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Atento Drives the Creation of New Roles in Generative AI to Transform the CX Sector

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Atento Drives the Creation of New Roles in Generative AI to Transform the CX Sector

  • The company accelerates industry innovation by hiring specialists in conversational AI, including prompt engineers and conversational designers, reinforcing its commitment to people‑centered technology

Atento, one of the world’s largest providers of customer relationship management and business transformation outsourcing (CRM/BTO) services and an industry leader, is taking a leading role in transforming the customer experience (CX) sector by creating new professional roles centered on generative artificial intelligence. These roles combine creativity, technology, and human expertise to deliver more innovative and personalized solutions for clients worldwide.

This initiative is part of Atento’s global BTO strategy, which focuses on integrating AI and automation into customer service processes while enhancing human capabilities through continuous training and technological innovation.

New positions, including prompt engineers, conversational designers, and AI analysts, are now embedded in Atento’s operations, demonstrating the company’s commitment to humanizing technology and delivering seamless, personalized customer experiences.

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“Technology will not replace people; it will empower them to become their best selves. At Atento, we are building increasingly skilled teams to support our clients in this transformation process, promoting talent development and creating new career opportunities,” explains Thiago Zanon, Global HR Director at Atento.

Atento’s strategy is aligned with the latest Gartner study, by 2027 half of the organizations that originally planned significant reductions in their customer service workforce will reverse course, choosing instead to retain human agents as a critical part of the customer experience.

To date, more than 40 new positions related to generative AI and automation with continued global expansion underway. The company has also launched more than 15 training programs focused on essential skills including the use of ChatGPT and other AI tools, innovation methodologies, and prompt creation, ensuring employees are fully prepared for the emerging challenges of generative AI.

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Among the professionals shaping this evolution are Vanessa Marquiafável Serrani, who holds a Ph.D. in Linguistic Studies and is now a prompt engineer at Atento, and Natália Favrin Keri, a conversational designer. Both represent a new generation of talent dedicated to developing innovative AI-driven solutions aligned with clients’ business goals.

“Working with conversational AI isn’t just about programming responses. It’s about understanding how people think and communicate, then translating that into machine language,” said Vanessa Marquiafável, Prompt Engineer at Atento.

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Zilliz Cloud Launches Customer-Managed Encryption Keys for Enterprise Data Sovereignty

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Zilliz Cloud Launches Customer-Managed Encryption Keys for Enterprise Data Sovereignty

New CMEK capability gives regulated enterprises full control over encryption keys for AI-scale vector workloads

Zilliz, the company behind Milvus, the world’s most widely adopted open-source vector database, announced the general availability of Customer-Managed Encryption Keys (CMEK) on Zilliz Cloud. The new capability allows enterprises to retain full ownership of their encryption keys, delivering true data sovereignty for AI workloads in regulated industries.

As enterprises embed AI into mission-critical workflows, the sensitivity of the underlying data—customer records, medical images, financial transactions—demands security controls that go beyond standard encryption at rest. Regulatory frameworks such as GDPR, HIPAA, PCI-DSS, and SOC 2 increasingly require organizations to demonstrate exclusive control over their encryption keys, not just the data they protect. For vector database deployments—where embeddings are derived from highly sensitive assets—this requirement is especially acute.

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“Security teams in regulated industries don’t just want encryption—they want proof that no one else, including their database vendor, can access their data. CMEK gives enterprises the strongest form of data sovereignty available in a managed service, removing one of the last barriers to deploying AI at scale in healthcare, financial services, and government,” said Charles Xie, Founder and CEO at Zilliz.

Why CMEK Matters for Enterprise AI

CMEK on Zilliz Cloud separates key ownership from data processing, ensuring that Zilliz never possesses or accesses customer encryption keys. Key benefits include:

  • True Segregation of Duties: Zilliz processes data while the customer retains exclusive control over encryption keys, creating the clean separation auditors and compliance teams require.
  • Instant Revocability: Disabling a key in AWS KMS immediately renders all associated cluster data cryptographically inaccessible—no vendor coordination needed.
  • Unified Audit Trails: Every key access event is logged in AWS CloudTrail, integrating directly with existing enterprise security monitoring infrastructure.

Setup takes minutes through the Zilliz Cloud console, with auto-generated IAM policies and support for zero-downtime key rotation.

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The Laboratory vs. Factory Model: Restructuring Marketing for the AI Age

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MTS Staff Writer
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The Laboratory vs. Factory Model: Restructuring Marketing for the AI Age

You probably organize your marketing department by channel. You have a social team, an email team, and a brand team. This old way is broken because AI moves way faster than your weekly status meetings. The old setup creates roadblocks that slow you down and kill new ideas.

To survive, you have to rethink how you build your teams. The best companies are switching to the “Laboratory vs. Factory” plan. This is one of the biggest changes in marketing operating models we have seen in years. It splits your people into two groups: one for inventing things and one for making them big.

What Is the Difference Between the Lab and the Factory?

We need to be clear about what these two places actually do for you. The “Laboratory” is a safe zone just for trying new things. In the Lab, it is okay to fail. In fact, you want to fail fast so you learn.

The “Factory” is the opposite. It is built to be perfect and fast. Once an idea works in the Lab, it moves here. Good marketing operating models need this engine to send messages to millions of people. The Factory takes a winning idea and uses software to copy it perfectly for every single customer.

What Actually Happens Inside the Lab?

This space lets your creative people test wild ideas without hurting your brand or wasting a ton of money.

  • Fake Customer Tests:

Your team uses AI bots to see how customers might react to ads before a real person ever sees them.

  • Fast Sketches:

Designers use AI tools to make fifty different image ideas in an hour instead of taking weeks.

  • Testing Words:

Writers try out totally new ways of speaking on small groups to see if people click more or less.

  • Finding New Apps:

The team plays with new things like VR or new social apps to get there before your rivals do.

How Does the Factory Build Content at Scale?

Once the Lab finds a winner, the Factory takes over. This isn’t about people working harder; it is about smart automation. You build a content machine that never sleeps.

The Factory takes the winning templates and hooks them up to your data. AI tools instantly make thousands of different emails and ads. This makes sure every customer sees something that fits them perfectly. Modern marketing operating models need this speed to keep up with what buyers expect today.

Can You Move Success From Lab to Factory Easily?

Moving an idea from a small test to global production often causes friction. You need strict governance rules.

  • Clear KPIs:

Establish the exact metrics a pilot must hit before it qualifies for the resources of the Factory.

  • Standardized Handoffs:

Create automated workflows that package Lab assets correctly so the Factory team can use them immediately.

  • Tech Compatibility:

Ensure the experimental tools used in the Lab can integrate or export easily to your main production systems.

  • Regular Audits:

Review Factory outputs frequently to ensure the automated scale does not degrade the original quality of the idea.

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Who Are the New Leaders in This Dual Structure?

You need different types of talent to run these two engines. A person who loves structure will hate the Lab.

  • The Experimentation Lead:

This person acts like a scientist, designing hypothesis-driven tests and getting excited about data that disproves their assumptions.

  • The Scale Architect:

This role focuses on process optimization, ensuring that the content supply chain never breaks under heavy load.

  • The Bridge Manager:

You need a translator who sits between the two groups to facilitate communication and resource transfer effectively.

Agile marketing operating models rely heavily on getting the right personality types into the right seats.

How Should You Budget for Risk and Reliability?

Money flows differently in this new world. You cannot budget everything annually. Financial planning in these new marketing operating models looks more like venture capital allocation.

  • Risk Capital:

Allocate a specific percentage of the budget to the Lab knowing that fifty percent of the projects might fail.

  • Scale Funding:

Reserve the bulk of your cash for the Factory to ensure your proven revenue-generating campaigns never run out of fuel.

  • Dynamic Reallocation:

Review budgets quarterly to shift funds quickly from failed experiments to the initiatives that are showing promise.

  • Tooling Investments:

Spend heavily on automation software for the Factory while keeping Lab tools cheap and flexible for testing.

Does Your Tech Stack Support Two Different Speeds?

Your technology choices must reflect the duality of these evolving marketing operating models. You cannot force the Lab to use rigid enterprise tools.

The Laboratory needs sandbox environments. These are isolated spaces where data is safe, but rules are loose. Teams can plug in new AI agents or try beta software without security risks. Meanwhile, the Factory runs on a stable, locked-down stack. It prioritizes uptime and security above all else. By separating these tech environments, you protect your core business while still allowing your team to play with the future.

Structuring for Speed and Stability

You are facing a market that demands both novelty and consistency. The old ways cannot deliver both at the same time. By adopting the Laboratory and Factory approach, you solve this paradox. You create a protected space for genius ideas and a powerful engine for reliable growth. This is the only way to build a marketing machine that survives the age of AI.

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Claude, ChatGPT, Cursor, and Other AI Agents Can Now Take Direct Action on WordPress.com Sites Through Natural Conversation

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Claude, ChatGPT, Cursor, and Other AI Agents Can Now Take Direct Action on WordPress.com Sites Through Natural Conversation

WordPress.com, Automattic’s hosted website platform built on the open source WordPress software, announced the launch of new write capabilities for its Model Context Protocol (MCP) server. The update enables AI agents — including Claude, ChatGPT, and Cursor — to create, edit, and manage content on WordPress.com sites directly through natural conversation, on behalf of users.

The launch puts AI agents to work on a platform where 70 million new posts are published every month, making WordPress.com one of the largest and most active content platforms on the web, and a natural fit for agentic site management.

“WordPress.com is where millions of people build and manage their sites every day, and more and more of them are using AI tools like Claude and even OpenClaw to get work done,” said Ronnie Burt, AI Product Lead, WordPress.com. “Now those tools can actually take action — draft a post, build a page, manage comments — directly on your site, through conversation. You stay in control the whole time.”

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Building on a Year of AI Innovation

Today’s announcement builds on a series of AI features introduced on WordPress.com over the past year. In April 2025, WordPress.com introduced its AI-powered website builder that lets users generate a fully designed, content-ready website from a prompt, bringing anyone from idea to live site in minutes. Earlier this year, WordPress.com launched the WordPress AI Assistant, an in-editor tool embedded directly in the editor and Media Library that helps users draft, edit, and refine content as they work, without leaving the WordPress.com environment.

In October 2025, WordPress.com extended that momentum to the agentic web with the launch of its MCP server, giving AI agents like Claude, ChatGPT, and Cursor their first window into WordPress.com site content, analytics, and settings. WordPress.com users can now surface site insights and reduce time spent navigating the dashboard.

With today’s addition of write capabilities, WordPress.com has extended the agentic web. AI agents can now actively build and manage websites. Where the original MCP server let AI agents read, the new write and content authoring capabilities let them act: drafting posts, editing pages, and managing content on behalf of users, all with explicit user confirmation at every step.

What Users Can Do

With WordPress.com’s new MCP write capabilities, users can instruct their AI agent to:

  • Draft and publish blog posts and pages
  • Edit and update existing content
  • Create new pages and manage site content
  • And more — all through natural conversation, without touching the WordPress.com dashboard

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Built With Safety in Mind

WordPress.com designed the feature with clear safeguards to ensure users remain in control at all times. Updates require explicit user confirmation before any action is taken, and changes to already-published content are clearly flagged as going live immediately. The MCP server is opt-in only, with nothing enabled by default. Users can choose which capabilities are active, site by site. This commitment to user safety is core to how Automattic builds across its platform.

What Is MCP?

The Model Context Protocol (MCP) is an open standard that allows AI agents to connect to external tools and services in a structured, reliable way. WordPress.com’s MCP server provides MCP-compatible AI agents with a direct connection to WordPress.com sites, enabling them to read content, write drafts, make edits, and manage pages through a standardized interface, secured with OAuth 2.1.

WordPress.com is built on WordPress, the open source software that powers more than 40% of the web. That means AI agents using WordPress.com’s MCP capabilities are building on the most widely-used publishing platform on the web.

A Platform Built to Scale

WordPress.com is part of Automattic, whose ecosystem serves hundreds of billions of page views a year. WordPress.com users publish 70 million new posts every month, 1,600 new blog posts every minute, or 26 per second. That scale makes WordPress.com one of the most important destinations for AI-powered content management on the web.

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Circles and Huawei Sign Strategic Collaboration to Advance AI-Native Digital Telecom Solutions Globally

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Circles and Huawei Sign Strategic Collaboration to Advance AI-Native Digital Telecom Solutions Globally

Circles, a global digital telecom software company, and Huawei, a leading global provider of information and communications technology (ICT) infrastructure and smart devices, have signed a strategic collaboration agreement to explore the joint delivery of AI-native, next-generation digital telecom solutions for operators worldwide.

The collaboration aims to combine Huawei’s robust network and cloud capabilities with Circles’ digital BSS vertical SaaS platform to enable telecom operators to accelerate digital transformation, unlock real-time monetization opportunities, and deploy intelligent, AI-driven services at scale.

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Setting the Foundation for AI-Native Telecom Innovation

Under the agreement, the parties will explore strategic integration across key telecom domains including charging, policy control, cloud infrastructure, and intelligent automation.

As part of this collaboration, Circles and Huawei will assess potential integration between Huawei’s policy and charging capabilities and Circles’ digital BSS SaaS platform. This includes enabling:

  • Real-time monetization through advanced charging and policy orchestration
  • AI-driven policy optimization to dynamically manage network resources and service quality
  • Intelligent customer lifecycle management powered by data-driven automation and personalization

By combining network intelligence with digital customer engagement and monetization capabilities, the integrated solution aims to help operators launch innovative services faster, optimize revenue streams, and enhance customer experiences in increasingly competitive markets.

“Telecom operators are at an inflection point where AI is no longer optional – it is foundational,” said Sanjay Kaul, Chief Revenue Officer at Circles. “Together with Huawei, we’re combining network expertise and our AI-native digital BSS to help operators accelerate monetization and deploy intelligent services at scale.”

Alex Kang, Huawei Cloud Ecosystem President, expressed appreciation for Circles’ decision to work with Huawei and highlighted Huawei Cloud’s extensive experience in the telecom industry. “Huawei Cloud has been deeply engaged in supporting telecom operators’ digital transformation worldwide. We look forward to working with Circles to develop joint solutions and bring Circles’ products onto the Huawei Cloud Marketplace. Through joint marketing and market expansion, we aim to create greater value for operators and achieve a true one-plus-one greater-than-two collaboration.”

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Scalable, Sovereign-Ready Deployments

The parties will also explore deploying Circles’ SaaS platform on Huawei Cloud environments. This approach is designed to support scalable, secure, and sovereign-ready AI workloads, enabling telecom operators to meet regulatory, data residency, and performance requirements across diverse markets.

Leveraging Huawei’s cloud infrastructure and Circles’ AI-native digital stack, both parties aim to work together toward an integrated end-to-end, network-to-digital architecture that supports rapid service innovation, automation at scale, and operational efficiency.

Exploring Joint Go-to-Market Opportunities

In addition to technology collaboration, Circles and Huawei may explore joint go-to-market initiatives to position an integrated network-to-digital stack solution for telecom operators globally. This includes jointly engaging operators seeking to modernize legacy systems, deploy AI-enabled capabilities, and transition toward fully digital, software-driven operating models.

This strategic collaboration reflects a shared commitment to advancing intelligent telecom infrastructure and accelerating the industry’s evolution toward AI-native operations.

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Coralogix and Skyflow Redefine Privacy-Safe Observability for the AI Era

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Coralogix and Skyflow Redefine Privacy-Safe Observability for the AI Era

Skyflow Logo

Coralogix and Skyflow are launching a strategic partnership designed to help organizations safeguard sensitive customer data within logs. This collaboration ensures robust data protection without compromising the ability to perform searches, investigations, or leverage AI-driven operations.

Enterprises can now keep sensitive customer data out of logs, dashboards, and downstream tools while preserving observability. Skyflow and Coralogix make this possible with observability that’s privacy-safe, operationally effective, and AI-native.

Logs and telemetry play a critical role in debugging, incident response, security analysis, and AI workflows. However, they often contain sensitive customer data, embedded both in structured fields and unstructured text. While many observability tools mitigate this risk through redaction, this approach comes at a cost—eliminating exposure but also stripping away context. The result? Logs become more difficult to query, correlate, and operationalize effectively.

Coralogix and Skyflow take a fundamentally different approach: protect sensitive customer data by default while preserving the usability of observability data across humans and AI systems.

“The traditional approach of redaction creates a false trade-off between safety and usefulness,” said Anshu Sharma, CEO of Skyflow. “Once sensitive data is stripped out, teams lose the ability to search effectively, investigate incidents, or let AI agents reason over what actually happened. As a Runtime AI Data Control Platform, Skyflow ensures sensitive customer data stays governed and isolated, while observability data remains fully usable.”

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Ariel Assaraf, Coralogix CEO, said: “Coralogix customers rely on observability data as a trusted system of record—supporting engineers, security teams, and the growing demands of AI-driven automation. They shouldn’t have to choose between safeguarding sensitive customer data and maintaining operational efficiency. By partnering with Skyflow, we ensure they can achieve both seamlessly.”

Why Traditional Approach Falls Short

In conventional observability pipelines, sensitive customer data is simply masked or completely removed breaking functionality:

  • Identifiers no longer match across events
  • Search and correlation degrade
  • AI tools lose critical context
  • Teams introduce risky exceptions to get work done

Instead of permanently removing sensitive values, Skyflow replaces them with consistent, privacy-preserving tokens, allowing logs to remain searchable and analyzable while the underlying data is centrally controlled, access-governed, and auditable.

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Data Residency and Sovereignty by Design

Coralogix already enables customers to deploy observability workloads in specific geographic regions to meet data residency requirements. By combining this with Skyflow’s runtime data control capabilities, organizations can continue to meet strict data sovereignty obligations—ensuring sensitive customer data is governed, isolated, and accessed only under policy, while observability data remains local, usable, and compliant across regions. This approach helps organizations operating in regulated or multi-region environments reduce cross-border data exposure while maintaining full visibility and operational effectiveness.

Built for AI-Driven Observability

The joint approach enables organizations to:

  • Keep sensitive customer data out of logs, dashboards, and downstream tools
  • Preserve search, filtering, and correlation across events
  • Enable AI agents to operate safely on telemetry, without direct access to raw sensitive data
  • Allow policy-based rehydration only for approved workflows
  • Reduce data sprawl and strengthen compliance across the observability stack

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

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MTS Staff Writer
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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.”

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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.

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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.

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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.”

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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.”

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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.

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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.

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

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

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MTS Staff Writer
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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.

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

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