less effective. By doing a thorough MarTech audit, companies may find and fix these problems before they spend more money on advertising.

Another important result of a MarTech audit is making the marketing infrastructure stronger. For digital ads to work, they need dependable data, well-planned campaigns, accurate targeting methods, and useful automation tools. When these parts work together in a well-optimized MarTech ecosystem, marketing teams can run campaigns with more accuracy and confidence. This better infrastructure makes it easier to ensure that advertising money is spent on tactics that lead to measurable business results.

Another great thing about MarTech audits is that they help you make decisions based on data. Companies can now get a lot of performance data in the world of digital marketing. But this information is only useful if it is correct, combined, and easy to understand. A full MarTech assessment makes sure that analytics platforms, conversion tracking systems, and attribution models are all in line with the goals of the organization. When marketing directors have solid information, they can make smart choices about how to spend their money, who to target, and how to grow their campaigns.

Before expanding advertising initiatives, doing a MarTech audit also helps lower risk. Businesses often make existing problems worse when they spend more on advertising without fixing them. Campaigns that weren’t doing well before may use up more money without getting improved outcomes. First, companies make sure their MarTech infrastructure is ready to support expansion by reviewing and improving existing marketing processes.

In the end, businesses that put a lot of effort into MarTech audits have a strategic edge in the digital marketplace. By finding problems early and making specific adjustments, they build a stronger base for marketing operations that can grow. This proactive approach lets firms get the most out of their advertising money while keeping a better track of how well their campaigns are doing.

Companies that check their marketing systems before putting additional money into digital ads are much more likely to see long-term growth. When marketing teams have a well-optimized MarTech ecosystem, they can confidently grow their campaigns, get more people to interact with them, and get better long-term returns on their digital marketing investments.

Reply announces a partnership with Mistral AI to develop sovereign and enterprise-grade artificial intelligence solutions

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Reply announces a partnership with Mistral AI to develop sovereign and enterprise-grade artificial intelligence solutions

Reply announced a new partnership agreement with AI leader Mistral AI aimed at accelerating the adoption of local, customizable, secure and enterprise-grade generative AI solutions at scale.

At the core of the collaboration is a shared vision of frontier AI, designed to enable organizations to adopt AI solutions while ensuring data control, protection of sensitive information, compliance with regulatory requirements and deployment on European infrastructures.

By combining Mistral AI’s high-performance AI models with Reply’s expertise in designing and customizing Large Language Models using proprietary and domain-specific data, organizations in highly regulated sectors – such as public administration, defense, financial services, healthcare, telecommunications, and energy & utilities – can deploy tailored AI solutions that integrate seamlessly with existing systems. These solutions support the transformation of operational processes, enhance decision-making and deliver measurable business value, while ensuring the highest standards of security, data sovereignty and compliance.

Marketing Technology News: MarTech Interview with Miguel Lopes, CPO @ TrafficGuard

A second core pillar of the Reply–Mistral AI collaboration is advanced AI model customization. Reply will become a Mistral Forge global launch partner, enabling its teams to design and train Large Language Models on proprietary and specialized datasets tailored to complex, data-intensive domains and ready for operational use.

Under this agreement, both companies are collaborating with the Austrian Academy of Sciences, focusing on the creation of a customized Large Language Model for the Greek language, spanning ancient, medieval, and modern texts. The model is designed to support researchers working with ancient Greek sources by providing advanced text search and text completion capabilities. It is trained on a highly specialized corpus that includes published ancient Greek literature, digitized inscriptions and papyri from multiple collections and selected modern Greek texts, curated from publicly available and scholarly sources. Within this initiative, Reply and Mistral AI collaborate on the training and evaluation of the model to ensure accuracy, reliability and practical relevance, demonstrating how sovereign AI infrastructures and advanced model customization capabilities can be effectively combined even in highly specialized and data-intensive domains.

“The integration of the Mistral AI ecosystem with Reply’s experience in developing AI solutions tailored to specific business processes will enable organizations to deploy custom, secure and governable models, designed to ensure data sovereignty and data protection, that integrate seamlessly into existing operational workflows and scale reliably within enterprise architectures.” said Filippo Rizzante, CTO of Reply.

Marketing Technology News: Disrupt or Be Disrupted: The AI Wake-Up Call for B2B Marketers

Mistral AI’s Chief of Revenue Marjorie Janievicz said: “We are proud to partner with Reply. Together, we will help organizations deploy AI that meets their needs for performance, control, and customization.”

Through this collaboration, Reply and Mistral AI provide a trusted and secure environment on European infrastructures, accelerating the adoption of advanced AI solutions while enabling organizations with stringent regulatory, privacy and data protection requirements to fully leverage generative AI.

Write in to psen@itechseries.com to learn more about our exclusive editorial packages and programs.

AutoRaptor joins NIADA as an official Member Benefit Partner

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AutoRaptor joins NIADA as an official Member Benefit Partner

Partnership gives NIADA members exclusive access to AutoRaptor’s AI-powered CRM platform with 35% discount

AutoRaptor, the AI-first automotive CRM platform built exclusively for independent dealerships , is joining NIADA’s member benefit program, offering members up to 35% off their monthly subscription, making enterprise-grade CRM and AI sales tools more accessible to independent dealers.

Independent dealers face unique challenges that off-the-shelf CRM tools weren’t designed to solve: limited staff, high-velocity used car inventory, and buyers who expect a fast, personalized experience. AutoRaptor was built from the ground up to address exactly these needs, combining lead management, automated follow-up, and AI-powered capabilities, like AutoRaptor’s AI Sales Assistant, into a single platform that helps smaller dealerships compete and win.

Marketing Technology News: MarTech Interview with Nicholas Kontopoulous, Vice President of Marketing, Asia Pacific & Japan @ Twilio

“We’ve always believed that independent dealers deserve the same quality technology as the big franchise groups without the enterprise price tag. Partnering with NIADA is a natural extension of that mission,” said Jami Ribeiro, AutoRaptor Chief of Staff.
“This deal gives thousands of independent dealers a meaningful, cost-effective path to modernizing how they manage leads and close deals.”

NIADA represents more than 13,000 independent automobile dealers nationwide. The organization’s member benefit program connects dealers with vetted vendors offering exclusive pricing across technology, operations and business services.

“Along with the state associations, we are committed to creating more value for members and enhancing our member benefit program,” said NIADA CEO Jeff Martin. “This new partnership continues that commitment to our members.”

Marketing Technology News: The ‘Demand Gen’ Delusion (And What To Do About It)

Through this partnership, NIADA members gain exclusive access to AutoRaptor’s full platform, including:

  • AI Sales Assistant (AISA) — automated lead follow-up across voice, SMS, email, and web chat agents.
  • Integrated Desking & Payment Penciling — build and present deal structures directly within the CRM with e-signature
  • Universal communications experience — phone, email, web, and third-party listing integrations in one place
  • Real-time pipeline visibility and automation— know where every deal stands and automate each next step
  • Seamless DMS integrations — including Dealertrack and other leading dealer management systems

Write in to psen@itechseries.com to learn more about our exclusive editorial packages and programs.