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Pipedrive Accelerates Australian Growth With Sydney Data Centre and Local Expansion

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Business Wire
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Pipedrive Accelerates Australian Growth With Sydney Data Centre and Local Expansion

Reducing latency and removing data barriers as demand grows for faster, locally compliant software

Pipedrive, an easy and intelligent CRM for small and medium-sized businesses (SMBs), has launched a Sydney-based data centre, tackling the “distance tax” that has traditionally slowed down software performance for Australian users. Built on AWS (Amazon Web Services), the move cuts platform latency by up to 60% while supporting local data residency requirements. Until now, Australian customer data has been hosted in the United States, creating a gap between global SaaS infrastructure and local expectations.

Pipedrive has launched a Sydney-based data centre, tackling the “distance tax” that has traditionally slowed down software performance for Australian users.

The Sydney data centre strengthens Pipedrive’s offering for SMBs in Australia, where businesses increasingly expect faster, locally hosted CRM solutions. The move reflects a broader shift towards a more localised approach in key markets, driven by customer demand for better performance and compliance.

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

The investment comes as Pipedrive’s Australian customer base continues to grow. With more than 2.5 million SMBs, Australia represents a significant opportunity and is now one of the company’s key global markets. This marks a shift away from a one-size-fits-all global model towards a more locally relevant experience, including a new Australian business entity.

“What we’re hearing from Australian customers is that they value Pipedrive, but increasingly expect their software to feel more local,” says Joe Futty, Chief Product and Technology Officer at Pipedrive. “For years, software companies have asked customers to adapt to their systems. We think it should be the other way around. If you’re selling in Australia, your CRM should feel Australian, built for local performance and the way people actually sell.”

Tackling Australia’s “distance tax”

Previously, data had to travel thousands of kilometres across the Pacific. By storing data onshore in Sydney, Pipedrive is reducing the “distance tax”, enabling faster and more consistent access to the platform.

For Australian sales teams, this means quicker response times and a smoother experience when managing deals and customer interactions, with faster, low-latency access to the CRM platform. It also reduces reliance on cross-region infrastructure.

Local data storage removes a key barrier for SMBs working with enterprise and government clients, who often require onshore data hosting.

The Australian data centre will also support customers across the wider Asia-Pacific region, improving performance beyond Australia.

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

Strengthening its Australian presence

Australia is currently Pipedrive’s seventh-largest market and is a key region for future growth. “We are not just expanding globally; we are localising intentionally,” says Joe Futty. “That means making sure the experience feels right for Australian salespeople, not just available. With data now in Sydney and a stronger presence on the ground, Pipedrive is built to grow alongside the Australian business community.”

The Australian data centre is part of Pipedrive’s broader global infrastructure, which now spans multiple regions across the US and Europe, with further expansion planned in Canada.

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It’s Time To Stop Letting Platforms Grade Their Own Ad Fraud Homework

By
Mike Schrobo
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It’s Time To Stop Letting Platforms Grade Their Own Ad Fraud Homework

Ad fraud is big business. Not only are scammers using automation to reach new scale – contributing to a criminal enterprise that’s eight times the size of credit card fraud – but fake engagement causes knock-on effects. For advertisers, bad data sullies marketing campaigns and drains budgets. For ad platforms, despite what they say publicly, invalid clicks still contribute to the bottom line.

Meta projected that 10% of its 2024 revenue came from scams and banned goods, Reuters reported, with the social media giant internally estimating that its platforms show 15 billion scam ads a day. Meanwhile, our analysis of more than 100 million clicks found invalid click rates running about 50% higher than what Google reports. Both cases demonstrate the conflict of interest in fighting fraud.

The current model – where the platform giants act as both ad salesman and fraud policeman – is broken. In this new landscape, supercharged by increasingly autonomous bots, advertisers should no longer outsource fraud detection to the platforms that profit from it.

The ad fraud conflict of interest

At this point, fraud is more of a feature than a bug in digital advertising. Remember that most ad networks operate on a volume-driven revenue model in which every click, regardless of authenticity, contributes to the platform’s bottom line. Aggressively eliminating fraud would mean admitting their reach is smaller than marketed. These publicly traded companies face pressure to maintain traffic metrics and billable inventory. As a result, we’re more often seeing platforms catch the most obvious bots while sophisticated invalid traffic persists.

Meta’s leaked documents offer valuable insight into the inner workings. Internal memos revealed that enforcement teams track fraud but operate under strict guardrails on how to act on it. One review found the company ignored or rejected 96% of valid user reports flagging scam ads and the threshold for actually banning an advertiser required 95% certainty of fraud. Otherwise, anyone below that just got charged higher ad rates and kept running.

And actions against fraudulent advertisers were capped at 0.15% of revenue. This tells us that fraud is flagged but the bottom line matters in deciding what to do with it.

Marketing Technology News: MarTech Interview with Max Groth, CEO at Decentriq

The problem with self-reporting

A similar dynamic plays out with invalid clicks. For example, Google reports an average invalid click rate of 11.4%. Our independent analysis across 43,000 accounts, however, finds a rate of 17.8%. Adding to the issue, invalid click rates have doubled since 2010 thanks to the increased sophistication of AI-driven bots and ad fraud malware.

Since then, it’s become much harder to differentiate between human and bot engagement. This is because bad actors are using artificial intelligence and machine learning to create bots that pause on content and simulate scrolling, thereby mimicking human viewing behavior and making detection far more difficult. Additionally, they’re using malware to infect user devices, secretly drive traffic to scammer-controlled domains, and make it unclear whether ad clicks are coming from users or “ghost click farms”. In turn, ad fraud is only growing and marketers are losing about one in five dollars.

And downstream, bad data means autobidding starts chasing bot patterns and retargeting non-existent users. Google’s Smart Bidding, Performance Max, and automated campaigns start learning from signals that increasingly include traffic that isn’t real. The result is inflated cost per action (CPA), distorted return on advertising spend (ROAS), and budget being allocated to interactions that never convert. Data poisoning like this makes up look like down and down look like up.

The fact of the matter is that ad platforms have skin in the game. Right now, these corporations are essentially grading their own ad fraud homework with little incentive to accurately report the true scale and scope. It’s time for marketers to stand up for themselves and start treating every click as a security event worthy of independent verification.

How we restore accountability

We need to stop taking platform-reported analytics as gospel. Instead, the status quo demands due diligence with closer monitoring of user verification, behavioral analytics, and fraud scoring.

For example, teams can and should keep a closer eye on inflated CTRs without corresponding conversions, as well as on traffic spikes from unusual countries, to better understand actual performance. This foundation is then strengthened with independent fraud detection tools that analyze traffic patterns, device fingerprints, IP behavior, and engagement signals. These complementary solutions go a long way to creating an independent source of marketing truth.

Armed with this information, teams can better push back on the sophisticated invalid traffic they miss. And, by partnering with platforms that enable real-time metric monitoring, it’s also possible to block bad traffic before it enters the funnel and corrupts bidding, targeting, and forecasting data.

Practitioners need to go the extra mile to keep the tech giants honest. This way, we can refund more questionable clicks, show the platforms we’re watching, and better protect the integrity of campaign data.

About Fraud Blocker

Fraud Blocker, is a leading click fraud prevention software.

Marketing Technology News: The Rise Of AI Discovery Engines: Martech Strategies Must Adapt To Machine-Led Search

SalesProTracker Launches as the Sales System Built for Teams That Want to Grow—Not Be Watched, Led by Lisa C. Laird

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MTS Staff Writer
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SalesProTracker Launches as the Sales System Built for Teams That Want to Grow—Not Be Watched, Led by Lisa C. Laird

SalesProTracker — The Sales Accountability System

Lisa C. Laird, A New Orleans–based founder, is redefining sales software around execution, relationships, and accountability.

NOLA Persistence Consulting today announced the expansion of SalesProTracker, a Sales System designed to help small and mid-sized teams grow through consistent activity, real coaching, and a level of accountability most traditional CRMs fail to deliver.

This is not a CRM — it’s a Sales System.”

— Lisa C. Laird, Founder, NOLA Persistence Consulting

Built on a simple principle — people do not do what you expect, they do what you inspect — SalesProTracker was created to solve a problem founder Lisa C. Laird experienced across two decades in sales and health technology: software that tracks activity but never improves performance.

After years of working inside enterprise CRM platforms, Laird saw a recurring pattern: tools collecting massive amounts of data without helping sales professionals build relationships or close deals. “CRMs collect data. This system teaches you how to build real business relationships,” says Liard.

Marketing Technology News: MarTech Interview with Stephen Howard-Sarin, MD of Retail Media, Americas @ Criteo

SalesProTracker shifts the focus from tracking to doing by holding users accountable for the activity that actually drives revenue: Weekly Activity Scorecard — Track calls, follow-ups, networking, and customer check-ins in a clear, visible system:

* 7-Rule Follow-Up Framework — A structured outreach sequence that ensures consistent communication and proper closure
* Unlimited Campaign Access — Every user can run outreach campaigns without volume restrictions
* Admin-Controlled Communication — Maintain brand voice across the entire team
* Meeting Integration — Booked appointments flow directly into the pipeline
* Built-In Training System — Teaches new reps how to execute, not just navigate software

Unlike traditional platforms, SalesProTracker does not sell user data, train AI on private conversations, or monitor keystrokes. The company’s position is clear: the data sales team’s data belongs to the sales teams – full stop.

Marketing Technology News: From MarTech Stack to MarTech Fabric: Weaving Brand, Content, and Conversion Into One Thread

“Built by a 20-year sales professional, SalesProTracker helps you follow up, build relationships, and actually close,” says Laird. The system is designed to eliminate guesswork and replace it with structure, giving every rep a clear understanding of what needs to be done daily to grow their pipeline. Within weeks, teams move from inconsistent effort to measurable execution — where performance is visible, repeatable, and tied directly to results.

SalesProTracker has been live since December 2025 and has been used across industries, including real estate, insurance, and small-business sales. Founding members have been grandfathered into the original pricing as part of the company’s early-adopter model.

The platform is offered in three plans: Basic at $29.99 per month ($299 annually) for solo professionals and small teams, Team at $69.99 per month ($699 annually) for growing organizations, and Business at $129.99 per month ($1,299 annually) for established teams and offices. All annual plans include two months free, and a seven-day free trial is available with no credit card required.

NOLA Persistence Consulting also provides 1:1 coaching and live training built around the same accountability framework used inside SalesProTracker — combining system execution with real-world sales development. “A sales system keeps the score,” Laird explains. “Coaching teaches you how to win. You need both”.

Building on its accountability framework, NOLA Persistence Consulting is developing BuildDatSystem, a three-day execution platform designed to help users build, test, audit, and maintain their own applications. Unlike traditional courses, BuildDatSystem is structured as a forced-execution system that guides users through defining and structuring their idea, building and designing their application, testing with real users and tracking feedback, performing daily audits to ensure system performance, and verifying payment systems and user subscriptions.

The platform is designed to continue beyond launch, supporting users as they maintain, improve, and grow their systems over time. “This is not about learning how to build something. It’s about actually building it — and making sure it works,” Laird adds.

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Innovative Solutions and AWS Sign Two-Year Strategic Collaboration Agreement to Scale Agentic AI Delivery

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PRNewswire
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Innovative Solutions and AWS Sign Two-Year Strategic Collaboration Agreement to Scale Agentic AI Delivery

Agentic Service Delivery model triples engineer capacity, cuts project staffing in half, and reframes how services businesses grow with customers

Innovative Solutions, an Amazon Web Services (AWS) Premier Tier Services Partner that delivers AI and data services to growing businesses, announced that it has signed a 2-year Strategic Collaboration Agreement (SCA) with AWS around agentic service delivery. Innovative Solutions’ success with agentic delivery is aligned with AWS to drive customer outcomes rather than traditional sales metrics. The agreement represents AWS’s commitment to scaling Innovative’s agentic service delivery model across customers to accelerate AI adoption in the AWS ecosystem.

“AWS structured this SCA unlike any before it, and they did it because the combination of agentic service delivery, DarcyIQ, and our go-to-market motion changes the math on how customers get value from the cloud,” said Justin Copie, CEO of Innovative Solutions. “This isn’t an investment in Innovative Solutions. It’s AWS signaling that agentic AI is the next chapter of partner-led transformation, and that the partners who lead it will define what customer outcomes look like for the next decade.”

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

Accelerated Growth Through Agentic Delivery

The results of Innovative Solutions’ new AI-powered delivery model challenge the traditional relationship between headcount and services growth. The company leveraged DarcyIQ, its proprietary AI platform that supports revenue acceleration and project delivery for services organizations, to develop a proprietary model for Agentic Delivery, in which AI agents execute work alongside data architects and engineers across the full delivery lifecycle. As a result, the company has become one of the first AWS services partners to sign an SCA focused on agentic service delivery.

The Agentic Delivery model developed by Innovative Solutions has proven to significantly increase output, reduce staffing requirements, and accelerate project timelines without added headcount. Key results include:

  • 3X project capacity per engineer. Individual engineers now manage 10+ concurrent projects at the same quality level that previously required multiple engineers across fewer engagements
  • 50% reduction in engineering headcount per project. Projects are delivered with half the typical staffing requirements
  • 38% faster time-to-production.  Proofs of concept delivered in days, not weeks
  • 30% increased quality. Higher accuracy rate based on anticipated delivery output

“These results aren’t an efficiency story, they’re a structural one,” added Copie. “Every services business in the world is built on the same constraint: you grow as fast as you can hire. Agentic service delivery breaks that equation. We’re shipping faster, with fewer people, at higher quality without the tradeoff. It is a new operating model. The companies that figure this out first will redefine what ‘services at scale’ actually means.”

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

Seamless End-to-End Connection

Agentic Delivery embeds AI agents across the lifecycle of service-based business, covering every process stage, including prospecting, sales, solution engineering/presales, project setups, partner integrations, architecture, service delivery, and ongoing managed services.  Rather than starting each engagement from scratch, architects pair with DarcyIQ agents to consider the full project context, history of conversations, legal documents, technical documents, and more.  This enables delivery teams to move quickly and confidently with execution, minimizing wasted time on rework, handoffs, and misunderstandings. The model replaces traditional bottlenecks with automated multi-pass review cycles, allowing work to be validated continuously at every pull request in many IDEs of choice, including Cursor, Kiro, Claude Code, and Codex.

“What Innovative Solutions is doing with agentic delivery is a leading example of how far enterprise AI workloads have evolved in production,” Rob Ferguson, VP of Technology & Strategy, Fireworks AI. “They’re running 10+ concurrent AI-agent workflows per engineer, which requires consistent, high-throughput inference. Fireworks AI provides the infrastructure layer that makes that level of concurrency practical in real-world environments, helping support a 3x increase in delivery capacity per engineer while maintaining reliability across production systems.”

Using DarcyIQ, Innovative’s teams generate proposals, execute projects, and maintain ongoing client engagement within a single system. By maintaining continuity of context from initial sale through delivery and beyond, the platform reduces the inefficiencies that typically occur at handoff points.

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Middleware Launches OpsAI, an AI SRE Agent That Resolves Production Issues Before They Impact End Users

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PRNewswire
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Middleware Launches OpsAI, an AI SRE Agent That Resolves Production Issues Before They Impact End Users

Middleware, the unified observability platform for cloud-native engineering teams, announced the general availability of Middleware OpsAI, an AI-native Site Reliability Engineering (SRE) agent that detects, diagnoses, and resolves production issues across the full application stack, often before end users are ever affected.

On-call engineers spend nearly 60% of their time hunting for root causes instead of building features, juggling 10+ monitoring tools. Alert fatigue, context-switching, and Kubernetes opacity have made MTTR one of DevOps’ most stubborn metrics, and Gartner projects more than 50% of enterprises will adopt AIOps and agentic automation by 2027.

Middleware OpsAI addresses this gap with an all-in-one SRE agent built directly on Middleware’s full-stack observability platform. Unlike platform-agnostic agents that depend on third-party APIs, OpsAI has native, first-party access to APM, RUM, Logs, Infrastructure, and Kubernetes telemetry, enabling faster, more accurate investigations and end-to-end remediation.

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

OpsAI, an SRE agent built on Middleware’s full-stack observability platform, automates root cause analysis, generates pull-request-ready code fixes, and remediates Kubernetes incidents, already auto-resolving more than 80% of production issues in customer environments.

Key capabilities at launch include:

  • Automated root cause analysis across backend, frontend, and Kubernetes signals, correlating traces, logs, metrics, and frontend sessions in seconds and tracing issues to the exact line of code.
  • Pull-request generation via secure GitHub MCP integration, file-scoped reads, and zero source code retention.
  • Kubernetes Auto Fix for direct remediation of pod crashes, memory leaks, and misconfigurations — choose Auto RCA mode (OpsAI proposes the fix) or Auto Fix mode (OpsAI applies it directly).
  • Third-party alert ingestion from Datadog and Grafana, no migration required. Run agentic SRE investigations inside Middleware using existing observability data.
  • AI-powered anomaly detection and log pattern analysis across the full stack, flagging genuine deviations and surfacing recurring patterns while filtering false positives.

In internal benchmarks, OpsAI has resolved more than 80% of Middleware’s own production issues automatically, achieved a detection-to-resolution rate of over 90% in customer beta accounts, and delivered 10× faster response times than competing AI SRE agents.

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

“Observability platforms have spent the last decade getting better at telling you something is wrong. The next decade is about systems that fix it for you,” said Laduram Vishnoi, Founder and CEO of Middleware. “OpsAI lives inside your observability stack and ships actual code fixes when confident. This is what agentic observability should feel like, less firefighting, more building.”

“Middleware reduced the time we spend on debugging and resolving issues by nearly 90%,” said Nico Laqua, CEO of Corgi Insurance.

OpsAI supports almost all languages and also integrates with GitHub, Datadog, Grafana, and Kubernetes, and is available immediately under usage-based pricing. A free 14-day trial is available with no credit card required. Middleware is SOC 2 Type II, ISO 27001, HIPAA, and GDPR compliant.

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

SurfiAI Launches Single Pane of Glass Company Dashboard with 60+ API Integrations and AI-Powered KPI Analytics

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MTS Staff Writer
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SurfiAI Launches Single Pane of Glass Company Dashboard with 60+ API Integrations and AI-Powered KPI Analytics

SurfiAI unifies company KPIs across 60+ business apps into one AI-powered dashboard with automated analytics and intelligent remediation recommendations.

SurfiAI announced the launch of its new Single Pane of Glass company dashboard, a unified AI-powered analytics platform that connects to over 60 business APIs and delivers real-time KPI insights, anomaly detection, and intelligent remediation recommendations — all from a single screen.

Companies don’t suffer from a lack of data — they suffer from a lack of one place to see it. SurfiAI gives every team one AI-powered view of what’s working and what to do.”

— SurfiAI Spokesperson

Designed for operations leaders, executives, and IT teams who are tired of jumping between tools, SurfiAI consolidates data from across the modern business stack — CRM, finance, marketing, support, productivity, cloud infrastructure, HR, e-commerce, communications, and more — into one cohesive view. The platform’s AI engine continuously analyzes those KPIs, surfaces what matters, and recommends concrete next steps when something goes off track.

Marketing Technology News: MarTech Interview with Stephen Howard-Sarin, MD of Retail Media, Americas @ Criteo

KEY FEATURES:

• 60+ API Integrations — Out-of-the-box connectors for the most widely used SaaS, cloud, and business platforms.
• Single Pane of Glass — Every KPI that matters to your company, side by side in one unified dashboard.
• AI-Powered Analytics — Automatic trend detection, anomaly alerts, and natural-language KPI explanations.
• Intelligent Remediations — When a metric slips, SurfiAI suggests concrete remediation steps and the systems to act in.
• Cross-System Correlation — Connect cause and effect across tools (e.g. ad spend → pipeline → revenue → churn).
• Custom KPI Builder — Define company-specific KPIs from any combination of connected sources.
• Role-Based Views — Tailored dashboards for executives, managers, and operators.
• Privacy-First Architecture — Built on the same security-first foundation as the broader SurfiAI platform.

Marketing Technology News: From MarTech Stack to MarTech Fabric: Weaving Brand, Content, and Conversion Into One Thread

WHY IT MATTERS:

Most companies today operate across dozens of disconnected SaaS tools. Critical KPIs live in silos, dashboards proliferate, and leaders waste hours stitching together a single view of business health. SurfiAI’s Single Pane of Glass eliminates that friction — turning fragmented data into one continuously updated, AI-curated command center for the company.

A SurfiAI spokesperson said: “Companies don’t suffer from a lack of data — they suffer from a lack of one place to see it. SurfiAI gives every team a single, AI-powered view of what’s working, what isn’t, and exactly what to do about it.”

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

XstraStar Launches Full-Funnel GEO Optimization Service to Help Brands Win AI Search Visibility

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PRNewswire
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XstraStar Launches Full-Funnel GEO Optimization Service to Help Brands Win AI Search Visibility

XstraStar has launched a full-funnel Generative Engine Optimization (GEO) solution designed to help brands build measurable visibility across AI search platforms including Doubao, DeepSeek, ChatGPT, and Perplexity. As AI search adoption accelerates globally, the company positions GEO as a practical alternative to traditional SEO—focused on making brands both discoverable and recommendable throughout the AI-driven buyer journey.

Why GEO, Why Now

Search behavior is rapidly shifting toward AI platforms. By mid-2025, AI search tools had reached an estimated 685 million monthly active users worldwide, while traditional search market share continued to decline. Brands relying solely on SEO face a shrinking channel, as AI platforms increasingly serve as the primary interface for product discovery and purchase decisions.

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

A Structured Approach to AI Visibility

XstraStar’s methodology is built around “meta-semantic optimization,” transforming brand information into structured, authoritative content that AI systems are more likely to retrieve and cite. The workflow includes four stages: semantic profiling, technical content optimization, multi-channel distribution, and continuous performance monitoring.

The company also provides a proprietary analytics platform that tracks five key metrics—mention rate, average AI ranking, sentiment, competitive positioning, and recommendation likelihood—updated daily across major AI platforms. These metrics are combined into a unified scoring system, enabling brands to benchmark performance and track progress over time.

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

Early Client Results

XstraStar reports clients achieving AI mention rates rising from zero to between 50–70% within three to five months, along with multi-fold increases in organic search impressions and clicks. Clients served include tech companies expanding internationally across SaaS, productivity tools, and research applications.

XstraStar operates teams across Asia and Singapore, and states it is among the first providers to commit to full-funnel KPIs spanning AI exposure through to user registration and conversion.

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

Guidepoint Launches MCP on Claude, Embedding Trusted Expert Insights into AI-Powered Research Workflows

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PRNewswire
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Guidepoint Launches MCP on Claude, Embedding Trusted Expert Insights into AI-Powered Research Workflows

Guidepoint, a global pioneer in access to expert insight, announced the launch of its Model Context Protocol (MCP) server with Anthropic’s Claude. Research teams can now incorporate trusted knowledge from Guidepoint’s library of 100,000+ expert interview transcripts directly into their AI-enabled research workflows, with every insight attributed and linked to its source in Guidepoint360.

Research teams can now incorporate expert insights from a library of 100,000+ transcripts directly into Claude.

Guidepoint MCP is powered by its proprietary library of expert insights which is built by Guidepoint’s team of 300+ content specialists and 100% compliance reviewed to institutional standards. The continuously expanding dataset supports both deep company-level diligence and broad thematic research across industries and geographies. With 5,000+ new transcripts added every month, it represents a uniquely powerful foundation for AI-enabled research that only gets stronger over time.

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

“Our clients depend on Guidepoint for expert insights they can trust,” said Albert Sebag, Founder & CEO of Guidepoint. “Guidepoint MCP lets them combine our rigorously sourced expert library with Claude’s advanced reasoning and agentic capabilities. The result is a research workflow that is faster, more comprehensive, and more powerful than anything available before.”

What that means in practice cuts across client segments. A healthcare investor preparing for an earnings call can surface expert physician perspectives on a drug class in minutes, with every source cited and auditable. A private equity analyst conducting diligence on a company with limited public information can draw on Guidepoint’s proprietary interviews to build conviction faster than any traditional research process allows. A consulting team scoping a new market can canvass operators, former executives, and channel partners across geographies in a single search, surfacing on-the-ground realities that traditional desk research alone misses.

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

Key benefits of Guidepoint MCP include:

  • Seamless AI incorporation. Enabling new research workflows that combine proprietary expert knowledge with the full power of AI.
  • Full source attribution. Every answer traces back to the source transcript in Guidepoint360, with a complete audit trail.
  • Compliance by design. All content is compliance-reviewed and configurable to client-specific controls, including off-limits lists and topic restrictions.
  • Compounding dataset. A foundation that only gets stronger over time with 100,000+ transcripts available now and 5,000+ added every month.

Today’s launch on Claude marks only the beginning of Guidepoint’s availability on other platforms. Additional connections are planned in partnership with other leading platforms to ensure clients have access to trusted expert insights across their research process.

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

KYBER1 Launches to Expand Access to Commerce Media for Retailers and Data Owners

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prweb
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Sendbird Launches Agent Steward to Bring Full Autonomy and Ownership to AI Customer Experiences

KYBER1 Launches to Expand Access to Commerce Media for Retailers and Data Owners

KYBER1 enables retailers and data-rich organizations to transform first-party commerce signals into privacy-safe audiences that can be activated beyond owned channels. Its approach emphasizes governed access, interoperability, and closed-loop measurement tied to business outcomes, without requiring partners to build full retail media networks.

KYBER1 announced its launch with a focus on helping a broader set of retailers and data owners turn first-party commerce data into scalable media and audience opportunities.

As commerce media grows, most activation remains concentrated among a small number of large networks with the scale and infrastructure to bring their audiences to market. Many other organizations hold valuable first-party data—including customer identity, transaction behavior, and product insights—but lack a practical, measurable path to activation.

Marketing Technology News: MarTech Interview with Stephen Howard-Sarin, MD of Retail Media, Americas @ Criteo

KYBER1 was built to address that gap.

The company enables retailers and data-rich organizations to transform first-party commerce signals into privacy-safe audiences that can be activated beyond owned channels. Its approach emphasizes governed access, interoperability, and closed-loop measurement tied to business outcomes, without requiring partners to build full retail media networks.

“Commerce media does not stop with owned channels, but most activation models do,” said Phil Guay, Founder and CEO of KYBER1. “A much broader set of organizations should be able to participate in this growth. Many already have the data. What they need is a practical way to bring it to market.”

KYBER1 focuses on the “middle” of the market—organizations with strong data assets but limited access to scalable activation and buyer demand. At the same time, brands and agencies face increasing fragmentation when trying to access high-quality commerce audiences beyond major platforms.

Marketing Technology News: From MarTech Stack to MarTech Fabric: Weaving Brand, Content, and Conversion Into One Thread

The company’s commerce-first, agentic-designed model supports more coordinated and repeatable activation workflows, helping partners move from data to activation and from activation to measurable revenue.

KYBER1 is entering the market through pilot programs with select partners, focused on assessing data readiness, defining activation-ready audiences, and testing off-site use cases designed to demonstrate clear business value.

“We believe many data owners are closer than they think,” said Luc Cormier, VP Marketing and Customer Success at KYBER1. “They don’t need to rebuild their entire stack to get started. They need a credible first use case and a way to prove results.”

KYBER1 is also a founding member of AgenticAdvertising.org, supporting the development of open standards such as the Ad Context Protocol (AdCP), which aims to enable more interoperable activation and measurement across the advertising ecosystem.

The company is now opening discussions with retailers, commerce media teams, and data owners interested in exploring new audience and revenue opportunities.

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Adsterra Monetization Case Study: Google Discover Traffic Revenue

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MTS Staff Writer
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Adsterra Monetization Case Study: Google Discover Traffic Revenue

Adsterra desktop logo

Insights into building a scalable model with Google Discover traffic and optimized ad placements by Adsterra publisher.

A newly released case study explores how a digital publisher used trending news content, Google Discover traffic, and Adsterra monetization tools to generate nearly $7,000 in revenue over a short period. It outlines a practical approach to spotting emerging topics, creating timely content, and monetizing large volumes of traffic through CPM-based advertising.

This case demonstrates how Adsterra empowers publishers to turn trending traffic into reliable revenue through strong CPM, global demand, and performance data transparency”

— Gala Grigoreva, Adsterra’s CMO

The case describes Karan – an independent publisher from India who developed a scalable workflow built around trend-watching – reacting quickly to trends and prioritizing mobile audiences. He aligned content with real-time user interest which made it possible for him to capture sharp traffic spikes and turn them into steady ad revenue.

Performance Overview
The case delivered strong results looking at traffic, engagement, and monetization metrics which demonstrates the viability of trend-driven publishing with strategic execution.
– Total revenue: $6,976.06
– Impressions: 8.45 million
– Clicks: 703,948
– Average CTR: 8.32%
– Average CPM: $0.825
– Traffic source: ~95% Google Discover
– Mobile share: ~85%
– Publishing volume: 4–7 articles daily

Marketing Technology News: MarTech Interview with Stephen Howard-Sarin, MD of Retail Media, Americas @ Criteo

These results were achieved using Adsterra’s CPM-based monetization across multiple GEOs. More insights on monetization strategy for Adsterra are presented below.

Monetization Insights Across GEOs
Revenue distribution varied significantly by county, underlining the importance of balance between traffic volume and CPM efficiency. High-volume regions contributed the bulk of impressions, while Tier-1 countries delivered stronger returns per impression.

For example, India generated the largest share of total revenue due to scale ($2,114; 6.2M impressions; CPM $0.339), while markets such as the United States and Canada achieved substantially higher CPMs despite lower traffic volumes (CPM $23.30 and $19.97 accordingly). This is a key to understanding monetization dynamic: sustainable growth often depends on combining broad reach with targeted exposure to high-value audiences.

Technological Requirements
A news platform seeking visibility in Google Discover must prioritize speed, mobile performance, and technical clarity. This requires fast managed hosting with CDN support, a mobile-first design approach, and a lightweight theme to protect Core Web Vitals. Proper implementation of Open Graph tags and NewsArticle schema markup ensures content is correctly interpreted and displayed, while full HTTPS coverage safeguards trust and performance.

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In addition, it is important to avoid intrusive interstitials or pop-ups that obstruct content due to their negative impact on user experience and search visibility. Platforms such as WordPress, supported by tools like Rank Math SEO and Ad Inserter, can effectively support this requirement for seamless user interface. All in all, this helps to maintain the overall system fast, clean, and fully optimized for SEO, structured data, and ad control.

Content and SEO Insights
While a strong technical setup is essential, success in Google Discover also depends on authority, performance, and content quality. Domain authority supports faster indexing and trust, and consistent monitoring of Core Web Vitals (LCP, CLS, and INP) ensures strong mobile performance. Lightweight front-end architecture with minimal JavaScript helps avoid render delays, and content should align with timely trends while remaining genuinely engaging.

Additional trust signals, including publisher recognition in the Knowledge Graph and inclusion in Google Publisher Center, further reinforce visibility. From a graphics standpoint, high-quality featured images (minimum 1200px wide) remain critical in a visual-first environment.

Proven performance history also contributes to sustained reach. Data from Google Search Console demonstrates that strong Discover traction can compound over time, with one site achieving 10.5 million clicks, 287 million impressions, and a 3.7% average CTR over six months, including a peak exceeding 2 million daily clicks in late 2025.

Workflow Used
A consistent, fast-paced workflow is key to turning trending news into measurable results in Google Discover. Each day begins with scanning Google Trends, Google News, and social platforms to identify emerging topics, followed by selecting and prioritizing those with strong audience appeal and momentum.

Articles are produced quickly using a clear, structured format, supported by multiple headline variations and a high-quality featured image (minimum 1200px wide). Content is then published with proper technical setup and submitted through Google Search Console, with performance reviewed within hours to scale topics that gain traction.

Refinement is kept simple and intentional. Small headline updates may help revive visibility when early growth appears, but should be used sparingly. The most reliable results come from content with a strong emotional angle, such as surprise, controversy, or inspiration, which drives clicks, engagement, and sustained Discover exposure.

Traffic Behavior
Monetizing traffic from Google Discover requires understanding how different it is from traditional search. Unlike traditional search, Discover traffic arrives in sharp, unpredictable spikes, sometimes bringing hundreds of thousands of visits within a day or two, along with sharp increases in CPM, especially in high-value markets like the United States. As Discover users they tend to move quickly through content, high-volume display ads prove to be effective, particularly in combination with conversion-based models. Using ad network data, such as country performance, CTR, and real-time revenue, helps identify what works best and react quickly to trends, including “second-wave” topics that still have growing interest but less competition.

Challenges and Risk Factors
While the model proved profitable, it also presented several challenges. Traffic volatility made revenue less predictable, and reliance on a single distribution channel introduced platform dependency risks.

At the same time, maintaining quality is essential. Misleading headlines, low-value or copied content, inconsistent posting, and poor visuals can quickly reduce visibility. A consistent “promise-and-deliver” approach where headlines accurately reflect content remains crucial, as early user signals such as bounce rate directly impact visibility. Similarly, overly aggressive monetization, like intrusive ad formats, risked increasing bounce rates and negatively impacting distribution.

To mitigate these risks, the publisher focused on maintaining a balance between engagement and user experience, ensuring that monetization did not undermine long-term performance. In other words, as Discover increasingly prioritizes user experience, it is important to limit intrusive ads, focus on original and timely stories, and build authority in specific topics, while targeting audiences in higher-value regions for better overall returns.

Criteria for Choosing an Ad Network
The publisher tested several ad platforms, and chose Adsterra as the most reliable option for monetizing trending traffic even compared to Google AdSense. It offers consistent payouts without delays or hidden fees and performs well across both high-value and high-volume regions. It works with both countries like the United States and Canada that deliver strong CPM rates, and large traffic markets such as India that generate solid revenue due to scale. Its ad formats, including Social Bar and Native Banners, work well on mobile and do not disrupt the reading experience, helping maintain user engagement.

The platform also provides clear and detailed reporting, showing performance by country with key metrics like impressions, CTR, CPM, and revenue in one place. This makes it easy to understand where earnings are coming from and to adjust strategy quickly based on what performs best.

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Cannon Studio Develops Unified Platform for AI Video Creation and Workflow Management

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Cannon Studio Develops Unified Platform for AI Video Creation and Workflow Management

Cannon Studio_Icon

Platform integrates generation, editing, asset management, and distribution tools to support structured AI content production

Cannon Studio announced ongoing development of its AI-powered platform designed to support video creators, filmmakers, and digital teams working with artificial intelligence tools.

The future of AI video is not random one-off clips. It is consistent worlds, better workflows, stronger communities, and tools that help creators finish what they start”

— Founder of Cannon Studio

As AI-generated video content becomes more widely adopted, creators are increasingly using multiple tools for ideation, generation, editing, and publishing. This workflow can introduce fragmentation, requiring users to manage assets and processes across separate platforms.

Cannon Studio is developing a system intended to consolidate these stages into a single environment, allowing users to manage content creation workflows from initial concept through final output.

Marketing Technology News: MarTech Interview with Stephen Howard-Sarin, MD of Retail Media, Americas @ Criteo

Structured Approach to AI Content Creation

Cannon Studio includes a system for organizing content into reusable components.

Users can create and manage assets such as characters, locations, objects, and visual references, which can be reused across different projects. This approach is designed to help maintain consistency across scenes and outputs without requiring repeated setup.

The platform also includes a workflow framework referred to as Creator Flow, which allows users to organize projects into scenes and individual shots. Each shot can include prompts, dialogue, and reference elements, providing a structured format for generating and refining content.

Integration of Generation and Production Tools

In addition to content generation, Cannon Studio incorporates tools for editing and post-production within the same environment.

These tools include:

– Video trimming and cropping
– Format conversion and compression
– Basic audio and narration workflows
– Output preparation for publishing

The platform also provides access to multiple AI video generation models within its interface, allowing users to select different options depending on project requirements.

Development of Distribution and Asset Systems

Cannon Studio is also developing features related to content distribution and asset management.

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The platform includes Cannon TV, an internal environment where users can share video content. This includes support for longer-form content as well as shorter video formats.

In addition, the company is working on an asset marketplace where users will be able to manage, and potentially exchange, reusable creative elements such as characters, environments, and visual components within the platform.

Mobile Access and Platform Expansion

According to the company, a mobile application is currently in development. The app is expected to provide access to projects, content review, and sharing capabilities outside of desktop workflows.

The platform is designed to support creators working across different environments, including individual users, small teams, and content production groups.

Evolving AI Content Workflows

The development of platforms like Cannon Studio reflects a broader shift in how AI tools are used in content creation.

While early AI tools focused primarily on generating individual outputs, newer systems are increasingly incorporating workflow management, asset organization, and production features.

This shift is being driven by the need for more structured processes as creators move from experimentation to repeatable content production.

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Ping Identity and OLOID Bring Passwordless, Verified Trust to the Clinical Workforce

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Ping Identity and OLOID Bring Passwordless, Verified Trust to the Clinical Workforce

The cloud-delivered solution brings verified onboarding, passwordless Tap-and-Login, and secure recovery to reduce clinician friction and credential risk

Ping Identity, a leader in securing digital identities for the world’s largest enterprises, announced a partnership with OLOID to deliver a passwordless, Verified Trust identity solution for the U.S. clinical healthcare workforce. As healthcare organizations face rising credential-based attacks and increasing pressure to eliminate passwords, the joint solution modernizes clinical workforce identity and access management by replacing access friction, lost badges, and account lockouts with verified onboarding, passwordless Tap-and-Login, and high-assurance account recovery. Ping provides the identity verification and trust layer, while OLOID delivers seamless Tap-and-Login access across clinical environments.

Clinicians today face mounting time pressure compounded by the friction of logging into shared devices, workstations, and EHR systems. At the same time, healthcare organizations are battling rising cyberattacks and impersonation risks, including organized schemes involving fraudulent clinical workers. Cyberattacks are increasingly impacting patient care, with 72% of healthcare organizations reporting disruptions and 29% reporting increased mortality rates, according to a recent industry report. Designed to mitigate unsafe workarounds and introduce adaptive assurance, the solution helps healthcare organizations reduce clinician downtime, improve productivity, and strengthen protection against credential-based attacks.

The joint solution introduces a passwordless, Verified Trust model for healthcare, where identity is continuously verified through credentials and adaptive assurance rather than static passwords. This approach extends beyond login to support trusted onboarding, seamless Tap-and-Login access across multiple low-friction authentication options, and secure account recovery. Clinician-held credentials can also be presented through mobile wallet experiences, including Apple Wallet and Google Wallet, where available.

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The joint solution supports the clinical workforce lifecycle across three core use cases:

  • Verified Onboarding: Verify clinicians early, issue reusable verifiable credentials, and accelerate day-one access
  • Verified Tap-and-Login: Deliver seamless, passwordless Tap-and-Login for shared workstations, shared accounts, and VDI-hosted EHRs
  • Verified Recovery: Enable fast, high-assurance recovery for lost badges and locked accounts using adaptive verification and reusable credentials

Together, these capabilities enable healthcare organizations to deliver a consistent, seamless experience across both back-office and clinical workforce employees.

A Cloud-Native, Privacy-Preserving Approach
The 100% cloud-native SaaS solution replaces fragmented identity tools and legacy tap-and-go systems with a unified approach to verified onboarding, Tap-and-Login, and recovery, reducing reliance on on-premise infrastructure and legacy appliance overhead.

Ping Identity’s PingOne Verify is designed to give healthcare organizations control over their data, using minimal data collection and policies that support rapid deletion of Personally Identifiable Information (PII) following verification. Biometric verification is performed using a one-second passive liveness check at the edge, helping protect against deepfakes while preserving user privacy.

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“Healthcare organizations must balance speed of care with high-assurance security,” said Gaurav Sharma, VP Product Strategy, Workforce, Ping Identity. “By combining verified onboarding, seamless Tap-and-Login, and secure recovery, we’re reducing access friction while strengthening protection against credential fraud.”

“Together with Ping Identity, we’re delivering seamless Tap-and-Login as part of a broader Verified Trust approach across shared devices and EHR systems, so clinicians can access what they need quickly and get back to patient care,” said Madhu Madhusudhanan, Co-Founder & CTO, OLOID.

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DealerOn Unveils Sidekick, an AI-Powered Suite to Elevate Dealership Website Management & Performance

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DealerOn Unveils Sidekick, an AI-Powered Suite to Elevate Dealership Website Management & Performance

New intelligent tools enhance merchandising, engagement, and insights to help dealers compete in the Artificial Intelligence age

DealerOn, a leading provider of automotive dealership websites and digital sales and marketing solutions, announced the launch of DealerOn Sidekick, an AI-powered suite of tools designed to elevate dealership website management and performance.

Built as an intelligent layer, Sidekick works across a dealership’s digital presence to help dealers attract, engage, and convert today’s informed online shoppers—while streamlining day-to-day marketing operations.

Every Hero Needs a Sidekick

The DealerOn Sidekick Suite brings AI-driven intelligence to the most important parts of a dealership’s digital operation, including vehicle merchandising, customer engagement, compliance, and performance insights.

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Key capabilities include:

  • Quick Connect Chat Sidekick: Quick Connect Chat Sidekick enables shoppers to ask questions in natural language and receive instant answers about inventory, appointments, trade-in values, service recalls, and dealership information. The solution also delivers personalized prompts based on real-time shopper behavior, with responses in three seconds or less. When paired with DealerOn’s Lead Driver Quick Connect offering, dealers can connect sales teams with online shoppers as soon as a lead is submitted—often while the customer is still browsing the site. Together, the tools help improve response times, increase lead conversion, and support more vehicle sales.
  • Insights Sidekick: Turns website data into clear, actionable recommendations. Using natural language prompts, dealers can quickly surface tailored insights based on their goals, store size, and local market trends—all within DealerOn’s Data Hub reporting suite, powered by Google Analytics 360 and BigQuery.
  • Merchandising Sidekick: Works with a dealer’s existing photo process, using AI to automatically score and enhance the images they already take—improving lighting, framing, and clarity, then applying backgrounds for a cleaner, more consistent presentation.
  • Compliance Sidekick: Gives dealers a clearer way to track OEM brand guideline violations across custom and platform pages, identify issues at the page level, and make timely fixes before they become bigger problems.

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The Sidekick Suite is designed to grow alongside the dealers who rely on it, with additional tools and capabilities planned for release throughout the year.

“DealerOn has always been focused on giving our dealers a competitive advantage,” said Ali Amirrezvani, CEO and Co-Founder of DealerOn. “Sidekick takes that mission further by weaving intelligent AI into every layer of the dealership’s digital operation from the moment a shopper lands on the site to the insights a dealer needs to make smarter decisions. We’re just getting started.”

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Precisely Advances Agentic-Ready Data with a New AI Agent, Data Product Marketplace, and MCP-Enabled APIs

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Precisely Advances Agentic-Ready Data with a New AI Agent, Data Product Marketplace, and MCP-Enabled APIs

Latest Data Integrity Suite enhancements help organizations build, share, and use trusted data across AI-driven workflows and analytics

Precisely, the global leader in data integrity, announced new capabilities in its Data Integrity Suite to help organizations build, share, and use Agentic-Ready Data, the highest quality data that is integrated, governed, and enriched for AI, automation, and analytics initiatives across the enterprise. The release introduces a Data Integration Agent that joins the Gio™ AI Assistant, a data product marketplace integrated into the Data Integrity Suite through a partnership with Huwise, and expanded APIs now accessible through a Precisely-hosted MCP (Model Context Protocol) server.

Most enterprises are eager to move from AI experimentation to full implementation, yet inconsistent, non-compliant, and siloed data continues to be a bottleneck to forward progress. The latest enhancements to the Precisely Data Integrity Suite address these challenges by helping organizations build reliable data pipelines, publish trusted data products, and make contextualized data directly usable by AI systems and automated workflows.

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The latest enhancements to the Precisely Data Integrity Suite include:

  • Data Integration Agent helps teams design and configure data replication pipelines by handling setup, schema mapping, and validation tasks – reducing manual effort, improving consistency, and accelerating time-to-value. This agent joins the previously announced Gio AI Assistant and a growing collection of specialized AI agents for data quality, enrichment, and more.
  • Data Product Marketplace, available through a partnership with Huwise, the leading provider of data marketplace solutions, enables organizations to publish and share trusted data products for internal and external use, so teams can reuse high-integrity data across the business, collaborate with partners, and power analytics and AI without rebuilding data for each project.
  • New APIs for Data Integration, Data Quality, and Data Catalog provide programmatic control over data pipelines, quality rules, and metadata – enabling automation and integration into AI-driven workflows.
  • The Precisely-hosted Model Context Protocol (MCP) Server extends the Data Integrity Suite APIs, enabling AI agents and tools to securely discover, access, and use these capabilities without custom integrations. This builds on Precisely’s previously released MCP server, which focused on location intelligence and data enrichment APIs.

“Data product marketplaces only deliver value when the data in them can be trusted,” said Franck Carassus, Co-founder & CEO, North America, Huwise. “Our partnership with Precisely brings data integrity and data product sharing together in one place—so organizations can publish, reuse, and share high-quality data with confidence across teams and partners, and accelerate AI initiatives.”

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Together, the capabilities in the latest Data Integrity Suite release give organizations a more direct, governed path from raw data to Agentic-Ready Data. With expanded APIs, a Precisely-hosted MCP Server, new agent capabilities, and a governed data product marketplace, teams can use trusted data across applications and AI workflows, without the custom engineering and manual prep that typically slow AI initiatives.

For example, one of the user’s data consumers can now ask a natural-language question like ‘What are the quality scores for our customer records?’ directly through an MCP-compatible client and receive the information without writing custom API calls or routing the request through a data engineering team.

“Organizations are eager to scale AI, but data readiness remains the biggest obstacle,” said Matt Waxman, Chief Product Officer at Precisely. “With the latest release of the Data Integrity Suite and our partnership with Huwise for the Data Product Marketplace, we are helping customers turn their data into a trusted, reusable asset that can directly power AI applications and agent-driven workflows.”

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Bluefish Launches AI Accuracy, Bringing Brand Verification to AI Channels for the First Time

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Bluefish Launches AI Accuracy, Bringing Brand Verification to AI Channels for the First Time

The launch is a first-of-its-kind AI accuracy technology and enables a new brand-verified data pipeline between enterprise brands and AI channels — improving AI accuracy for consumers, AI providers and commercial brands.

Bluefish, the Agentic Marketing Platform (AMP) for Fortune 500 brands, launched AI Accuracy — a first-of-its-kind capability that tracks whether AI channels are representing a brand factually. Central to the product release is Brand Vault, a new Bluefish capability that ingests a brand’s first-party content to establish a continuously updated, verified source of truth that can be shared with LLMs as training materials to improve accuracy.

AI accuracy is a critical priority for enterprise brands. Industry experts estimate that 5-20% of AI responses contain a factual inaccuracy or hallucination. According to a March 2026 Rithum survey of more than 1,000 US and UK shoppers, 58% say their trust in a brand decreases when AI provides incorrect product information. 16% abandon the purchase entirely, while only 5% verify AI recommendations on a brand’s own website. For enterprise brands managing hundreds of SKUs across dozens of markets, in categories where specifications, pricing and formulations change regularly, the surface area for AI to cite an error is enormous. In regulated industries including pharmaceuticals, financial services, and insurance, an inaccurate AI response carries direct legal and compliance exposure. Until today, commercial brands had no way to identify, measure or address these risks  at scale.

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The Bluefish Agentic Marketing Suite already enables marketers to track and influence AI performance across a number of dimensions:  AI Visibility, which measures whether a brand appears in AI responses; AI Favorability, which tracks whether the AI sentiment in the response is positive; and AI Safety, which flags content that is harmful or off-brand. Each of these metrics are critical KPIs to track, but the new AI Accuracy insights enable brands to understand what matters most – if the response is factually accurate.

Based on brand-verified information, this new Bluefish Accuracy technology continuously monitors AI responses across every major channel, extracting and verifying every factual claim made about the brand. Every mismatch is traced to the exact channel and response it came from, scored by severity, and filterable by product line, topic, and audience profile across millions of AI responses evaluated daily, enabling brands to quickly diagnose and take action.

“AI is only as accurate as its sources, and when AI gets facts about a brand wrong, consumers don’t blame AI, they blame the brand. That’s the dynamic that makes accuracy so consequential: it affects discovery, it affects conversion, and it affects trust,” said Alex Sherman, co-founder and CEO of Bluefish. “The brands that treat accuracy as a strategic priority now and build the infrastructure to manage it systematically will have a durable advantage over competitors.”

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“Accuracy is more than a defensive strategy, it is a growth lever. A consumer who gets correct, trustworthy information from AI at the research stage is more likely to convert, more likely to return, and more likely to trust the brand the next time AI surfaces it,” said Jing Feng, co-founder and COO of Bluefish. “Bluefish is built to deliver this at scale.”

Bluefish already processes millions of prompts daily for 10% of the Fortune 500, including Adidas, Hearst, and Ulta Beauty, across ChatGPT, Google AI, Claude, Perplexity, and Amazon Rufus. AI Accuracy is the first product of its kind, built on the most extensive AI channel data set in enterprise marketing.

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LinkSquares Launches the First and Only All-Agentic CLM Platform, Automating Contract Management from Draft to Execution

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LinkSquares Launches the First and Only All-Agentic CLM Platform, Automating Contract Management from Draft to Execution

New AI-native architecture transforms contract management by automating drafting, redlining and contract workflow in minutes, turning contracts into active drivers of business operations

LinkSquares, the leading AI-powered contract lifecycle management (CLM) platform, announced the launch of its new AI-native CLM platform, designed to move organizations from a static system of record to a dynamic system of execution.

Built from the ground-up on an AI-native architecture and powered by LinkAI, the new platform automates drafting, redlining and contract workflows in minutes, helping organizations move faster while maintaining control over high-stakes legal decisions. In the early access program, customers are already seeing significant time savings across core contract workflows.

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“We dropped a contract into the platform with a clear set of review notes, and within minutes it was fully redlined with suggested language already in place. What used to take hours was essentially done in two minutes. We reviewed for accuracy, and it was spot on. That’s when it clicked. This isn’t just AI assisting; it’s actually doing the work.” – LinkSquares Early Access Program Customer

Contracts sit at the center of revenue, risk and operational performance, yet most organizations still manage them as static files. At the same time, AI tools often operate outside core systems, forcing legal teams to manually connect those insights to business decisions. To solve this, most CLMs are simply adding AI to their legacy solutions, which are compounding the problems instead of solving them.

LinkSquares took a fundamentally different approach.

“The next phase of AI in CLM will move beyond helping customers work faster to AI driving the execution, with humans still reviewing the work and maintaining oversight into decision-making,” said Bill Hewitt, CEO of LinkSquares. “That’s a big shift that requires a completely different approach to how CLMs are built. Our new platform does just that, giving customers access to capabilities they have never seen before to ultimately deliver greater impact and value faster.”

LinkSquares’ new agentic platform is the first of its kind, designed to act on contracts instead of just analyzing them, handling repetitive tasks automatically while keeping legal teams in full control of review and strategic decision-making. Agents draft, redline, research and generate communications in minutes, while contract data automatically triggers workflows, tracks obligations and connects insight to execution in a single flow. Non-legal teams can use the platform within legal guardrails, accelerating deal cycles without introducing risk. And with citation-backed research, structured data and built-in governance, organizations can trust every output.

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“Most contract platforms are trying to layer AI onto systems that were never designed for it,” said Andrew Leverone, Chief Product Officer at LinkSquares. “We took a different approach by rebuilding the platform so that AI can operate across the entire contract lifecycle, planning and executing work inside of the system instead of generating outputs that someone has to act on.”

Key benefits of the new platform include:

  • Automatically drafts, redlines and applies clause libraries and playbooks with LinkSquares’ AI Legal Assistant, reducing hours of legal work to minutes while maintaining full control.
  • Turns contract insights into action by triggering workflows, tracking obligations and keeping work moving from intake through renewals in one connected system.
  • Eliminates bottlenecks with self-service and guardrails by automating intake, routing and approvals through LinkSquares’ Legal Front Door.
  • Moves quickly with confidence using trusted AI built on structured data, with citation-backed insights, governance and full transparency for high-stakes work.

Most importantly, the LinkSquares platform is designed to deliver immediate value without friction and fast adoption. Both legal and non-legal teams can start quickly – without heavy implementation or complex training – while maintaining the control and customization required for high-stakes contract work. Building on LinkSquares’ industry-leading customer satisfaction and time-to-value, the new platform further accelerates adoption, helping organizations move from onboarding to impact faster than ever.

The launch of LinkSquares’ all-agentic platform marks a shift toward contract systems that don’t just store and analyze information but actively move work forward. For in-house legal teams, the impact is immediate: faster execution, greater accuracy and consistency, and a more streamlined user experience.

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SlashID Launches AI Identity Governance, the First Access Graph-Native Solution Built to Govern OAuth-Connected AI Apps, Agents, and MCP Servers

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SlashID Launches AI Identity Governance, the First Access Graph-Native Solution Built to Govern OAuth-Connected AI Apps, Agents, and MCP Servers

Purpose-built to extend SlashID’s Access Graph to every AI identity touching corporate data — from OAuth 2.0 app authorizations and MCP servers to cloud-hosted models and browser-based shadow AI — with policy-based controls and continuous segregation-of-duties enforcement

SlashID, the platform that secures every identity, announced the launch of AI Identity Governance.  This represents the identity access graph’s first native governance capability. Through its identity access graph, SlashID enables customers to extend visibility, access control, and lifecycle policies from traditional users and service accounts to AI applications, agents, and MCP servers. This approach eliminates the governance gap and addresses Shadow AI—the most rapidly expanding source of unmanaged access to corporate data today.

The release arrives after SlashID’s analysis of the April 2026 Vercel security incident, in which attackers compromised an employee’s Google Workspace account through a malicious OAuth 2.0 application originating from a third-party AI tool. Traditional governance platforms, built for SaaS applications with predictable lifecycles, cannot keep pace with AI tools. These tools are installed in seconds, inherit broad OAuth scopes, and often connect further downstream via MCP and agent frameworks.

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“AI governance is fundamentally about identity and entitlements,” said Vincenzo Iozzo, SlashID’s Co-Founder. “Every time an employee authorizes a new AI assistant, connects an MCP server, or hands a task to an autonomous agent, they are effectively creating a new non-human identity with access to corporate resources. Security teams need the same visibility, policy enforcement, and lifecycle controls for those identities that they already have for users and service accounts — and they need it today, not after a year-long IGA re-platforming project.”

Enterprises are investing heavily in point solutions for AI security — DLP proxies, prompt firewalls, and CASB-style shadow AI discovery. These tools operate in isolation from the identity fabric, produce alerts without the context needed to act on them, and cannot answer the core governance question: which identities, human or non-human, can reach which resources through which AI applications. The result is that the same OAuth grant patterns that caused the Vercel breach remain unmanaged in most organizations.

SlashID’s AI Identity Governance solves these challenges with three core capabilities:

  • Unified Visibility Across the AI Identity Surface: Continuous discovery of OAuth 2.0 grants issued to AI applications, MCP servers, shadow AI usage surfaced through the SlashID Browser Extension. It also covers models hosted on Amazon Bedrock, Azure OpenAI, and equivalent CSP-native services. The Access Graph models OAuth scopes as first-class edges, so security teams can see not just that a user connected to an AI app, but exactly which mailboxes, drives, calendars, or repositories that app can reach.
  • Policy-Based Access Control for AI Applications and Agents: Allows teams to permit, restrict, or disable access to specific AI applications, model providers, or agentic identities using any attribute in the graph. Define rules once — for example, preventing HR or finance personnel from authorizing consumer AI tools — and enforce them continuously across the joiner-mover-leaver lifecycle, with a full audit trail for SOC 2, ISO 27001, and HIPAA reporting.
  • Continuous Segregation-of-Duties Enforcement: Security teams can express toxic combinations as saved Access Graph queries — for instance, “identities with access to regulated customer data that also hold active grants to external LLMs.”  These queries can be scheduled to automatically trigger remediation workflows, such as revocation, MFA step-up, ticket creation, or Slack notifications. The same primitive powers a range of AI-specific SoD policies without requiring a separate product.

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Unlike standalone AI security tools, SlashID’s AI Identity Governance operates at the identity graph layer, governing AI applications with the same primitives used for SaaS, cloud, and on-premise entitlements. It requires no changes to how employees use AI, no inline proxies, and no additional agents. The solution is available today to SlashID customers at no additional cost as part of the existing Identity Governance and Administration product, covering every major identity provider, cloud, and SaaS platform SlashID already integrates with.

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DealerOn Launches OnPrompt to Help Dealerships Compete in the age of AI Search

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DealerOn Launches OnPrompt to Help Dealerships Compete in the age of AI Search

New platform gives dealers visibility into how their business appears across AI-powered search experiences, helping them identify sentiment, competitive positioning, and content gaps

DealerOn, a leading provider of automotive dealership websites and digital sales and marketing solutions, announced the launch of DealerOn OnPrompt, an AI search visibility platform built for automotive dealerships.

Search remains one of the most important ways shoppers discover and evaluate dealerships. As AI-powered platforms like ChatGPT, Gemini, and Perplexity become part of that journey, dealers need new tools to understand how their business is represented in AI-generated answers alongside their traditional search performance.

OnPrompt helps dealers monitor AI visibility, evaluate brand sentiment, identify content gaps, and uncover opportunities to improve how their dealership appears across AI-powered search experiences.

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Built for the Next Layer of Search

Traditional SEO continues to play a critical role in helping dealerships attract shoppers through search engines. But the search experience is expanding. Consumers are increasingly asking AI-powered platforms direct questions about vehicles, service, pricing, reputation, and local dealership options.

OnPrompt gives dealerships a clearer view into this emerging layer of search. Rather than claiming to expose private user queries inside AI platforms, OnPrompt helps dealers evaluate how AI systems may respond to relevant dealership, inventory, service, and market prompts—then turns those findings into practical recommendations for improving visibility, accuracy, and representation.

Core features include:

  • Prompt Tracking: Monitor how AI platforms respond to relevant prompts about your dealership, inventory, services, and local market. 

  • Visibility Analysis: Understand where and how your dealership appears across AI-powered search experiences, including whether it is mentioned, omitted, or positioned against competitors.

  • Brand Sentiment Analysis: See how AI tools describe and characterize your dealership, including tone, accuracy, strengths, and potential reputation concerns.

  • Content Gap Identification: Identify missing, weak, or unclear website content that may limit how well AI systems understand your dealership, services, inventory, and differentiators.

  • Competitor Analysis: Compare your dealership’s AI visibility and positioning against local and regional competitors.

  • AI Referral Analytics: Identify traffic patterns from AI-powered platforms and understand how those visitors engage with your website.

  • Crawler Visibility: Gain insight into how AI-related crawlers and bots interact with dealership website content, helping dealers understand which pages are accessible and discoverable.

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To help dealers act on these insights, DealerOn is also introducing OnPrompt GEO Services, a managed service that turns AI visibility findings into content, technical, and optimization strategies. Designed to complement DealerOn’s traditional SEO services, OnPrompt GEO Services helps dealers strengthen the content and signals AI platforms may use to understand, summarize, and recommend their business.

“Search isn’t disappearing—it’s evolving,” said Ali Amirrezvani, CEO and Co-Founder of DealerOn. “As shoppers turn to AI-generated answers to decide where to buy and service vehicles, dealers need visibility into how their business is being represented. OnPrompt gives them a clear, practical way to understand and influence this new layer of discovery alongside traditional SEO.”

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Yellow.ai Launches Nexus Vox: The First Enterprise Voice AI That Can Clone Any Brand’s Voice and Deploy It Across 500+ Languages in Under a Second

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Yellow.ai Launches Nexus Vox: The First Enterprise Voice AI That Can Clone Any Brand's Voice and Deploy It Across 500+ Languages in Under a Second

The first enterprise voice AI built as one system, eliminating the multi-vendor complexity, latency tax, and language ceilings that have held enterprise voice automation back for a decade.

Global enterprises answer billions of customer calls a year. Almost all of them run through complex stitched-together architecture. One vendor handles speech recognition. Another handles voice synthesis. A third runs the conversational AI. A fourth manages the telephony. The result is a system full of handoffs, delays, and procurement overhead. For a decade, this has been accepted as the cost of doing business.

Yellow.ai announced Nexus Vox that fundamentally reimagines how enterprises deploy voice automation. Vox is the first enterprise voice AI built as a single integrated system rather than stitched from multiple vendors’ APIs. The result is voice agents that:

  • operate below the latency threshold of natural human conversation,
  • support more than 500 languages and dialects natively (a breakthrough made possible by Vox’s native integration with Yellow.ai’s proprietary multilingual AI models)
  • can be trained on 10 seconds of any human’s voice
  • are wired directly into enterprise systems that actually run the business so conversations don’t just sound right, they get resolutions.

“Enterprise voice AI so far has been a Frankenstein’s monster with several different vendor APIs stitched together, each adding latency, each introducing a point of failure, and none of them effectively helping resolve the problem. Every vendor has tried to fix it by adding another limb. ” said Raghu Ravinutala, Co-founder and CEO of Yellow.ai. “Vox is the first voice AI built from the ground up to ensure the voice and the brain share the same runtime. It’s a completely different architecture.”

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The operational problem Vox is built to solve

Enterprises today are managing a voice channel that fails in three specific ways:

  1. It fails to sound human. Every API handoff in a stitched stack adds 100 to 200 milliseconds of latency. Round-trip response times end up at 800 milliseconds or more — long enough to make conversations feel robotic. And because most platforms use generic synthetic voices, every enterprise ends up sounding identical.
  2. It fails to serve the business’s actual customer base. Most enterprise voice AI platforms support fewer than 30 languages. English-speaking customers get automation. Everyone else gets an agent queue or a dropped call. For enterprises operating across international regions, it’s a structural limit on who the business can automate for at all.
  3. It fails to provide autonomous resolutions. Most voice AI is a speech interface bolted onto a chatbot. Without shared context between the voice and conversation layers, the system can’t orchestrate the CRM, ticketing, and booking engines needed to complete real tasks — and containment on complex calls stays low.

Nexus Vox is built to address all three and eliminate this architecture entirely.

How Nexus Vox works

Nexus Vox runs natively inside Yellow.ai’s Nexus platform sharing the same runtime as the conversational AI, knowledge base, and automation engine. Yellow.ai refers to this as a “zero-hop architecture.” There are no API round-trips between voice processing and conversation processing.

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Key capabilities include:

  • Sub-400ms end-to-end latency from customer speech to AI voice response. Within the range of natural human conversation.
  • 10-second voice cloning from any audio sample, the original speaker’s timbre, cadence, and emotional range are preserved.
  • 500+ languages and dialects with natural, human-quality voice synthesis. Including Gulf, Levantine, and Egyptian Arabic as distinct dialects, not a generic standard fallback.
  • Omnichannel deployment across telephony (SIP/PSTN), web widgets, and REST APIs. From a single configuration.
  • Real-time sentiment awareness. Vox adjusts tone, pacing, and escalation behavior mid-conversation.
  • CX and EX support. Built as a single platform for both customer-facing and employee-facing voice automation. Not two separate products.

Market context

The enterprise voice AI market is projected to reach $47.5 billion by 2030, yet adoption remains hindered by the limitations of current architectures. According to Everest Group, voice quality, latency, and limited language support remain the primary barriers to enterprise adoption at scale.

“Breakthrough developments in large language models are accelerating the shift from clicks to conversations,” said Anubhav Das, Practice Director at Everest Group. “Yellow.ai’s voice platform addresses the limitations of traditional voice bots — high latency, poor response audio quality, and limited conversational depth. It has the potential to help enterprises redefine customer experience.”

Enterprise use cases

Nexus Vox is designed for enterprises that handle high volumes of voice interactions across multiple languages and channels. Early use cases include:

  • A global bank using Vox to handle 12 million monthly customer calls in 47 languages — expanding coverage from the three supported by its legacy IVR, without adding headcount or regional vendors. First-call resolution and cost per call, both improve dramatically.
  • A hospitality group deploying a single cloned concierge voice across 30 properties worldwide. Every guest is greeted in their native language by the same branded voice — without localized recordings or regional voice talent. Guest satisfaction scores on arrival experience improve, and all pre-arrival queries are fully automated.
  • A telecommunications provider using Vox to run internal IT helpdesk support in 15 regional languages, 24/7, without regional helpdesks per timezone. Level-1 tickets that previously took hours to resolve across geographies now close in under two minutes.

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

Sumsub Partners With Chainlink to Power Cross-Chain Identity for On-Chain Compliance

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Sumsub Partners With Chainlink to Power Cross-Chain Identity for On-Chain Compliance

Sumsub and Chainlink to Enable Privacy-Preserving KYC Credentials Across Ethereum, Arbitrum, Avalanche, Polygon, and Base

Sumsub, a global verification and anti-fraud leader, announced its partnership with Chainlink, the industry-standard oracle platform. This enables compliant, privacy-preserving identity verification across major blockchains, allowing clients to leverage Chainlink’s Automated Compliance Engine (ACE). As digital asset markets move into a more regulated phase, the ability to verify users across blockchain ecosystems without compromising privacy is becoming critical infrastructure.

The partnership provides access to a Cross-Chain Identity (CCID) framework, a core component of Chainlink ACE, to unlock reusable, privacy-preserving identity credentials on-chain in a way that supports compliant access across blockchain ecosystems. This addresses key challenges in on-chain compliance, including enabling verification without exposing raw personal data, supporting reusable identity across multiple wallets, and allowing permissioned access for asset issuers and protocols to enforce eligibility rules.

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Once a user completes Sumsub’s KYC flow and proves wallet ownership by signing a message, Chainlink ACE issues a CCID – a reusable, privacy-preserving credential containing verified claims like “Age > 18.” No raw personal data ever touches the chain. This mechanic allows a single verified identity to be linked across multiple wallets, eliminating the need for repeated KYC at every entry point.

The initial rollout supports Ethereum, Arbitrum, Avalanche, Polygon, and Base, and is designed for a global retail-oriented audience participating in ACE launch campaigns. This represents Phase 1 of the rollout, in which individual retail users complete Sumsub’s KYC flow and receive a reusable ACE credential linked to their wallet.

“Digital asset markets need identity verification that can extend into compliant on-chain workflows without forcing users through repeated onboarding,” said Ilya Brovin, Chief Growth Officer, Sumsub. “Through Chainlink ACE, Sumsub can extend its identity verification services into compliant institutional digital asset markets and help enable access to permissioned assets with less friction.”

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“We’re excited to see Sumsub support Chainlink’s Automated Compliance Engine to advance privacy-preserving identity and compliance infrastructure, enabling the Cross-Chain Identity (CCID) framework for our clients. This is the kind of scalable, privacy-preserving compliance infrastructure needed to unlock tokenized assets at institutional scale.” — Ishan Vishnoi, VP of BCM Product & Business Ops, Chainlink Labs.

This launch marks the first phase of a broader roadmap. Phase 2 is scheduled to be released in the summer of 2026, transitioning to a model in which Asset Issuers act as the end users. Future phases may involve Sumsub enabling users to connect to authorize third-party access to underlying data via API. Together, these phases reflect a shared vision for identity infrastructure that scales alongside the compliant digital asset ecosystem.

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

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