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Zenity Sets the Foundation for Guardian Agents With Continuous, Contextual Security

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Zenity Sets the Foundation for Guardian Agents With Continuous, Contextual Security

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New capabilities transform AI security from disconnected signals into continuous, contextual risk assessment, enabling security teams to understand and act on AI risk as it evolves

Zenity, the leading security and governance platform for AI agents in the enterprise, announced continuous, contextual security for AI agents – a new approach that transforms how enterprise AI systems are secured and sets the foundation for Guardian Agents.

Zenity’s continuous, contextual security closes these gaps and lays the groundwork for Guardian Agents, enabling security teams to accurately identify, prioritize and respond to AI risk as it evolves across the enterprise.

According to Gartner, “Guardian Agents represent the next evolution in AI governance, shifting from passive monitoring to active, real-time protection of AI systems.” Zenity’s continuous, contextual security delivers the foundational capabilities required to enable this shift.

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AI agent risk doesn’t emerge in a single moment. It develops over time across configuration changes, runtime behavior, long-horizon tasks and interactions between agents, users and enterprise systems. Their behavior and exposure can shift in real time as agents rewrite instructions, update memory, and dynamically alter execution. Yet most security approaches, across legacy and startup solutions, still rely on snapshot posture scans and stateless prompt analysis, missing attacks that unfold across multiple interactions and leave teams with a view of risk that is outdated the moment it’s captured.

“Enterprise AI security is breaking under the current model. Agents don’t just execute, they reason, take action and evolve. As they do, risk evolves with them, continuously across every interaction and trigger,” said Ben Kliger, CEO of Zenity. “This release sets a new standard for securing agent-driven systems. Zenity is the first platform to unify posture, runtime behavior, and threat signals into a real-time view of risk. This is the architecture required to secure AI agents at scale, and we’ve built it.”

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Zenity’s continuous, contextual security closes these gaps and lays the groundwork for Guardian Agents, enabling security teams to accurately identify, prioritize and respond to AI risk as it evolves across the enterprise. With Zenity, teams are empowered to:

  • Detect evolving threats with agent context in mind with the stateful threat engine that analyzes full interaction chains in real time, across users, agents and sessions. This includes multi-step prompt injection, gradual data exfiltration and tool misuse that would otherwise appear benign.
  • Maintain real-time exposure visibility by replacing periodic posture scans with real-time, event-driven ingestion that reflects configuration, permission, MCP, and connector changes as they occur, ensuring security teams operate on current risk.
  • Prioritize what matters most with Issues Correlation Agent, connecting posture, runtime activity, and environmental signals into unified risk objects and allowing teams to identify where exposure and active behavior intersect.

By bringing together real-time exposure visibility, stateful runtime detection and contextual risk correlation into a unified model, Zenity moves beyond fragmented monitoring toward intelligent, adaptive security that understands how risk develops and evolves.

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SentinelOne Brings AI Security to On-Premise, Regulated, Sovereign, Self-Hosted and Airgapped Environments

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CenterPoint Group Acquires Prokuria to Bring AI-Powered Solutions to Group Purchasing

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New and expanded on-premises offerings deliver industry-leading, AI-powered security, giving customers complete control over their data while ensuring it never leaves their environment.

SentinelOne® , the AI Security leader, is bringing the power of autonomous AI security to on-premise and self-hosted environments. The new and expanded portfolio builds on SentinelOne’s existing advantage as the only next generation cybersecurity company to deliver modern, cutting edge endpoint protection with zero cloud dependency. By ensuring all data is processed strictly within the customer’s own environment, the offerings provide complete data privacy and sovereignty. Already deployed across millions of on-premises endpoints, the expanded portfolio will now secure servers, private clouds, and data pipelines at an unprecedented autonomous scale. As a result, highly regulated public and private sector organizations can now defend their most critical, air-gapped environments with the power and speed of AI, without ever sacrificing control of their data.

“Empowering global organizations with the certainty that their data stays in their control is more urgent than ever given the need to adopt AI without compromising privacy. For too long, organizations in highly regulated sectors have faced a trade off between the speed of AI security and total data sovereignty, privacy and control – especially for airgapped networks,” said Ana Pinczuk, President of Product and Technology at SentinelOne. “At SentinelOne, we are committed to breaking that trade-off. By delivering our most advanced autonomous engines and AI protections directly into the customer’s own hardware environment, we are giving them the freedom to innovate securely.”

The security market is shifting as governments and regulated industries increasingly demand total control over where their data lives and how it is processed. This is especially pronounced with the rapid rise of AI – causing these organizations to ensure that AI applications and data stay secure and fully aligned to their principles, without any third party dependency. At the same time, rising geopolitical risks are forcing critical infrastructure to move toward air-gapped systems that can stay secure even when disconnected from the internet.

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Building on its successful FedRAMP and GovRAMP authorized on-premise endpoint security capabilities, the expanded offerings introduce AI-powered protection and the same level of security trusted in the public cloud to the world’s most sensitive organizations. By using a single, lightweight agent, national security agencies, financial institutions, and healthcare providers can standardize their security across any infrastructure. These deployments allow customers to keep all their data in-house, streaming telemetry directly into their own systems for threat hunting and investigations without ever sending information to a cloud service. This established global footprint already protects tens of millions of endpoints for critical infrastructure operators who require total data control.

To further secure private and sovereign clouds, SentinelOne provides real-time protection for servers, containers, and data storage that operates entirely within a customer’s own network. This technology is built to be stable and autonomous, using multiple detection engines that work on-device without needing a persistent internet connection. The solution also extends to local data stores, integrating with systems like NetApp and Dell to automatically scan and quarantine malware at the point of entry. By keeping all threat detection and remediation local, organizations ensure that sensitive data never leaves their secure boundary during the inspection process, and that no off-premise connectivity is ever possible.

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SentinelOne’s Prompt Security On-Premise adds these same protections to the world of AI, even in fully disconnected environments. This self-hosted AI security discovers “shadow AI” usage and redacts sensitive information in real time across thousands of applications. By acting as a specialized firewall for both internal and external AI tools, Prompt Security blocks threats like prompt injections and data leaks while ensuring organizations maintain complete sovereignty over every interaction – no external connection required.

Beyond core security, SentinelOne is introducing a new AI Data Pipeline designed specifically for on-premises environments to optimize how local data flows. By using intelligent filtering, this pipeline helps security teams reduce alert fatigue and cut down on infrastructure costs by only processing what matters most. It also enriches telemetry and monitors the health of the entire data stream, giving organizations better visibility and more reliable insights. AI Data Pipelines allow moving data easily while sanitizing it between different sources and end points like Generative AI models – without the data ever leaving the premises or cloud processing needed.

The expanded offerings can be deployed anywhere so customers can easily meet in-country data residency and regulatory requirements achieving true sovereignty without sacrificing platform capability.

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HuLoop Launches QuickApp Builder to Digitize Paper, Create Intelligent Micro Applications Across Its AI-Powered Work Optimization Platform

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HuLoop Launches QuickApp Builder to Digitize Paper, Create Intelligent Micro Applications Across Its AI-Powered Work Optimization Platform

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HuLoop Automation, a leader in AI-powered work optimization, announced the launch of QuickApp Builder, a no-code capability that transforms paper-based and static data collection into intelligent micro applications that automate work across the HuLoop platform.

“Teams can design purpose-built micro applications that activate workflows instantly, update systems in real time, and eliminate the BS work that slows operations down.”

Organizations continue to rely on PDFs, spreadsheets, and basic forms that require manual re-entry and fragmented approval chains, slowing execution and increasing risk. QuickApp Builder eliminates these bottlenecks by capturing validated information once and automatically initiating the appropriate workflows, system updates, and approvals.

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As the newest module in HuLoop’s rapidly expanding portfolio, QuickApp Builder works alongside Productivity Discovery, Work Orchestration, Process Automation, Content Processing, and Test Automation to digitize frontline processes and accelerate end-to-end efficiencies. Integrated natively within HuLoop’s Unified Work Optimization Platform, it guides users through dynamic, context-aware inputs, exposes only relevant fields, and routes information seamlessly across systems the moment it is submitted. This eliminates disconnected tools and enables a single source of truth across operational workflows.

“Traditional, static data collection methods continue to bog down teams with avoidable manual work and process delays,” said Todd P. Michaud, CEO of HuLoop. “QuickApp Builder digitizes data collection into intelligent micro applications that drive automated action. Teams can design purpose-built micro applications that activate workflows instantly, update systems in real time, and eliminate the BS work that slows operations down.”

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Teams can launch fully branded micro applications in as little as 30 minutes. QuickApp Builder also supports enterprise-grade integrations, including SQL databases and REST APIs, allowing organizations to connect data across systems while orchestrating workflows from a centralized platform.

By replacing manual data entry with intelligent collection and automated workflows, organizations can increase efficiency and accuracy by 50-70 percent and free employees from hours of redundant work daily. In banking environments, for example, a financial institution with 50 tellers could save up to 300 hours per month through digitized forms and automated approvals.

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Overfuel Unveils Automotive Industry’s First Search Engine Intelligence Platform to Quantify Factors in AI and Search Visibility

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Overfuel Unveils Automotive Industry's First Search Engine Intelligence Platform to Quantify Factors in AI and Search Visibility

Proprietary Platform Powers Landmark 2026 Mobile PageSpeed Study — and Is Built for Continuous, Real-Time Monitoring of Every Element on the Automotive SERP

Overfuel, the automotive industry’s leading dealer website platform, officially unveiled its proprietary Search Engine Intelligence platform the first tool specifically engineered to reveal the factors that drive AI Overview (AIO) inclusion and organic search visibility for automotive dealerships. While the platform powered the findings of Overfuel’s landmark 2026 Automotive Mobile PageSpeed Study, it is built for something far larger: continuous, real-time monitoring of every element on the automotive search engine results page.

“Dealerships can no longer rely on ‘feeling’ fast,” said Douglas Karr, Principal SEO at Overfuel. “Our platform reveals the data-driven factors that secure top positions. If you aren’t ranking for the most voluminous and highest-value keywords, you’re missing the most intentful buyers in your market. We provide the intelligence to make sure your digital front door stays open.”

Beyond Traditional SEO Tools

Where conventional SEO platforms stop at rankings and keyword tracking, Overfuel’s Search Engine Intelligence platform goes further — capturing the full picture of what local car buyers actually see on their mobile devices. The platform monitors organic listings, local map packs, paid advertisements, People Also Ask (PAA) boxes, and Knowledge Graph panels across the largest 250 U.S. markets simultaneously.

To ensure data integrity, the platform uses a Stateless Request Architecture that bypasses session history, cached cookies, and user behavior biases, delivering an untainted view of local search results as they appear to car shoppers in each market.

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Key platform capabilities include:

  • Economic Impact Modeling: Translates search rank directly into dollar value by integrating keyword cost-per-click (CPC), metro population data, and position-specific click-through rates — giving dealers a clear line of sight from technical performance to revenue impact.
  • AIO Impact Analysis: Quantifies how Google’s AI Overviews affect click-through rates at each rank position, and reveals how top organic spots dominate inclusion in generative AI results.
  • Granular Search Taxonomy: Classifies search phrases by vehicle segment, intent type, and keyword category to isolate performance patterns across new-vehicle, used-vehicle, make-specific, and commercial queries.

Proven at Scale: The 2026 Mobile PageSpeed Study

The platform’s debut at scale produced one of the most consequential findings in automotive digital marketing to date. Analyzing 89,257 organic search result rows across 9,020 dealership domains — drawn from 25,000 searches executed across 250 geographic markets — the 2026 study found a near-perfect statistical correlation between mobile PageSpeed scores and Google search rankings.

Only 8% of dealership domains earned a “Good” mobile PageSpeed score (90+) across all URLs. Nearly 70% scored in the “Poor” tier (0–49). The Pearson correlation between rank position and the share of Good scores was r = -0.932 — a relationship statisticians classify as “very strong.” Sites at position #1 were more than 1.4x more likely to hold a Good score than sites at position #10, and captured roughly 22x the clicks.

“The 2026 study validated what our platform was built to measure,” said Alex Griffis, CEO of Overfuel. “But the real value isn’t a single report — it’s continuous intelligence. The automotive SERP is constantly changing, and dealers need a platform that keeps pace. That’s exactly what we’ve built.”

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The Intelligence Layer Automotive Has Been Missing

As Google’s AI Overviews increasingly compress the traditional organic SERP, the stakes for dealerships have never been higher. Top organic positions now serve as the gateway to AI-generated results, meaning the dealers who rank at the top capture traffic from multiple search surfaces at once — while those ranked lower face an accelerating visibility crisis.

Overfuel’s Search Engine Intelligence platform is designed to close that gap, giving dealers and dealer groups the ongoing intelligence they need to understand exactly where they stand, what’s driving competitors’ results, and where the greatest opportunities for organic growth exist in their local markets.

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Multiply Raises $9.5M for Self-learning Ads, Reports 300%-500% Pipeline Increase for B2B companies

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Multiply raises $9.5m for self-learning ads, reports 300%-500% pipeline increase for B2B companies

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Multiply is the first hybrid AI + human media agency for B2B companies. Launches Self-Learning Advertising, where ads learn from company data and continuously improve.

Multiply is the first AI-native media agency for B2B companies. All marketers know that in traditional advertising, campaigns start losing effectiveness the moment they launch. Creative gets stale and audiences tune out. Multiply calls this phenomenon “decaying ads.”

Today, the company emerged from stealth with $9.5 million in funding to introduce what it calls the next paradigm: Self-Learning Advertising, where ads use internal data to continuously get better on their own. The round was led by Mayfield, with participation from Sorenson Capital, Instacart Co-Founder Max Mullen, Google Head of Gemini and Google Labs Josh Woodward, and executives from HubSpot, Braze, Issuu, Brex, Sierra, and Common Room, among others.

Early customers report outsized impact in sales pipeline generated from ads. Vanta, a leader in security automation, which has raised over $500 million from Sequoia Capital and other top VCs, shared: “We’ve seen 770% more sales meetings, we build and test faster with their AI, and their team is strategic, hands-on, and operates as trusted partners.” Listen Labs, the leading AI customer research platform that has raised $100M, said LinkedIn has become its most efficient paid channel for new leads, with campaigns performing 5X above LinkedIn benchmarks. Across customers, the common thread is velocity, and lead quality, and pipeline impact.

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Multiply Co-Founder & CEO Matt Jayson explains that “Modern companies already have all the data needed to create radically better ads. Sales conversations, CRM systems, and pipeline outcomes reveal exactly why customers buy – yet those insights rarely make their way into ad campaigns fast enough.” Today, Multiply focuses on Google Search ads and LinkedIn ads. The company connects directly to sales call recordings, CRMs, and ad platform performance data to generate new creative and messaging aligned to why buyers choose a company over competitors. Hundreds of structured experiments run continuously, refining audiences, copy, and creative, so campaigns improve every week–instead of declining.

Multiply was founded by Matt Jayson, formerly at Google and Brex, and Ashish Warty, formerly SVP Engineering at HackerOne and engineering leader at Dropbox and Airship. Jayson describes the company’s ambition: “We help companies get discovered by their dream customers. To do this, we’ve built the world’s most insatiable AI agent. Just like a great growth marketer, it’s never satisfied. There’s never enough pipeline. So it keeps learning, testing, and finding ways to get better.”

To tackle something this ambitious, Multiply couldn’t just build AI software. The company operates as a media agency staffed by expert strategists, who use Multiply’s proprietary AI to operate campaigns at speeds and with impact previously impossible.

Multiply’s Customer Insights AI Agent extracts real customer language from sales calls and uses it to personalize ads. The ICP Agent analyzes closed-won deals to refine targeting. The Quality Score Agent continuously tunes copy and keyword alignment. The Creative Design Agent refreshes images weekly. The A/B Testing Agent runs hundreds of experiments, quickly identifying winners and cutting losers. Ashish Warty, Co-founder and CTO of Multiply, describes, “Together, these systems allow Multiply to iterate faster than any traditional agency model.”

“Brand safety is paramount,” explains Warty. “Every campaign includes human oversight from experienced media buyers, and we work within each customer’s brand and compliance requirements. We move as fast as their teams and systems allow.”

While Multiply launched first with Google and LinkedIn ads, the company says its infrastructure was designed for emerging AI-driven ad platforms like ChatGPT ads. Multiply is already helping its customers prepare for ChatGPT ads. All campaign learnings and experimentation systems can extend directly into new formats, including conversational and AI-driven advertising experiences.

“There is a major shift happening in the $50B B2B advertising market,” said Patrick Salyer, Partner at Mayfield and Multiply board member. “Service-as-Software is redefining how companies grow, and Multiply has built the first AI model for B2B advertising. Instead of static campaigns managed manually, Multiply has become a compounding growth engine for every company it partners with.”

Looking ahead, Multiply will expand into a full omni-channel ad buyer for B2B companies, enabling businesses to launch and optimize advertising across all major platforms from a single system. The roadmap includes expansion to additional channels, daily creative refresh, unified cross-channel attribution, and AI-driven budget allocation across ad channels to maximize pipeline impact. As new AI-powered advertising channels emerge, Multiply aims to help customers adopt them early while continuing to outperform across existing platforms.

Multiply is the first AI media agency, designed specifically to help B2B companies get discovered by their ideal customers and turn ads into a reliable pipeline engine. Early customers report outsized gains. Companies using Multiply have seen 300 to 500% improvements in sales meetings booked and pipeline generated from ads.

It combines proprietary AI with experienced growth strategists who operate as an extension of a customer’s team. Multiply plugs directly into sales calls and CRM data to understand why customers actually buy. Its AI agents then translate those insights into highly personalized ads, launch hundreds of structured experiments, and continuously optimize performance across Google and LinkedIn.

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Aurora Mobile’s EngageLab Redefines Marketing Agility at MarketingPulse: Bridging AI Trends with Unified Execution

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Aurora Mobile's EngageLab Redefines Marketing Agility at MarketingPulse: Bridging AI Trends with Unified Execution

Aurora Mobile Limited, a leading provider of customer engagement and marketing technology services, announced that its AI-first customer engagement platform, EngageLab, successfully concluded its high-profile participation at Hong Kong MarketingPulse & eTailingPulse 2026, organized by HKTDC. Throughout the event at the HKCEC, EngageLab’s Booth became a premier destination for marketers seeking to “Redefine Customer Relationships for the AI Era.”

Turning AI Trends into Marketing Reality

MarketingPulse 2026 was abuzz with discussions on Agentic AI, reflecting a critical shift in the industry: marketers are no longer looking for simple automation, but for intelligent “agents” capable of autonomous reasoning and execution. As brands grapple with the challenge of scaling personalized experiences without losing the human touch – a recurring theme across the summit’s keynote sessions – EngageLab stood out by demonstrating how these high-level trends are already integrated into its Native AI capabilities:

AI-Human Collaboration: EngageLab LiveDesk showcased how AI Agents seamlessly collaborate with human agents within the LiveDesk environment. By handling routine inquiries and automating complex workflows across all messaging channels, AI allows teams to focus on high-value customer interactions.

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AI-Powered Marketing Efficiency: Aligning with the event’s focus on long-term value, EngageLab demonstrated how its Native AI automates high-converting content generation and optimizes delivery strategies. This ensures that every message is not only personalized but also delivered at the most impactful moment, significantly shortening the path from engagement to conversion.

The “All-in-One” Breakthrough: Solving the Marketer’s Greatest Pain Point

The most significant highlight of the event was the overwhelming interest in EngageLab’s Unified Platform, which directly addressed the “fragmentation fatigue” voiced by many e-commerce leaders during the summit’s Digital Transformation sessions. In a landscape where marketing stacks are increasingly siloed—often resulting in disjointed customer experiences and lost conversion opportunities—EngageLab’s ability to consolidate channels, verify, marketing and support into an united, cohesive ecosystem resonated with businesses.

Visitors saw how a single source of truth—combining marketing automation with real-time support—can reshape the consumer journey. This unified approach allows brands to move beyond “stitched-together” stacks to a truly Customer-Centric Marketing Automation strategy that unlocks long-term value.

“Marketers don’t just want to send messages; they want to build relationships,” said the Marketing Director at EngageLab, Tanya Quan. “By combining modular products, we provide a unified solution that allows brands to ‘start anywhere, expand into one platform,’ simplifying the complex marketing tech stack and unlocking long-term value.”

As the event drew to a close, EngageLab’s message to the marketing world remained clear: in an era of complexity, the path to growth lies in simplification. Whether starting with a single channel or deploying a full-scale AI-driven ecosystem, EngageLab stands ready to help brands turn every interaction into a lasting relationship.

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Bazaarvoice Research Finds Consumers Are Using AI to Help Edit Reviews, Not Ghostwrite

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Bazaarvoice research finds consumers are using AI to help edit reviews, not ghostwrite

New data reveals nearly one in four consumer product review writers use AI tools, but the vast majority are using them only to refine grammar and tone

New research from Bazaarvoice Inc., the world’s leading platform for collecting and distributing authentic consumer product ratings and reviews, reveals how shoppers feel about the intersection between AI and the online product review ecosystem. The findings suggest that while shoppers may not be completely trusting of AI’s role, the core issue isn’t simply its use, but how it is applied and communicated. As AI becomes more common in content creation, consumers are making it clear that authenticity, transparency, and preserving a reviewer’s personal voice and valid personal product experience remain essential to maintaining trust in reviews.

According to the research, nearly 1 in 4 review writers (23%) say they use AI at least sometimes to help write reviews, signaling that AI is quickly becoming part of the review creation process. Yet despite rising adoption, 64% say reviews written with AI are not authentic, highlighting a growing tension between technological convenience and consumer trust. What makes this distrust interesting, however, is that only 16% are very confident they could distinguish between an AI-written and human-written review.

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“AI is rapidly becoming part of the shopping journey, but when it comes to reviews, authenticity still matters above all else,” said Doug Straton, Chief Marketing Officer at Bazaarvoice. “Consumers may use AI as a tool to help refine their thoughts, but they ultimately trust reviews that reflect real experiences and real voices. Our Content Coach feature is the perfect example of using AI to help generate reviews authentically. It doesn’t write anything for the shopper – it simply uses AI to suggest unbiased topic ideas tailored to the specific product category for the reviewer to write about, preserving review authenticity while increasing the length and richness of reviews to fuel future customer purchases.”

Despite authenticity concerns, the research shows that most consumers using AI tools are not outsourcing their opinions. Instead, they are using AI primarily as an assistant to improve clarity and structure. In fact, nearly half (47%) of respondents use on-site suggestion tools that are embedded in review platforms for this refinement. Those using third party generative AI tools such as ChatGPT, Gemini, or Perplexity, overwhelmingly (83%) reported they write the full review themselves first, using AI tools only to refine grammar and tone. Additionally, half of respondents (53%) stated that they share their own notes and bullet points to guide AI assistance, showing that AI is not being used as the ghostwriter but as the copy editor.

“Whether it comes to artificial intelligence usage in review writing or LLM suggested product recommendations, our research has shown time and time again that authenticity is paramount,” said Alex Kirk, Director of Insights at Bazaarvoice. “In previous research we’ve found that consumers are more likely to trust product recommendations provided from generative engines when they know that authentic ratings and reviews are sourcing the suggestions, so it makes sense that they want reviews to be written based on real human experience. Consumers can use AI to improve the grammar and clarity of their reviews, but they should always make sure they’re getting their honest thoughts and opinions across.”

Even among consumers who use AI to help write reviews, concerns about authenticity remain. Many respondents say AI-generated outputs can feel overly promotional or disconnected from their real experiences, with 48% saying the tone feels robotic, 44% saying it erases their voice, and 35% worrying it could introduce inaccurate product details.

“AI is here to stay, and its adoption is only accelerating; banning it entirely from review content is not only extremely difficult, it’s unnecessary – we’d have to ban the use of spelling and grammar checks built into our device operating systems while review writing, after all,” continued Straton. “A review written with AI assistance is not inherently fraudulent. There is a world of difference between a customer using AI to articulate their genuine experience and a bad actor using it to mass-produce fake reviews for products they’ve never touched. To ensure that AI and authenticity can coexist, we’re continuing to create digital guardrails such as our Intelligent Trustmark, a visual symbol we display on reviews that have passed Bazaarvoice’s rigorous authenticity standards, and are proven to be real and verified. Proper disclosure and unbiased moderation processes will be key as this space evolves.”

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LTM Expands BlueVerse™ Tech with AppIQ, AgentIQ and FusionIQ to Accelerate AI‑Led Engineering

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LTM Expands BlueVerse™ Tech with AppIQ, AgentIQ and FusionIQ to Accelerate AI‑Led Engineering

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LTM – the Business Creativity partner to the world’s largest enterprises, announced the expansion of BlueVerseTM Tech, its AI‑led engineering platform, with the launch of AppIQ, AgentIQ and FusionIQ—three purpose‑built platforms designed to help enterprises modernize applications, orchestrate AI‑first software delivery, and engineer quality at scale.

As software development evolves from human‑only execution to human + intelligent agents, traditional effort‑driven engineering and QA models are increasingly unable to keep pace. These BlueVerseTM platforms embed agentic, engineering‑aware AI across the software development lifecycle (SDLC), enabling enterprises to move faster from legacy complexity to modern, resilient, and high‑quality digital systems.

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AppIQ — Modernize Legacy Applications, Faster

AppIQ applies AI to read and understand legacy codebases, generate documentation, map functional workflows, and produce actionable specifications for forward engineering. What previously required weeks of reverse engineering can now be completed in days, significantly reducing modernization risk and cost.

AgentIQ — Orchestrate AI Agents Across Software Delivery

AgentIQ provides a unified platform to deploy, govern, and orchestrate AI agents across the software delivery lifecycle, with ready‑to‑use agents, no‑code setup, and enterprise‑grade security—enabling production‑ready AI adoption across engineering teams.

FusionIQ — Accelerate Speed to Market with Assured Quality

FusionIQ accelerates enterprise test automation across the software testing lifecycle, from requirement understanding and test design to automation scripting, test data management, and continuous optimization by embedding AIdriven monitoring and feedback into testing workflows.

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Together, AppIQ, AgentIQ and FusionIQ deliver 40–50% reduction in engineering effort across the software development lifecycle — from legacy modernization and AI-driven delivery to quality engineering — while accelerating time-to-market and lowering ongoing operational costs.

“BlueVerseTM Tech reflects a fundamental shift in how engineering organizations create value with AI. By embedding AI across modernization, delivery orchestration, and quality engineering, we are helping clients reduce complexity, improve predictability, and move faster with confidence—turning AI from experimentation into measurable business advantage at scale,” said Gururaj Deshpande, Chief Delivery Officer, LTM.

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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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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.

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

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.”

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

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.”

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

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.

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

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.”

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

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