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Noonlight Upgrades Video Monitoring Solution with AI Person Filtering and Advanced Verification

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Two new capabilities cut video noise, reduce false dispatches, and accelerate issue resolution, without missing real emergencies.

Noonlight, an innovator in intelligent emergency response and professional monitoring solutions, announces two new features for its Verify API which powers its video monitoring solution, designed to help video security providers deliver smarter, more proactive protection to end users.

Two new capabilities to Noonlight’s Verify API cut video noise, reduce false dispatches, and accelerate issue resolution, without missing real emergencies. www.noonlight.com

Verify adds a human verification layer to video-triggered events, enabling trained Noonlight agents to review incidents before emergency response is dispatched. By pairing advanced analytics with professional monitoring, Verify reduces non-actionable video noise and helps agents resolve real threats faster — without increasing operational burden or false dispatches.

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The new Verify features include AI Person Filtering, which automatically screens incoming video and dismisses clips with no person detected, eliminating the cost and time of reviewing empty motion alerts. When an event is escalated to a live monitoring agent, Advanced Verification provides deeper context through extended footage and live camera access, along with the ability to deter a situation in real time before an alarm is ever created. The result: fewer disruptions for end users, fewer unnecessary police dispatches, faster alarm resolution.

“The burden of responding to an emergency should never fall on the end user, and unnecessary disruptions should never affect the people our partners are trying to protect,” said John Tassone, President of Noonlight. “What we’ve built is a smarter way to deliver the right context to agents at exactly the right moment — so they can filter out the noise, act decisively on real situations, and prevent emergencies from escalating wherever possible. That’s what automatic safety looks like in practice.”

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AI Person Filtering

Noonlight uses a proprietary AI model to scan incoming video clips and automatically dismiss events with no person, ensuring that non-actionable footage is never sent to monitoring agents. By scanning all video at hundreds of frames per clip, Noonlight’s AI model detects even brief human appearances, delivering 99% recall and 97% precision — meaning it almost never misses a person and is almost always correct.

For partners already using AI-based filtering, Noonlight’s model delivers incremental noise reduction, while supporting vendor consolidation.

Advanced Verification

Events that require video verification often do not require emergency dispatch. Advanced Verification enhances Noonlight’s workflow by providing agents additional context, including extended footage up to 90 seconds before the event and live camera look-ins enabling them to more quickly identify potential threats.

When appropriate, agents can initiate active deterrence through talkdown via the camera — de-escalating situations before alarms are triggered and emergency response is dispatched.

This deeper context results in faster intervention and a better experience without compromising safety. Early data indicates advanced verification with talkdown reduces total false alarms by 45%. In 37% of cases, the person left on their own and in 8%, agents determined the person was an authorized employee — preventing unnecessary alarms while ensuring real threats are not missed.

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Similarweb Expands Ecommerce and Digital Shelf Analytics with Retail Intelligence Suite

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Similarweb Expands Ecommerce and Digital Shelf Analytics with Retail Intelligence Suite

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Similarweb Retail Intelligence unifies data on shopper behavior with digital shelf dynamics for Amazon marketplaces and 650+ online storefronts

Similarweb launched a new Retail Intelligence suite of retail analysis tools combining Amazon IQ (formerly Similarweb Shopper Intelligence) with Cross-Retail IQ coverage of 650+ online stores and marketplaces. New digital shelf analysis capabilities provide an understanding of product mix, availability, and pricing across stores and marketplaces.

Brands need to understand what shoppers need, where they are, how to improve product visibility, and when to invest in retail media. Retail Intelligence combines shopper behavioral data with digital shelf and pricing signals for a complete view.

The unified view that Similarweb Retail Intelligence creates is more important than ever as the AI-driven revolution continues to shape how consumers discover products and decide whether or where to buy them. Combined with the growth of retail media and marketplace fragmentation, brands and sellers are finding it harder than ever to understand where demand is forming, how competitors are winning, and what actions they can make to improve sales performance. The emerging Universal Commerce Protocol, which allows AI agents to make purchases from online stores, signals yet another transformation that will make the kind of content, stock, availability, and pricing optimization Retail Intelligence enables more important than ever.

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“AI is transforming the industry, but equally significant is how online retailers are reshaping their business models by following Amazon’s lead, operating as marketplaces and launching retail media networks,” said Marta Sulkiewicz, VP, Emerging Solutions at Similarweb. “That means brands need to understand what shoppers need, where they are, how to improve product visibility, and where and when to invest in retail media. Retail Intelligence combines shopper behavioral data with digital shelf and pricing signals to give companies a complete view, helping them turn insight into strategy and execution.”

For more than five years, Similarweb has been helping brands understand how to grow their sales on Amazon while helping retailers compete better in an online world where Amazon is a dominant player. Amazon IQ continues to deliver analysis of the world’s largest online marketplace for the US, the UK, Canada, France, Germany, and Italy.

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Retail Intelligence adds Cross-Retail IQ product, category and brand analytics for more than 650 global retailers. This also includes on-site search keyword optimization to surface what consumers are actively searching for that lead them to specific products.

New digital shelf analytics provide in-depth analysis of product availability and pricing to Cross-Retail IQ that Similarweb previously offered only for Amazon. In addition, both Amazon IQ and Cross-Retail IQ now offer the option of digital shelf automation that brands can employ to automatically reprice products as competitive dynamics change. That kind of automation can be the only way of staying competitive during a price war or retaining a favored “buy box” placement as the top recommended product in a category.

Retail Intelligence provides brands with clear content and pricing recommendations to improve product discovery and sales. Retailers can use the same data to identify gaps in product assortments and in-demand brands, while benchmarking inventory and pricing against the competition and optimizing their own performance.

“Similarweb’s Retail Intelligence acts as our lighthouse. It gives us the clarity to see category shifts across brands, on-site search consumer behavior, and competitor dynamics – all in one view,” said Ben Kaminski, Cofounder and Chairman at Essor, which is in the business of nurturing and growing online businesses.

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Prudent AI Launches Automated Rental Income Analysis, Fully Integrated with Fannie Mae Income Calculator

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Automating the complete rental income workflow — from document intake to submission-ready output — delivering instant, Fannie Mae-aligned calculations in a single console

Prudent AI announced the launch of automated rental income analysis within its Upfront Income platform, natively integrated with Fannie Mae’s Income Calculator. Prudent AI fully automates the end-to-end rental income workflow — from document upload through calculation, validation, and submission-ready output.

“This isn’t just integration with a calculator; it’s the full operationalization of rental income intelligence across the lending lifecycle. Lenders can now capture this opportunity without the operational strain.” – Jayendran GS, Prudent AI

Rental income has long been one of the most operationally demanding areas of mortgage underwriting. Lenders processing investors, multi-property borrowers, and non-W2 income profiles have historically relied on manual reconciliation of Schedule E filings, Form 8825, and property-level records — before manually underwriting or entering figures into spreadsheets or calculators by hand. The process is slow, error-prone, and a leading source of repurchase risk. Prudent AI removes every step of that friction.

The platform automatically extracts rental income data from source documents, submits to Fannie Mae for calculation, validates outputs before they reach the underwriter, and packages results in a submission-ready format for automated underwriting workflows. Calculations meet Fannie Mae’s standards and are eligible for representation and warranty relief.

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Three capabilities that set Prudent AI apart:

  • Complete Workflow Automation: Intake → calculation → validation → submission-ready findings in one seamless system — no spreadsheets, no manual data entry, no rework cycles
  • Universal Rental Income Coverage: Supports Schedule E, Form 8825, multi-property investors, and complex ownership structures with equal precision
  • Instant Compliance Confidence: Pre-emptive validation flags exceptions before they become repurchase risks, creating consistent and auditable income treatment across every file

While other vendors offer partial rental income support through manual workbook integrations, Prudent AI delivers it . The competitive advantage is immediate: Most platforms do extraction, perform calculations, and pass results. Prudent AI closes the loop operationalizing income intelligence across the entire underwriting lifecycle in a single system.

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“Income complexity, especially for rental and self-employed borrowers, has forced lenders to turn away profitable business for too long. We’ve removed that barrier entirely. This isn’t just integration with a calculator; it’s the full operationalization of rental income intelligence across the lending lifecycle. Lenders can now capture this opportunity without the operational strain.”

— Jayendran GS, Co-Founder & CEO, Prudent AI

“Full automation changes everything. Lenders get consistent; Fannie Mae-aligned rental income calculations in seconds — our AI handles the entire complexity every time. The result is faster cycle times, stronger pull-through, and underwriting teams focused on exceptions rather than manual calculation.”

— Srikanth Rajaraman, Co-Founder, Prudent AI

Rental income automation within Upfront Income is available now for all Prudent AI clients. Beyond rental income, Upfront Income delivers liquidity analysis, critical alerts, and agency switch capabilities — giving lenders a comprehensive view of borrower eligibility from the very first interaction. Agency switch enables lenders to seamlessly evaluate loans across Fannie Mae, Freddie Mac, FHA, VA, and USDA guidelines, automatically aligning calculations to investor and agency-specific requirements so the best-fit loan path is identified from the start. The platform integrates seamlessly with existing LOS environments, requiring no workflow disruption. Lenders across retail, wholesale, and correspondent channels can begin capturing the full value of automated income decisioning immediately.

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Persistent Launches AI-Powered Generative Molecules and Virtual Screening Solution Powered by NVIDIA

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Persistent Launches AI-Powered Generative Molecules and Virtual Screening Solution Powered by NVIDIA
Reimagining early-stage drug discovery leveraging NVIDIA BioNeMo Framework

Persistent Systems, a global Digital Engineering and Enterprise Modernization leader, announced it is working with NVIDIA to accelerate the development and deployment of AI-powered solutions for the Healthcare and Life Sciences (HLS) industry. The collaboration will help HLS organizations advance computational drug discovery and improve research outcomes using Generative AI and advanced analytics.

HLS organizations are under increasing pressure to drive innovation while operating in highly complex, regulated and data-intensive environments. Combining Persistent’s deep domain and engineering expertise with the full-stack NVIDIA AI platform, Life Sciences enterprises can move from AI experimentation to real-world production deployments in mission-critical environments.

Persistent will leverage NVIDIA AI Enterprise for specialized Life Sciences R&D use cases, including preclinical research. By enabling high-fidelity molecular simulation and virtual screening at scale, the collaboration applies AI to model and reason real-world biological and chemical behavior before it is realized in real-world wet laboratory environments.

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As part of this collaboration, Persistent will build and deploy production-grade Agentic AI applications for computational drug discovery using NVIDIA NeMo. Specifically, Persistent has created a Generative Molecules and Virtual Screening (GenMoIVS) solution, powered by the NVIDIA BioNeMo platform and NVIDIA NeMo Agent Toolkit. GenMolVS will deliver AI-driven molecular simulations that model the physical and chemical properties of molecules using large domain-specific models and create intelligent agents to streamline real-time drug discovery workflows. These agentic workflows enable continuous decisioning across virtual screening, candidate prioritization, and downstream experimental planning, helping research teams translate digital simulations into informed wet laboratory experiments. This simulation-led approach allows Life Sciences organizations to de-risk early-stage discovery, accelerate experimental cycles and improve downstream success rates in clinical development pipelines.

Persistent is planning to use NVIDIA Nemotron open models for further enhancement of its GenMolVS solution. Furthermore, to deploy production-grade AI applications, Persistent will use NVIDIA accelerated computes, servers, NVIDIA AI Enterprise and NVIDIA NIM microservices. Together, these capabilities will expedite cost-effective development of applications with cost-effective scaling options, with highly accurate AI outputs embedded directly into enterprise workflows. This infrastructure enables production-grade simulation and inference at scale, supporting real-time scientific decisioning in highly regulated research environments.

Through this collaboration, Persistent will also expand its AI and LLM engineering capabilities by leveraging NVIDIA AI infrastructure, training resources and certification programs to deliver increasingly sophisticated data and AI platforms for clients.

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Ganesh Nathella, Executive Vice President and General Manager – HLS Business, Persistent

“Healthcare and Life Sciences organizations need to discover new therapies faster, but traditional R&D is too slow and labor-intensive. By combining our GenMolVS solution with NVIDIA full-stack AI platform, we enable BioPharma clients to use generative molecules and virtual screening in production so they can move from months-long experiments to AI-driven discovery in days, using simulation-led intelligence to guide real-world experimentation without compromising on scientific rigor or compliance.”

John Fanelli, Vice President – Enterprise Software, NVIDIA

“To meet the urgent global demand for new therapies, the healthcare and life sciences industry is rapidly moving toward AI-driven computational research and discovery. By leveraging the full-stack NVIDIA AI platform, Persistent is empowering biopharma companies with production-grade agentic systems for molecular simulation and virtual screening.”

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Smartly Signs Letter of Intent to Acquire INCRMNTAL

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Smartly Signs Letter of Intent to Acquire INCRMNTAL

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Integration will combine creative and media orchestration with always-on incrementality measurement across social, commerce, and CTV

Smartly announced it has entered into a letter of intent to acquire INCRMNTAL, a pioneering AI-powered incrementality measurement platform that delivers real-time insights into the incremental impact of marketing investments across channels without relying on user-level data or tracking.

“With INCRMNTAL, Smartly enables marketers to connect what’s happening in their business outcomes in real time with how they optimize media, creative, and campaigns, so they can see performance as it happens and take immediate action.” Laura Desmond, CEO.

As brands activate across social, commerce, and premium CTV, understanding what actually drives incremental growth has become increasingly complex. By combining INCRMNTAL’s real-time incrementality insights with Smartly’s platform that enables advertisers to turn insights into action across channels, marketers can continuously direct investment to what drives business outcomes. The integration will translate incrementality signals into real-time planning and optimization within Smartly, helping brands and agencies allocate budgets with greater confidence.

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“Marketing leaders are demanding better measurement for performance and accountability,” said Laura Desmond, CEO of Smartly. “Incrementality is becoming increasingly important in a world where traditional approaches are challenged to move at the speed of AI and the changing consumer journey. With INCRMNTAL, Smartly enables marketers to connect what’s happening in their business outcomes in real time with how they optimize media, creative, and campaigns, so they can see performance as it happens and take immediate action.”

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INCRMNTAL’s AI-powered always-on methodology analyzes natural fluctuations in campaign activity instead of forcing marketers to exclude audiences, pause campaigns, or run formal experiments. The solution complements marketers’ existing measurement tools, including marketing mix modeling (MMM) and multi-touch attribution (MTA).

With this acquisition, Smartly will reinforce its position as the platform that connects creative, media, and intelligence, helping marketers orchestrate performance with confidence.

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Appier Releases Whitepaper on the Future of Autonomous Marketing with Agentic AI

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Ava C. Ivy Advances Future of Work with NVIDIA-Recognized AI Platform

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How agentic systems reshape marketing through continuous decision loops and synergies 　

Appier, an AI-native AaaS (Agentic AI as a Service) company, announced the release of its latest whitepaper, “The Future of Autonomous Marketing with Agentic AI.” The report explores how agentic AI is emerging as a new operating layer for modern marketing organizations, shifting AI from reactive assistance toward autonomous planning, execution, and continuous optimization, further reinforcing Appier’s market position as a leader in the Agentic AI field.

From Automation to Autonomous Execution
Marketing is approaching a structural inflection point. This shift is not defined by incremental feature releases or model upgrades, but by a fundamental change in how AI systems operate within organizations.

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As customer journeys become less linear and channel ecosystems grow more complex, marketing teams face a growing “Autonomy Gap”—the imbalance between manual human workflows and the sheer velocity of digital signals. Appier’s whitepaper demonstrates how agentic AI closes this gap by moving beyond simple “if-then” automation. Through continuous data iteration, closed-loop decision cycles, and coordinated execution frameworks, it effectively narrows this autonomy gap.

The report includes a deployment example in which activation timelines were reduced from three days to under one hour — representing up to a 24x improvement in operational velocity in those specific scenarios.

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Beyond Large Language Models: The ‘Pilot’ Steering the AI Engine
The report clarifies the distinction between large language models (LLMs) and agentic AI architectures. While LLMs provide the core reasoning and content generation—acting as the powerful “engine” of the system—they lack the ability to independently execute complex goals or adapt behavior over time.

The evolution toward agentic AI introduces the “pilot” to this engine. By connecting reasoning with a coordinated system of action and learning, agentic AI transforms reactive LLMs into self-directing, adaptive marketing systems. This structural shift restores the “dignity of strategy” to marketers, liberating them from the burdens of manual orchestration and allowing them to focus on high-impact creative generation and strategic planning.

The Agentic Ecosystem: Elevating Strategy over Operations
The whitepaper suggests that the MarTech ecosystem is moving toward connected, autonomous systems that bridge the gap between insight and action. In this model, specialized agents across data intelligence, marketing activation, and conversational commerce work in a “closed-loop” growth engine, allowing real-time signals to move directly into coordinated execution across all touchpoints.

This evolution meaningfully changes how marketing teams allocate their expertise. As agentic systems autonomously handle high-volume operational tasks—such as audience discovery, multi-step test setups, and real-time campaign adjustments—marketers are liberated from manual orchestration. This transition allows teams to pivot toward higher-value responsibilities, including strategic oversight, creative storytelling, and cross-functional governance. In this environment, execution becomes less linear and more adaptive, functioning as a collaborative system aligned to measurable business outcomes.

A New Marketing Operating Model: Toward an Agentic Workforce
Appier views agentic AI not as a short-term trend, but as the foundation of a new marketing operating model. The core challenge for modern brands is no longer just access to data, but the ability to translate that insight into coordinated action at superhuman speed.

The future of marketing is not about adding more tools; it is about building a connected ‘Agentic Workforce’ where intelligence and execution exist in a continuous, self-improving loop. This transition enables organizations to move from reactive assistants to self-driving growth engines, ultimately delivering unprecedented business ROI and operational scale while allowing human talent to focus on high-impact creative decisions.

“The core challenge today is not simply access to data, but the ability to translate insight into coordinated action,” said Chih-Han Yu, CEO and Co-founder of Appier. “As marketing environments grow more complex, embedding autonomy into decision loops enables organizations to respond with greater agility while maintaining strategic oversight. This whitepaper outlines how agentic systems can help teams align execution more closely with business objectives.”

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TrafficGuard Appoints Scott Thomson as Head of AI to Drive Next-Generation Fraud Prevention and Platform Innovation

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Thomson brings over 10 years’ experience in AI technology and strategy from Google, Adobe, and Telstra to one of the industry’s earliest AI-native ad fraud prevention platforms. 

TrafficGuard, a leading platform in AI-powered digital ad verification and invalid traffic (IVT) prevention, has appointed Scott Thomson as Head of AI. Thomson will oversee and accelerate the execution and embedment of AI into TrafficGuard’s platform and processes, building on more than eight years of machine learning innovation that has positioned the company at the forefront of ad fraud detection since its founding. He brings over a decade’s worth of experience in technology and AI innovation, having previously worked for Google, Adobe, and Telstra, as well as founding strategic consultancy firm SCRYPTID.

TrafficGuard was among the first ad verification platforms to deploy machine learning models for real-time invalid traffic detection, processing billions of data points to identify fraud patterns invisible to rules-based systems. That early investment in AI infrastructure now serves as the foundation for the company’s next generation of detection capabilities – engineered to combat an increasingly sophisticated threat landscape.

“We are delighted to welcome Scott in an executive capacity at a pivotal time for TrafficGuard. His deep expertise in AI spanning Google Cloud, generative AI strategy, and enterprise innovation is directly aligned with where we are taking this business,” said Mat Ratty, CEO of TrafficGuard. “Scott’s appointment accelerates our ability to execute our AI roadmap and deliver advanced fraud prevention to advertisers globally. We look forward to the contribution he will make as we enter our next phase of growth.”

Scott Thomson will support TrafficGuard’s expansion of its AI capabilities, ensuring the platform is well positioned to manage ad exposure and fraud risks  as the digital advertising ecosystem undergoes its most significant shift in a decade. He will leveragehis extensive experience in AI strategy and innovation to accelerate product development and commercial execution, bringing to market advanced solutions that monitor, detect, analyse, and respond to invalid traffic, while driving adoption across new verticals and channels.

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Scott Thomson, Head of AI, Traffic Guard said: “Having served as a non-executive director since early 2024 with TrafficGuard, I’m looking forward to adding my experience and guidance more directly to our already significant team. The AI arms race in ad fraud is real. Fraudsters are already deploying agentic bots and generative AI to evade detection at scale, and staying ahead requires deep AI expertise and a platform built for continuous evolution.

The appointment comes at a critical inflection point for the industry. The rapid proliferation of agentic AI – autonomous bots capable of browsing, clicking, filling out forms, and mimicking genuine user behaviour, is fundamentally reshaping the threat landscape for digital advertisers. Unlike traditional bot traffic, agentic bots operate with human-like intent patterns, making them significantly harder to detect using conventional verification methods. Industry analysts project that AI-generated invalid traffic will account for an increasingly material share of digital ad spend waste over the coming years, creating an urgent need for AI-native detection that can evolve at the same pace as the threats it defends against.

“TrafficGuard has a strong foundation in machine learning-driven detection, and I’m here to accelerate what’s next,” said Thompson. “Expanding our AI-powered capabilities beyond fraud detection into intelligent optimisation tools that help advertisers not just protect their spend, but maximise its performance. We’re evolving the platform to cover new channels and deliver actionable insights that turn fraud data into a genuine competitive advantage. Tackling ad fraud has become increasingly essential for advertisers, and I can’t wait to be part of the talented team building the future of ad verification.”

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This strategic appointment will further strengthen TrafficGuard’s commitment to driving innovation in ad fraud prevention. It supports TrafficGuard’s wider global channel growth strategy, following its recent expansion in the United States and the appointment of CPO Miguel Lopes. The company plans to significantly expand its team, enabling brands to boost their revenue and confidently scale advertising campaigns by eliminating invalid traffic. As brands allocate record budgets to digital channels, TrafficGuard’s mission to ensure every ad dollar reaches a real human has never been more commercially critical.

TrafficGuard, a flagship product of Adveritas Ltd (ASX:AV1), is a pioneering force in advertising technology, delivering AI-driven solutions that revolutionise digital ad fraud prevention and performance optimisation. Leveraging advanced machine learning, artificial intelligence, and big data, TrafficGuard empowers businesses to combat invalid traffic and ad fraud, protect advertising budgets, and enhance campaign efficiency, driving measurable return on investment (ROI). Positioned at the forefront of the rapidly growing ad tech market, TrafficGuard’s cutting-edge software has been recognised by prestigious industry awards, including The Drum, Martech Breakthrough Awards, and Mobile Marketing. Dedicated to setting new standards in transparency and security, TrafficGuard is shaping the future of intelligent, data-driven digital advertising.

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Over The Top SEO Launches Dedicated Generative Engine Optimization Division, Becoming One of the First Agencies to Offer Full-Service AI Search Optimization

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Ace SEO Consulting Strengthens Calgary Digital Growth with Advanced SEO, AEO, and GEO Strategies

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As AI-generated answers replace traditional search results, Guy Sheetrit says the window for establishing brand authority in AI systems is measured in months, not years.

Guy Sheetrit, CEO and Founder of Over The Top SEO, announced the formal launch of the agency’s dedicated Generative Engine Optimization (GEO) division — a purpose-built practice designed to help enterprise brands, ecommerce companies, and professional services firms establish and defend their visibility inside AI-powered search platforms including ChatGPT, Google Gemini, and Perplexity AI.

The new division represents one of the industry’s first full-service GEO offerings, combining AI citation optimization, entity authority building, structured data architecture, and ongoing AI visibility monitoring into a single managed service.

AI search platforms are experiencing explosive growth. Google Gemini’s user base is expanding 12 percent quarter-over-quarter. Perplexity has captured nearly six percent market share in under two years. ChatGPT has added 400 million weekly active users in twelve months. Across these platforms, roughly one in three queries is commercial.

The critical difference: traditional search presents a page of links. AI search names specific brands inside a generated answer — often before the user ever sees a link. For brands that aren’t named, the result is functional invisibility.

“If ChatGPT doesn’t mention your brand when someone asks about your industry, you effectively don’t exist for that person. And every month you wait, a competitor is building the citation history and topical authority that will make them the default recommendation. This is the same inflection point we saw with traditional SEO twenty years ago — except the timeline is compressed from years to months.”

— Guy Sheetrit, CEO & Founder, Over The Top SEO

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The Compounding Citation Advantage

OTT’s internal research indicates that brands establishing a Generative Engine Optimization presence in 2026 will hold a three-to-five-times citation advantage over competitors who enter in 2027. The gap compounds because of how AI models learn: brands that are cited frequently become more visible to the model, which leads to more citations, which further cements their authority in the model’s knowledge base. Late entrants must overcome not just their own absence, but the entrenched presence of competitors who moved first.

The parallel to early search engine optimization is instructive. In the early 2000s, the brands that invested in SEO first captured dominant positions that took competitors years to challenge. The difference with Answer Engine Optimization and GEO is speed — what took years in SEO will take months in the AI era.

Understanding the SEO-to-GEO Shift

GEO is not a replacement for traditional SEO. It is a parallel discipline addressing a different discovery mechanism entirely. OTT has published an in-depth analysis of the differences in its GEO vs Traditional SEO guide.

In traditional SEO, success means ranking on a page of links and earning a click. In GEO, success means being the brand named inside an AI-generated answer.

The ranking signals are fundamentally different. Traditional SEO relies on backlinks, keyword placement, page speed, and user experience. GEO relies on topical authority, structured data, AI citation patterns, and content architecture that large language models can reliably interpret. The competitive dynamics differ too: traditional SEO rankings shift with every algorithm update, while GEO citation authority compounds over time as AI models retrain on updated data.

OTT’s position is that brands need both — and that agencies with deep SEO expertise are best positioned to deliver GEO, because the technical foundations overlap significantly.

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Who Is Most at Risk

Enterprise and Fortune 500 organizations risk being displaced by AI-native competitors. For these brands, GEO is a defensive strategy as much as a growth strategy — protecting brand equity from erosion in AI-generated recommendations.

High-growth ecommerce brands competing for AI-powered purchasing decisions. When a consumer asks an AI assistant for the best product in a category, GEO determines whether their brand appears in the answer.

Professional services and SaaS companies where a single AI citation can generate significant pipeline value.

“We’ve been doing this longer than almost anyone, and we’ve never seen a shift this significant. The move from ten blue links to AI-generated answers changes everything about how brands get discovered. Everyone else is still writing the blog post. We already built the division.”

— Guy Sheetrit

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TruGen AI Launches Enterprise AI Teammates, Redefining How Organizations Work at Scale

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TruGen AI Launches Enterprise AI Teammates, Redefining How Organizations Work at Scale

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AI teammates with face, voice, vision, and persistent memory built for enterprises to enable human-like collaboration, deep workflows, and organizational memory

TruGen AI, an enterprise AI platform company, announced the general availability of its AI Teammates technology, a new category of enterprise AI that goes far beyond copilots, chatbots, and task-based agents and emerging video agents. TruGen AI Teammates are built to work like real people: joining live calls, executing complex workflows, collaborating across teams, and retaining institutional knowledge that grows more valuable over time.

The future of enterprise AI isn’t another tool or copilot. It’s AI teammates that join meetings, collaborate with teams, execute work in real systems, and learn how the organization operates.”

— Hemanth Kumar, CEO, TruGen AI

The announcement marks a pivotal shift in how enterprises think about artificial intelligence in the workplace. Where conventional AI tools offer suggestions and surface-level assistance, TruGen Teammates take action, operating as active participants within an organization’s existing processes, tools, and teams. Unlike standalone assistants or video agents designed primarily for conversational interaction, TruGen Teammates actively participate in real enterprise workflows.

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What Makes AI Teammates Different

TruGen AI Teammates are equipped with capabilities that set them apart from any AI product currently on the market:

1. Face, Voice, and Vision – Teammates participate in live video calls, read visual context, and communicate naturally with human team members,combining the conversational capabilities of video agents with real workflow execution.
2.Persistent Memory – Unlike stateless AI tools, TruGen Teammates retain organizational knowledge across every interaction, building an institutional memory that compounds in value over time.
3. Workflow-Level Integration – Teammates embed directly into existing enterprise processes, executing tasks end-to-end rather than assisting from the sidelines.
4. Measurable Productivity Lift – Designed to deliver quantifiable results at the squad level, across sales, operations, HR, finance, and more.

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A New Category of Enterprise AI
TruGen AI Teammates are not an upgrade to existing AI tools – they represent an entirely new category. Where productivity software automates tasks and copilots draft responses, and video agents enable conversational interactions through avatars or live video interfaces, TruGen Teammates execute complete workflows from start to finish, across the functions that matter most to enterprise operations:

1. Sales – Conducting live product demos, qualifying prospects, sharing Slides/documents and booking meetings in real time.
2. Human Resources – Screening and interviewing candidates at scale, without sacrificing quality.
3. Customer Success – Onboarding new accounts, filling forms and providing consistent, knowledgeable support.
4. Engineering – Autonomously completing coding tasks such as writing features, fixing bugs, generating tests, reviewing pull requests, and shipping production-ready code.

Building Organizational General Intelligence :
At the core of TruGen’s platform is a proprietary Organizational Memory Graph – a continuously growing intelligence layer that captures how a business actually operates. Over time, TruGen Teammates learn an organization’s unique workflows, terminology, relationships, and institutional knowledge, becoming increasingly embedded and indispensable.
TruGen calls this Organizational General Intelligence: AI that doesn’t just perform tasks, but understands the context, culture, and complexity of the organization it works within. This approach extends beyond traditional automation or video agents, enabling AI systems that both communicate naturally and execute meaningful work.

Enterprise-Grade Security and Governance :
TruGen is built for the governance and compliance requirements that enterprise IT, legal, and security teams demand. Key platform capabilities include:

1. VPC Deployment – Full private cloud deployment within the customer’s own secure AWS environment.
2. Governance-First Architecture – Role-based access control, approval gates, and complete action traceability across every Teammate interaction.
3. Complete Data Protection – Customer data never leaves the controlled environment.
4. Compliance Certifications – SOC 2, HIPAA, ISO, and GDPR certified.

Positioning Within the Emerging AI Workforce Landscape :
The emerging AI workforce ecosystem includes several approaches to automating work with AI. Platforms such as Stack AI, Cognition AI (Devin), Tensol and other focus on autonomous Enterprise agents capable of executing tasks. In parallel, companies including Tavus, Anam AI, Synthesia, Beyond Presence, D-ID and HeyGen focus on conversational avatars, and AI-generated video interactions.
TruGen AI represents the next evolution of this landscape. Rather than focusing solely on assistive tools, autonomous agents, or standalone video agents or visual avatars, TruGen is building enterprise AI teammates – AI systems that communicate naturally while participating directly in organizational workflows, executing tasks across enterprise systems, and accumulating institutional knowledge through an evolving Organizational Memory Graph.

A Shift Toward Human–AI Collaboration  :
Industry analysts increasingly view that AI is transitioning from automation tools to collaborative systems embedded within organizational workflows. TruGen.ai presents AI teammates represent a foundational step toward scalable digital workforces where AI operates alongside human teams as functional contributors.

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LinkSpree Launches an AI Accountability Engine to Turn Link Dashboards Into Living Systems

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LinkSpree Launches an AI Accountability Engine to Turn Link Dashboards Into Living Systems

LinkSpree

The visual link operating system now adds AI context, smart reminders, and an accountability layer that turns saved links into actions, not just a list.

LinkSpree, the visual link operating system for power users, agencies, and creators, announced the launch of its AI Accountability Engine — a major platform update that transforms passive link dashboards into intelligent, goal-aware systems that actively help users act on the information they collect online.

We’re not building a bookmark manager. We’re building the operating system for how people interact with the internet, one that understands your goals and holds you accountable to them.”

— Ignacio Guzman

The update introduces a fundamental new capability: AI Context. For the first time, users can attach natural-language intent to any saved link or entire dashboard — explaining not just what a link is, but why it matters and when it needs to be acted on.

Examples of AI Context in practice:

— “Check the weather one week before my trip on December 1.”
— “Review this Salesforce report every morning before 12 PM.”
— “Use this tutorial to finish learning Python.”

LinkSpree’s AI engine analyzes this context and takes action: sending contextual reminders, surfacing high-priority links, adjusting link urgency, elevating critical resources into higher visibility positions, and asking accountability questions when important links are ignored. If a user saves a link without explaining why, the AI may prompt: “Why did you save this?”

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From Bookmark Manager to Internet Operating Layer

“Most link tools are passive — they hold information but never help you do anything with it,” said Ignacio, founder of LinkSpree. “The AI Accountability Engine changes that. Your dashboard now understands your goals and your deadlines. It notices when you’re falling behind and helps you get back on track. We’re not building a bookmark manager. We’re building the operating system for how people interact with the internet.”

Three Behavior Modes and Personalised AI Personalities

Users control exactly how proactive the AI should be through three behavior modes. In Suggest Mode, the AI recommends improvements without making changes. In Hybrid Mode, the AI executes low-risk adjustments automatically while flagging bigger decisions. In Auto Mode, the AI actively reorganises the dashboard to keep it aligned with the user’s intentions and timeline.

To make the experience feel human rather than robotic, users can also choose between AI personality styles: Assistant, Coach, or Strict Accountability Partner, each with a distinct tone and level of directness. All AI interactions appear as in-dashboard popups, keeping the experience contextual and non-intrusive.

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Designed Around Real Behavior

The AI engine reviews dashboards on login and periodically throughout the day, scanning for patterns such as missed deadlines, unused links, and approaching tasks. The system is built on the insight that the gap between saving information and acting on it is where productivity collapses, and that a visual link dashboard is uniquely positioned to close that gap.

Platform Foundation: The Visual Link Dashboard

The AI Accountability Engine builds on LinkSpree’s existing platform, which already offers visual link dashboards with colour-coded cards, one-click shareable link hubs, white-label portals for agencies, team workspaces with role-based permissions, and link reminders. The platform is used for employee onboarding, resource hubs, client link portals, private knowledge bases, and creator content hubs, use cases previously served by static Notion pages, scattered Slack threads, or generic bookmark tools.

LinkSpree is positioned as a direct alternative for the millions of users displaced by Mozilla’s shutdown of Pocket in July 2025, offering a shareable, team-ready, and now AI-powered environment that far exceeds what passive bookmark managers provided.

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Miro Announces Asia Hub in Singapore to Accelerate Growth Across the Region and Bring AI Collaboration to New Markets

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Miro Announces Asia Hub in Singapore to Accelerate Growth Across the Region and Bring AI Collaboration to New Markets

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AI Innovation Workspace perfectly placed to help organisations maximise AI investment and accelerate innovation

Miro®, the AI Innovation Workspace for teams, announced plans to expand its operations in Asia, supporting organisations across the region in their AI transformation journey. Miro is investing in people, resources, and infrastructure as it targets growth in key markets, including Singapore, India, South Korea, and other Southeast Asia countries.

“The opportunity to grow our customer base across Asia is significant. Our investment in Singapore is part of a lasting commitment to customers, partners, and our wider ecosystem across the region.” Brigid Archibald, Head of JAPAC at Miro.

As the global innovation centre of gravity shifts toward Asia – where R&D spending reached 45% of global investment in 2024 – the organisations leading this charge need tools and platforms built for the complexity and pace of modern innovation and collaboration. Miro’s AI-powered innovation workspace is uniquely positioned to support this moment. Miro gives organisations the shared context layer they need to move from insight to execution faster than ever before. For Asia’s most ambitious innovators, where speed-to-market and cross-border collaboration are existential priorities, Miro provides the link between human creativity and AI capability.

At the heart of Miro’s expansion strategy is a new Asia hub located in Singapore. This hub will serve both the Singapore domestic market and provide a launchpad into neighbouring countries. The move strengthens Miro’s ability to support its existing customers, reach new customers, and continue to build an ecosystem with regional partners, including AWS, Vsaas Global, GoPomelo, Altudo, and others.

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“Singapore is a natural choice as a location to base our Asia operations,” said Sunil Pamnani, Head of Asia Sales at Miro. “This is a place where organisations, and government institutions alike understand the need for transformation – not just adoption. They value long-term thinking, disciplined execution, and technology that delivers real outcomes. That mindset is exactly what’s needed to reimagine how teams and AI work together.”

“The opportunity to grow our customer base across Asia is significant,” said Brigid Archibald, Head of JAPAC at Miro. “Our investment in Singapore is part of a lasting commitment to customers, partners, and our wider ecosystem across the region. Organisations are at a critical moment where they need to deliver on their AI investments and move from experimentation to integration. Miro is helping leaders to achieve this.”

Globally, Miro has 100M+ users and more than 250,000 customers. A significant number of these customers are based in countries across the region – and they are already using Miro and realising the benefits of embedding Miro into their workflows and critical operations. These include TCS Pace (operated by Tata Consultancy Services) and Frasers Property, which are using Miro to reduce time to market for product development lifecycle, improve the quality of ideas, and redefine their innovation processes.

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“With Miro AI, we can use intelligent prompts to challenge assumptions, test ideas, and explore new perspectives,” said Subin Pillai, Product Manager and Studio Lead at TCS Pace. “Miro Sidekicks acts like any other team member, helping validate use cases, suggest improvements, and simulate real-world scenarios. I could prompt it to take on different personas, to challenge our assumptions, to offer perspectives that broke through our mental debt. Suddenly, we weren’t just facilitating a workshop. We were orchestrating a symphony of human and artificial intelligence. The impact is 50% faster innovation cycles with working prototypes in 90 minutes.”

“Miro has saved us time, reduced costs, and made innovation more accessible,” said Iris Tan, Senior Manager, Strategic Innovation at Frasers Property. “Our senior leaders and global participants now use it to structure ideas and drive strategic decisions faster than ever before. We’ve moved away from simply building spaces to truly understanding what our tenants and their customers need. Design thinking is the foundation of that shift, and Miro allows us to embed it across our entire organisation.”

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Tells.co Launches AI Video Messaging Platform with RCS Business Messaging for Personalized Video at Scale

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NemoVideo Brings Viral Video Intelligence into the Editing Workflow with Viral+ Studio

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AI-powered platform delivers personalized video through RCS rich messaging

Tells.co announced the launch of its AI video messaging platform, combining AI-generated personalized video with RCS Business Messaging to deliver custom video content directly to consumers’ native messaging apps. The platform represents a first-of-its-kind integration of AI video generation and RCS rich messaging at enterprise scale.

We’re combining AI-generated personalized video with RCS Business Messaging to create the most compelling customer communication channel that exists.”

— David Schlaegel, Co-Founder, Tells

AI Video Meets RCS Business Messaging

The Tells.co platform uses artificial intelligence to generate unique, personalized videos for each recipient on a campaign list. These AI videos are then delivered via RCS Business Messaging with inline playback — meaning recipients watch personalized video content directly in their messaging app without clicking links, downloading apps, or leaving the conversation.

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“We built this because the future of business messaging isn’t text — it’s personalized AI video delivered through RCS,” said David Schlaegel, CEO of Tells.co. “Every recipient gets a video made specifically for them, playing right in their messages with our verified sender profile. Nothing else on the market combines AI video personalization with RCS delivery at this scale.”

How AI Video Personalization Works

The AI video engine processes customer data — names, addresses, vehicle information, appointment history, property details — and generates a completely unique video for every individual recipient. Each AI-generated video features natural voice synthesis, dynamic visuals tailored to the recipient’s data, and personalized storylines that speak directly to the viewer’s situation.

The platform renders thousands of personalized AI videos in minutes, enabling campaigns of 10,000+ recipients where every video is unique. Combined with RCS verified sender profiles displaying brand logos and verification badges, the result is a trusted, high-engagement messaging experience.

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RCS Video Driving Results Across Industries

Tells.co is deploying AI video through RCS across multiple verticals including real estate, automotive, and healthcare. Use cases include personalized home appraisal videos that reference specific property data and neighborhood sales, service reminder videos for auto dealerships featuring individual vehicle details, and healthcare follow-up videos tailored to patient treatment history.

Early campaigns combining AI video with RCS delivery are showing conversion rates significantly above traditional SMS and email benchmarks, driven by the combination of personalized video content, inline RCS playback, and verified brand trust signals.

First US Platform Approved for RCS Business Messaging

Tells.co is among the first platforms in the United States approved for RCS Business Messaging, building RCS capabilities into its core infrastructure rather than adding them as a supplementary feature. The company’s AI video messaging solution leverages this native RCS integration to deliver rich video experiences at scale with full analytics — including view rates, watch duration, and CTA engagement tracking.

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Cisco Secure AI Factory with NVIDIA Makes AI Easier to Deploy and Secure, Anywhere Organizations Need It Cisco logo

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Cisco Secure AI Factory with NVIDIA Makes AI Easier to Deploy and Secure, Anywhere Organizations Need It Cisco logo

Expanded architecture lets businesses run AI at scale, from central data centers to the factory floor, without sacrificing performance or security

  • Cisco expands its Secure AI Factory with NVIDIA to work not just in large data centers, but at local edge sites where real-time decisions can’t wait, from hospitals and warehouses to moving vehicles.

  • Cisco is the premier partner to deliver partner-developed systems featuring NVIDIA Spectrum-X switch silicon paired with a Cisco operating system, providing customers the flexibility of leveraging both NVIDIA Cloud Partner-compliant reference architectures and Cisco Silicon One-based architectures.

  • Cisco adds deeper security capabilities to its reference architecture by extending Hybrid Mesh Firewall policy enforcement to NVIDIA BlueField DPUs and integrating Cisco AI Defense to secure multi-agent systems.

  • Cisco AI Defense will support and secure NVIDIA’s new open agent development platform, OpenShell, adding controls and guardrails to govern agent and claw actions.

Cisco announced a major expansion of its Secure AI Factory with NVIDIA, giving customers a framework for deploying AI across their entire infrastructure – from central data center to local sites where data is created and decisions are made.  Enterprises, neoclouds, sovereign clouds, and service providers can now move AI from pilot to full-scale production without stitching together disconnected systems, compressing deployment timelines from months to weeks and embedding security from the start.

“Most organizations understand the potential for AI to transform their businesses, but they’re navigating how to deploy the technology safely and at scale,” said Chuck Robbins, Chair and CEO, Cisco. “In partnership with NVIDIA, we’re solving that challenge with an architecture that sets a new standard for performance – making it simpler to deploy, operate, and secure AI infrastructure.”

“AI factories are transforming every industry, and security must be built into every layer—from silicon to software—to protect data, applications, and infrastructure,” said Jensen Huang, founder and CEO of NVIDIA. “Together, NVIDIA and Cisco are building the secure foundation for AI infrastructure—core to edge—so companies can scale intelligence with confidence.”

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AI That Runs Everywhere, Not Just in the Data Center

AI inference happens where data lives and decisions can’t wait, whether on the hospital floor or for analyzing video of a factory floor in real-time to keep workers safe. This reality fundamentally reshapes infrastructure by requiring inference workloads to operate locally — closer to the data, the devices, and the moment a decision must be made. Cisco and NVIDIA are enabling organizations to support edge inferencing use cases by:

  • Transforming the Enterprise Edge: Now supporting NVIDIA RTX PRO™ 4500 Blackwell Server Edition GPUs across the Cisco UCS and Cisco Unified Edge portfolios, Cisco enables enterprises to run mission-critical AI workloads at the edge without the energy cost and footprint of data center-scale hardware.
  • Transforming the Service Provider Edge: Today Cisco announces the Cisco AI Grid with NVIDIA reference design that combines the power of Cisco’s Mobility Services Platform with NVIDIA RTX PRO Blackwell Series GPUs. This enables service providers to leverage their existing networks to offer managed services for edge AI applications with carrier-grade reliability and sovereignty.

Driving Performance and Efficiency for Massive-Scale AI Factories

Building on the momentum of the recently launched systems powered by Cisco Silicon One G300 for scale-out and P200 for scale-across, Cisco continues to raise the performance ceiling while making the whole process faster and simpler.

  • Next-Generation Performance: Cisco’s latest high-speed switches power the most demanding AI workloads, including a new 102.4Tbps Cisco N9100 powered by NVIDIA Spectrum-6 Ethernet switch silicon. This joins the now generally available 800G N9100 powered by NVIDIA Spectrum-4 Ethernet switch silicon.
  • Rapid Deployment: Cisco Nexus Hyperfabric, now a part of Cisco Nexus One, will support Cisco N9000 Series switches, including the N9100 Series powered by NVIDIA Spectrum-X Ethernet silicon. Now organizations can transform a complex, multi-vendor integration puzzle into a simple, full-stack solution to cut deployment times and reduce the burden on IT.

Customers building large AI factories now have two validated paths to choose from: an AI factory based on a reference architecture compliant with the NVIDIA Cloud Partner (NCP) program, and a Cisco Cloud Reference Architecture built on Cisco Silicon One that adheres to the same design tenets.

Security Fused into Every Layer

In an era where AI models are high-value assets and agents are more autonomous, taking actions, making decisions and interacting with other agents – security can’t be an afterthought. Cisco is embedding protection into the fabric of the Secure AI Factory with NVIDIA to safeguard against both external threats and rogue agent behavior, including:

  • Securing AI infrastructure: AI is only as safe as the hardware running it – and attackers know it. Cisco Hybrid Mesh Firewall delivers consistent security policies across a diverse set of enforcement points: network switches, workload agents, and more. Greater coverage means fewer gaps for attackers to exploit. Today, Cisco is extending the Cisco Hybrid Mesh Firewall solution to enable policy enforcement on NVIDIA BlueField data processing units (DPUs) embedded in NVIDIA GPU servers connected to Cisco Nexus One fabrics. Threats are blocked at the server level before they ever reach an organization’s data.  The result: AI workloads that can be protected from the inside out, with zero performance trade-off.
  • Securing AI agents: Cisco AI Defense delivers model security, automated vulnerability testing, and now purpose-built guardrails for AI agents at the edge through integration with NVIDIA NeMo Guardrails, a part of NVIDIA AI Enterprise software. This helps AI developers and security teams stay ahead of emerging threats and maintain trust in AI. AI deployments are becoming increasingly distributed, with agents at edge locations often interacting with those at the core to accomplish tasks and execute workflows. AI Defense, as a part of the Cisco Secure AI Factory with NVIDIA, now extends to securing those agent-to-agent interactions.

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Cisco Secures Enterprise AI Agent Development

Building on Cisco’s commitment to fuse security into all layers of AI infrastructure, as well as the agentic workforce, Cisco also announced today that Cisco AI Defense will support and secure NVIDIA’s OpenShell runtimes – part of the NVIDIA Agent Toolkit – adding controls and guardrails to govern agent and claw actions. By continuously monitoring and validating every tool and action an agent performs, Cisco AI Defense ensures that enterprises can confidently deploy AI agents to manage critical workflows without compromising security. This integration bridges the gap between innovation and risk, allowing organizations to trust their autonomous systems to operate reliably and securely.

Industry Reactions:
“As a leader in high-performance computing solutions, Cirrascale is thrilled by the introduction of new NVIDIA Spectrum-6 based Cisco’s N9100 series switches, extending Cisco’s NCP reference architecture-compliant portfolio with an impressive 102.4T capacity and a unified management plane through Nexus One. These innovations, combined with the flexibility of NX-OS and SONiC, enable us to scale our AI infrastructure seamlessly while maintaining operational simplicity. The availability of the 51.2T Spectrum-4 switch further enhances our ability to deliver cutting-edge AI solutions to our clients with unmatched performance and reliability.”
– Alex Nataros, CTO, Cirrascale Cloud Services

“Sharon AI looks forward to the Cisco’s N9100 series switches, offering 102.4T capacity with Nexus One’s cloud-managed Nexus Hyperfabric. With NCP RA compliance and the 51.2T Spectrum-4 based N9100 switch availability, we will be scaling our AI infrastructure with robust performance and efficiency. The G300 Silicon One-based N9300 switches provide the flexibility to meet evolving customer needs. Turnkey AI infrastructure deployment through Nexus One significantly simplifies operations and accelerates time-to-value for our initiatives.”
– Andrew Leece, COO and founder, Sharon AI

“World Wide Technology’s clients trust Cisco for enterprise networking. Their robust AI networking portfolio extends that trust to AI workloads. Cisco’s portfolio offers choice and flexibility to clients to build tailored AI infrastructure using Cisco Silicon One and NVIDIA Spectrum-X Ethernet switch silicon based switches with stellar performance up to 102.4Tbps running NX-OS or SONiC and unified by the Nexus One management plane. We’re excited about these advancements to deliver the scalability and performance required for the agentic era.”
– Jeff Fonke, Practice Director – Global Solutions & Architecture, World Wide Technology

“As organizations move beyond the experimentation phase of AI, the primary challenge has shifted from ‘what can AI do’ to ‘how do we operationalize it securely at scale.’ The industry is at a critical inflection point where AI workloads — specifically real-time inferencing —must move closer to the data at the edge without creating new security or infrastructure silos. The partnership between Cisco and NVIDIA is designed to offer customers the flexibility and choice they need to scale while helping them overcome complex integration challenges.”
 – Mary Johnston Turner, Global Lead, Digital and Datacenter Infrastructure and Services, IDC

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IBM Announces Expanded Collaboration with NVIDIA to Advance AI for the Enterprise

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IBM Announces Expanded Collaboration with NVIDIA to Advance AI for the Enterprise

Advancements across GPU-native data analytics, unstructured data extraction, on-premises and cloud infrastructure, Nestlé global supply chain decision speed, and consulting to mobilize enterprise AI at scale

IBM announced at GTC 2026 an expanded collaboration with NVIDIA to help enterprises operationalize AI at scale. Advancing efforts across GPU-native data analytics, intelligent document processing, on-premises and regulated infrastructure deployments, cloud, and consulting, the collaboration aims to give enterprises the data foundation, infrastructure, and expertise to move AI from pilot to production.

Enterprises are making significant investments in AI, but too many remain stuck between experimentation and production at scale. The barriers are consistent: data is fragmented and difficult to access; infrastructure wasn’t built for advanced AI workloads; AI deployments don’t support the compliance and residency requirements of regulated industries; and many organizations still need the guided expertise to implement and deploy the technologies.  Today’s announcements from IBM and NVIDIA are designed to close these gaps.

“In the next wave of enterprise AI, the model layer will rely on the data, infrastructure, and orchestration layers – and on businesses that can bring all three together,” said Arvind Krishna, Chairman and CEO, IBM. “Our partnership with NVIDIA goes to the heart of that challenge. Together, we’re giving enterprises the solutions they need to stop experimenting with AI and start running on it.”

“IBM pioneered enterprise computing and data processing six decades ago — and today they are redefining it for the AI era,” said Jensen Huang, founder and CEO of NVIDIA. “Data is the ground truth that gives AI context and meaning. Together with IBM, we are bringing CUDA GPU acceleration directly into the data layer — turning analytics and document processing from bottlenecks into real-time intelligence engines.”

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Accelerating Structured Data Analytics with GPU-Native Computing
IBM and NVIDIA are collaborating on an open-source integration to increase performance and reduce costs around how enterprises extract intelligence from their massive datasets. IBM watsonx.data’s SQL engine Presto is accelerated by NVIDIA cuDF to enable faster query execution on large datasets.

To validate in production, IBM and NVIDIA applied GPU-accelerated watsonx.data to Nestlé’s Order-to-Cash data mart. The data mart tracks every order, fulfillment, delivery, and invoice across 186 countries and processes terabytes across 44 tables. Nestlé was ideal for this proof of concept because of its strong digital backbone. With globally unified data models, a consolidated data foundation, and a single source of truth across markets, Nestlé already had timely, accurate, and trusted data at scale — the right foundation to put GPU-accelerated analytics to the test in a real production environment.

On CPUs, a single refresh previously took Nestlé 15 minutes and only ran a handful of times a day. Nestlé reports that with NVIDIA’s software and GPUs, the IBM watsonx.data Presto engine reduced query runtime down to three minutes – achieving 83% cost savings and an overall 30X price-performance improvement.

“For a company that serves billions, data underpins decision making across our global operations,” said Chris Wright, Chief Information and Digital Officer of Nestlé. “Working with IBM and NVIDIA, a targeted proof of concept has demonstrated the ability to refresh global operations data in a few minutes and at reduced cost. Our focus now is on turning this capability into tangible business impact — further improving decision speed in areas such as manufacturing and warehousing, and scaling these capabilities across our enterprise.”

Helping Enterprises Unlock the Full Value of Their Data
Most enterprises aren’t lacking data. But often, they’re unable to access and use it. SharePoint sites, CMS systems, vendor research, SME knowledge: the information exists but it is trapped in unstructured, multi-modal formats that are difficult to extract, standardize, and trust at decision speed.

IBM and NVIDIA are addressing this with Docling from IBM and NVIDIA Nemotron open models – a combination designed to make intelligent document extraction available at enterprise scale. Docling standardizes and converts documents into AI-ready formats with source-level traceability, while NVIDIA Nemotron models accelerate ingestion of multi-modal content. Early results show significantly higher throughput compared to other open-source models, while maintaining or improving accuracy wherever GPU-accelerated infrastructure is available.

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GPU-Optimized Infrastructure for On-Prem and Regulated Deployments
IBM and NVIDIA are extending their data efforts to the infrastructure layer. NVIDIA has selected IBM Storage Scale System 6000 to provide 10PB of high-performance storage to serve massive data for its GPU-native advanced analytics engines, pairing IBM’s unified data access layer and massive parallel throughput with NVIDIA’s GPU pipelines. IBM Storage Scale 6000 is certified and validated on NVIDIA DGX platforms.1

For enterprises and governments requiring data residency and regulatory control, IBM and NVIDIA are exploring the integration of IBM Sovereign Core and NVIDIA infrastructure and NVIDIA Nemotron models that would focus on enabling GPU-intensive AI workloads that run entirely within regional boundaries – without compromising governance or compliance.

Advancing the Enterprise AI Stack with IBM, NVIDIA and Red Hat
IBM and NVIDIA are also deepening their partnership across cloud and enterprise consulting to advance clients’ enterprise AI adoption. IBM plans to offer NVIDIA Blackwell Ultra GPUs on IBM Cloud in early Q2 2026 for large-scale training, high-throughput inferencing, and AI reasoning. This technology will also be integrated across Red Hat AI Factory with NVIDIA, and VPC servers with enterprise-grade compliance and data residency controls.

Additionally, IBM Consulting plans to bring Red Hat AI Factory with NVIDIA to clients through IBM Consulting Advantage – an IBM enterprise AI platform that helps clients build and scale AI across their technology environments. Combined with Red Hat AI Factory with NVIDIA, the platform is built to simplify how companies prepare data, build models, and deploy AI, while also enhancing performance and oversight. This builds on IBM Consulting’s broader efforts to help clients maximize outputs from their AI investments.

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Hitachi Vantara Expands Hitachi iQ Capabilities to Help Enterprises Advance Responsible Agentic AI

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Hitachi Vantara Expands Hitachi iQ Capabilities to Help Enterprises Advance Responsible Agentic AI

Expanded AI blueprints, infrastructure capabilities and intelligent data integration strengthen the Hitachi iQ portfolio for secure, on-prem production AI

Hitachi Vantara, the data storage, infrastructure and hybrid cloud management subsidiary of Hitachi Ltd., announced new capabilities across the Hitachi iQ portfolio, including enhanced AI blueprints and multi-agent coordination in Hitachi iQ Studio, expanded NVIDIA AI infrastructure options, and deeper data integration to support agentic AI in on-premises and virtualized environments. Together, these enhancements position Hitachi iQ as a comprehensive, enterprise-ready AI solution, enabling customers to build and manage AI agents within their own environments.

As organizations move from AI experimentation to scaled deployment, many are facing growing challenges tied to data complexity, AI sovereignty and evolving governance and security requirements. According to a recent report, in the U.S. and Canada, only 42% of organizations are considered data-mature, and 84% of those organizations report measurable AI ROI, compared with just 48% of organizations with weaker data foundations. As AI moves into production, the ability to pair strong data practices with secure, well-governed infrastructure is becoming a critical differentiator. The Hitachi iQ portfolio is designed to help close that gap by bringing together AI-ready infrastructure, integrated agent capabilities and enterprise-grade oversight and compliance controls designed for responsible enterprise AI deployments.

“AI is moving into production faster than many organizations’ data foundations are ready to support,” said Octavian Tanase, chief product officer, Hitachi Vantara. “With these latest enhancements to the Hitachi iQ portfolio, we are expanding across software innovation, high-performance infrastructure and intelligent data integration to give customers greater flexibility and control as they move agentic AI from pilot to production.”

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New Accelerated Computing Options for Modern AI Workloads
Hitachi iQ is designed to help enterprises deploy and operate AI infrastructure with predictable performance and reliability, built on Hitachi Vantara’s Virtual Storage Platform One (VSP One) data platform and supporting HMAX by Hitachi, a suite of next-generation solutions that brings the power of AI to social infrastructure. Hitachi iQ now supports NVIDIA Blackwell GPUs (air-cooled), NVIDIA Blackwell Ultra GPUs (air-cooled and liquid-cooled) and a 2U NVIDIA MGX-based system with up to four NVIDIA RTX PRO™6000 Blackwell Server Edition GPUs. Hitachi iQ also plans to support the newly announced NVIDIA RTX PRO™ 4500 Blackwell Server Edition GPU. These options give customers greater flexibility to align compute with their AI workloads – from model development and fine-tuning to inference and agentic applications – while supporting diverse form factors that address cooling, power and space constraints and meet enterprise requirements for security, resilience and production readiness.

Hitachi iQ integrates accelerated computing, networking and storage into a validated infrastructure stack. It is built to keep data close to compute, helping improve utilization and efficiency for data-intensive AI workloads.

New AI Blueprints and Data Orchestration in Hitachi iQ Studio
Hitachi iQ Studio, the AI software component of the Hitachi iQ portfolio, enables organizations to design, deploy and govern AI agents within secure enterprise environments. Built on the NVIDIA AI Data Platform reference design, it now includes expanded AI blueprints and multi-agent coordination capabilities that help teams move from prototype to production with greater clarity and control.

The new blueprints introduce defined agent roles, including supervisor and worker models. Worker agents execute tasks while supervisor agents coordinate multi-agent workflows and adapt based on outcomes. This structured orchestration helps organizations automate complex processes while maintaining visibility, efficiency and governance.

Hitachi iQ Studio also expands support for NVIDIA Nemotron models, large language models designed to power advanced, tool-using agentic AI systems, and introduces time machine capabilities that enable AI systems to navigate historical datasets with context and speed. This time-aware intelligence strengthens explainability and supports industries that rely on long-term data patterns to inform decisions.

“As enterprises continue to scale AI, the ability to combine accelerated computing with consistent software and trusted data becomes essential,” said Jason Hardy, vice president of storage technologies, NVIDIA. “Full-stack AI infrastructure optimized for enterprise demand enables organizations to support a wider range of AI outcomes while maintaining the performance, governance, and operational consistency enterprises require.”

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

Expanded Hammerspace Capabilities to Simplify, Automate and Accelerate Data Access
Building on their strategic partnership, Hitachi iQ delivers tighter integration between Hitachi iQ Studio and Hammerspace to streamline data access for agent-driven workflows. With this expanded capability, data managed by Hammerspace can be accessed directly within Hitachi iQ Studio using Model Context Protocol (MCP), an open standard that allows AI systems to securely connect to external data sources.

This enables customers to build AI agents in Hitachi iQ Studio that can securely work with and help manage their Hammerspace data environments, extending automation and insight directly to distributed data without requiring relocation. Data remains governed and protected within VSP One, helping maintain availability and consistent performance as agents operate across environments.

This deeper integration improves data observability and simplifies access to distributed datasets without adding infrastructure complexity, allowing AI agents to work with in-place data across environments without unnecessary data movement. The result is a stronger connection between data orchestration and agent coordination, supported by VSP One Block infrastructure to deliver consistent performance and 100% data availability while preserving hybrid cloud flexibility for enterprise AI.

Accelerating AI Storage
Hitachi Vantara will also be supporting the newly announced NVIDIA STX reference architecture to develop AI-native storage solutions powered by NVIDIA Vera Rubin, BlueField-4, Spectrum-X networking, and NVIDIA AI software.

Hitachi Vantara will showcase Hitachi iQ and Hitachi iQ Studio at NVIDIA GTC 2026, taking place March 16-19 in San Jose, California. Attendees can explore how Hitachi iQ simplifies and advances agentic AI development across industries.

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Dell AI Data Platform with NVIDIA Supercharges Enterprise AI with Breakthrough Data Orchestration and Storage Innovations

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PRNewswire
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0
Dell AI Data Platform with NVIDIA Supercharges Enterprise AI with Breakthrough Data Orchestration and Storage Innovations

Dell AI Data Platform with NVIDIA advancements automate the complete AI data lifecycle and deliver extreme AI storage performance for demanding agentic AI workloads

Dell Technologies will support all of NVIDIA’s latest AI storage and data management innovations

Dell Technologies announces Dell AI Data Platform with NVIDIA advancements that help enterprises discover and activate enterprise data while delivering extreme storage performance to power AI applications and autonomous AI agents.

Why it matters
AI is rapidly shifting from assistive tools to autonomous, agentic systems, but its effectiveness is constrained by the data it can access, trust and act upon. Many enterprises hit a wall because much of their data remains trapped in silos, lacking structure, business context, and governance. The result: AI initiatives stall, investments underdeliver and competitive advantages slip away.

Dell and NVIDIA are removing one of the biggest blockers to enterprise AI: data that’s too slow, too siloed, or too messy to use. As a core component of the Dell AI Factory with NVIDIA, the Dell AI Data Platform with NVIDIA activates enterprise data for AI while maintaining security, governance, and best-in-class performance at scale. Customers see up to 12X faster vector indexing1, 3X faster data processing,2 and 19X faster time-to-first-token3 than traditional computing approaches.

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Automating the entire AI data lifecycle
Dell data engines, accelerated by NVIDIA AI infrastructure, automate the complete AI data lifecycle and dramatically reduce data preparation time while maintaining enterprise governance.

  • The Dell Data Orchestration Engine, powered by technology from Dell’s recent Dataloop acquisition, redefines how enterprises operationalize data for AI. The no-code, low-code engine orchestrates the AI data lifecycle—automatically discovering, labeling, enriching, and transforming structured, unstructured, and multimodal data into governed, AI-ready datasets at scale. By combining automated pipelines with active learning and human-in-the-loop workflows, organizations can continuously improve dataset quality and model accuracy while maintaining governance and control. The Data Orchestration Engine Marketplace lets organizations deploy production-ready data workflows without having to build them from scratch with a curated library of NVIDIA NIM microservices, NVIDIA AI Blueprints and more than 200 other models, applications and templates.
  • Dell Technologies supports the latest NVIDIA AI-Q blueprint, helping enterprises build customizable AI agents that deliver actionable insights for smarter decision-making. NVIDIA-accelerated data engine integrations in the Dell AI Data Platform enable high-performance data preparation, retrieval, and reasoning pipelines across structured and unstructured data. Customers also gain access to a growing library of pre-built NVIDIA blueprints and NIM microservices, along with the NVIDIA Nemotron 3 Super model on Dell Enterprise Hub on Hugging Face.
  • Dell Technologies will also support NVIDIA STX, a new modular reference design powered by next-generation NVIDIA Vera Rubin NVL72, NVIDIA BlueField-4 DPUs, and NVIDIA Spectrum-X™ Ethernet networking that accelerates how organizations manage, process, and retrieve data for AI.
  • The new AI Assistant within the Dell Data Analytics Engine brings conversational natural language interface directly into SQL analytics. Business users can query, visualize and collaborate on governed data products with a common semantic understanding of key metrics intuitively without specialized SQL knowledge. This democratizes data access, streamlines decision-making and unlocks deeper insights faster, which is particularly critical for organizations deploying AI agents that need to access structured data.
  • Within the Dell AI Data Platform with NVIDIA, the introduction of NVIDIA RTX PRO™ Blackwell Server Edition GPUs will bring acceleration directly into the data platform layer. Accelerated NVIDIA CUDA-X libraries including NVIDIA cuDF for structured data processing, and NVIDIA cuVS for vector indexing and search applied to unstructured data, work alongside Dell’s data engines and optimized infrastructure to deliver up to 3x faster SQL queries4 and 12x faster vector indexing.5 These technologies help organizations develop more responsive AI applications and improved infrastructure efficiency when processing and preparing data at scale.

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

Extreme-scale storage software innovations keep GPUs running at full speed
As enterprises move from AI experimentation to production deployment, storage becomes the critical constraint. Traditional storage architecture slows down as it scales, creating bottlenecks that leave GPUs idle and waste infrastructure investments. Dell’s AI-optimized storage engines solve this problem with purpose-built architectures that maintain performance at massive scale.

  • Dell Lightning File System, the world’s fastest parallel file system6, delivers extreme performance density for AI training and inferencing environments with up to 150 GB/second per rack7, up to 20X greater performance versus traditional flash-only scale out file competitors8 and up to 2X greater throughput per rack unit than competing parallel file systems.9 Purpose-built fabric architecture with direct storage access prevents slowdowns, keeping GPUs fully utilized at massive scale. Lightning FS integrates seamlessly into NVIDIA-based AI infrastructures, keeping training and inference workloads running at full speed.
  • Dell Exascale Storage, the only 3-in-1 storage built for extreme-scale AI and HPC10, gives IT teams the flexibility to deploy Dell’s best-of-breed file, object, and parallel file system storage software on the latest Dell PowerEdge servers. Customers can allocate Dell PowerScale, Dell ObjectScale, and/or Dell Lightning File System storage resources on a common hardware platform to support the most demanding AI and HPC environments like high-frequency trading and neoclouds. With support for NVIDIA CX-8 and CX-9 SuperNICs and planned network connectivity up to 800GbE, Exascale delivers read performance up to 6TB/second per rack11, providing the high throughput required by multimodal AI workloads.
  • NVIDIA CMX context memory storage platform support and inference acceleration with KV Cache on shared storage across Dell PowerScale, Dell ObjectScale and Dell Lightning File System allows organizations to offload KV cache from GPU memory to Dell CMX Storage and high-speed shared network storage based on performance needs. This dramatically improves GPU utilization for long-context and agentic AI workloads, allowing AI systems to maintain context across extended interactions without exhausting GPU memory. This capability is essential for enterprises deploying AI agents that need to reference extensive historical data or maintain long conversation threads.
  • PowerScale performance testing: New testing demonstrates that Dell PowerScale’s software-driven Parallel Network File System (pNFS) architecture delivers up to 6X faster performance with large files in enterprise AI environments compared to NFSv3.12 This keeps GPU-intensive AI workloads continuously fed with data, reducing bottlenecks across the entire pipeline and ensuring expensive GPU resources don’t sit idle waiting for data.

Dell AI Factory with NVIDIA delivers proven path to enterprise AI ROI
Dell Technologies today marks the two-year anniversary of the Dell AI Factory with NVIDIA with advancements spanning its end-to-end AI infrastructure, software, solutions, and services portfolio that help enterprises move AI from pilot to production at scale. With over 4,000 customers deploying the Dell AI Factory, and early adopters seeing up to 2.6x ROI within the first year13, Dell proves that an end-to-end approach delivers measurable business results.

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Switch Integrates NVIDIA Omniverse DSX Blueprint into Switch’s EVO AI Factories

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PRNewswire
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Switch Integrates NVIDIA Omniverse DSX Blueprint into Switch's EVO AI Factories

Switch’s Living Data Center (LDC) EVO transforms AI factories from human-managed infrastructure to an automated, intelligent system.

Switch announced that they have integrated the NVIDIA Omniverse DSX Blueprint into their EVO AI Factory™ architecture and LDC EVO™ operating system. LDC EVO, combined with NVIDIA Omniverse libraries and OpenUSD, delivers high-fidelity operations across Switch’s deployed portfolio. LDC EVO’s workflows, intelligence and modeling deliver live, physics-accurate visual representation of the EVO AI Factory.

Traditional data centers run on DCIM, or data center infrastructure management, where humans make decisions assisted by monitoring tools. AI factories operate at extreme density, creating operational complexity that exceeds what DCIM was designed to manage. LDC EVO replaces this model. LDC EVO presents the automation of every system in the facility in near real-time, maintaining an updated 3D digital twin of the complete AI factory, providing our people with unprecedented support and capabilities.

Every NVIDIA DGX deployment requires a facility engineered to its specifications. Switch’s EVO AI Factory is that facility. Switch enables its customers to deploy NVIDIA accelerated computing on Dell PowerEdge servers at extreme density from day one. Switch helped deliver deployments of NVIDIA Grace Blackwell on Dell PowerEdge servers in EVO AI Factories. LDC EVO presented capabilities to allow its customers to validate these hardware configurations before physical deployments.

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Leadership Perspectives

“LDC EVO is the operating system for Switch’s EVO AI Factory, orchestrating the modular and configurable campus architecture that enables hybrid cooling and supports extreme AI densities,” said Zia Syed, Chief Technology Officer of Switch. “It’s built to operate every generation of NVIDIA reference design, including the Rubin DSX architecture. Leveraging NVIDIA Omniverse libraries and OpenUSD for digital twins, we’ve layered in automation workflows and operational intelligence to unify deployments. LDC EVO presents dynamic operations of an AI Factory at scale.”

“Gigawatt-scale AI factories require a shift toward autonomous, telemetry-driven infrastructure capable of orchestrating extreme power and cooling densities in real time,” said Vladimir Troy, Vice President of AI Infrastructure at NVIDIA. “The integration of the NVIDIA Omniverse DSX blueprint into the Switch LDC EVO operating system provides the high-fidelity simulation and operational intelligence necessary to optimize the deployment of next-generation NVIDIA AI infrastructure.”

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The Switch Ecosystem

We brought together the expertise of leading suppliers across the AI infrastructure ecosystem including NVIDIA, Dassault Systèmes, Cadence, ETAP, Schneider Electric, SUSE, Dell Technologies, Oxide Computer Company and Procore Technologies, Inc.

Within LDC EVO™, these collaborating technologies operate as integrated capabilities: thermal modeling, electrical simulation, reality capture, construction lifecycle management and facility telemetry are synchronized into a single presentational environment. The result is that teams can simulate, monitor and adjust operations—all within one interface that improves every operational cycle.

This will be showcased at NVIDIA GTC 2026, where Switch will feature its EVO AI Factory in the DSX AI Infrastructure Pavillion, Booth #91.

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

LangChain Announces Enterprise Agentic AI Platform Built with NVIDIA

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PRNewswire
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LangChain Announces Enterprise Agentic AI Platform Built with NVIDIA

Comprehensive agent engineering platform combined with NVIDIA AI enables enterprises to build, deploy, and monitor production-grade AI agents at scale

LangChain, the agent engineering company behind LangSmith and open-source frameworks that have surpassed 1 billion downloads, announced a comprehensive integration with NVIDIA to deliver an enterprise-grade agentic AI development platform. As part of this collaboration, LangChain is also joining the Nemotron Coalition, NVIDIA’s global initiative to advance frontier open AI models through shared expertise, data, and compute.

LangChain Announces Enterprise Agentic AI Platform Built with NVIDIA.

The collaboration combines LangChain’s LangSmith agent engineering platform and its open-source frameworks (Deep Agents, LangGraph, and LangChain) with NVIDIA Agent Toolkit, including NVIDIA Nemotron models, NVIDIA NeMo Agent Toolkit profiling and optimization, NVIDIA NIM microservices, and NVIDIA Dynamo giving developers a complete stack to build, deploy, and continuously improve AI agents in production. The platform also incorporates NVIDIA OpenShell, a secure runtime that sandboxes autonomous, self-evolving agents with policy‑based guardrails. Development teams often spend months building custom infrastructure rather than delivering business value. The LangChain-NVIDIA platform is designed to close that gap.

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What the Platform Delivers

Build with LangGraph, Deep Agents, and AI-Q: The combined LangChain-NVIDIA stack enables developers to build agents at increasing levels of complexity. LangGraph provides a runtime for stateful multi-agent orchestration with complex control flows and human-in-the-loop patterns. Deep Agents, LangChain’s agent harness, goes further with built-in task planning, sub-agent spawning, long-term memory, and context management, enabling agents that run for minutes or hours across dozens of steps. Building on top of Deep Agents, NVIDIA AI-Q Blueprint is the flagship result of this collaboration: a full production enterprise deep research system that ranks #1 on deep research benchmarks. NeMo Agent Toolkit lets teams onboard existing LangGraph agents with minimal code changes and immediately access advanced profiling, evaluation, and MCP/A2A protocol support for composing multi-agent systems.

Accelerate LangGraph with NVIDIA: The LangChain NVIDIA software package provides NVIDIA-optimized execution strategies applied at compile time with no changes to node logic or graph edges. Parallel execution automatically identifies independent nodes and runs them concurrently, eliminating sequential bottlenecks. Speculative execution runs both branches of conditional edges simultaneously, discarding the wrong branch once the routing condition resolves. Together, these optimizations significantly reduce end-to-end latency for complex multi-step agent workflows.

Deploy with NVIDIA NIM: NIM microservices deliver up to 2.6x higher throughput compared to standard deployments across cloud, on-premise, and hybrid environments. Nemotron 3 Super’s MoE architecture enables cost-efficient deployment on a single GPU. NVIDIA NeMo Agent Toolkit adds production-readiness features including authentication, rate limiting, and a built-in UI for debugging deployed workflows. The toolkit’s GPU cluster sizing calculator lets teams profile their LangGraph workflows under load and forecast exact hardware requirements for scaling from a single user to thousands of concurrent sessions.

Monitor with LangSmith and NeMo Agent Toolkit: LangSmith, which has processed over 15 billion traces and 100 trillion tokens, provides application-level observability: distributed tracing, cost and latency monitoring, Insights Agent for automatically detecting usage patterns and failure modes on a recurring schedule, Polly for natural-language debugging and prompt engineering, and LangSmith CLI for working with trace data. The NeMo Agent Toolkit observability system natively exports telemetry to LangSmith, creating a unified view where infrastructure-level profiling (token usage, timing, throughput down to individual tokens) combines with LangSmith’s application-level tracing and AI-powered analysis in a single platform. To ensure enterprises have the right tools to embrace responsible AI practices, NVIDIA NeMo Guardrails integrates out of the box with LangChain, enabling teams to enforce content safety and policy compliance while customizing guardrails per use case.

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Evaluate across the Nemotron model family: LangSmith and NeMo Agent Toolkit together provide comprehensive evaluation across the full agent lifecycle. LangSmith supports offline evaluation (human review, LLM-as-judge, pairwise comparison, CI/CD integration via pytest/Vitest/GitHub workflows) and online evaluation including multi-turn evals that score entire conversation trajectories for task completion and decision quality. NeMo Agent Toolkit complements this with RAG-specific evaluators, agent trajectory analysis, and a hyper-parameter and prompt optimizer. These capabilities are especially powerful when applied across the Nemotron model family: teams can benchmark the same agent across Nemotron 3 Nano (30B/3B active), Super (~100B/10B active), and Ultra (~500B/50B active), measuring tradeoffs between accuracy, latency, and cost to right-size model selection per task, then use NeMo Agent Toolkit’s automatic reinforcement learning to fine-tune the chosen Nemotron model for their specific workflows.

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Retailers Are Missing Revenue by Getting Personalisation Wrong

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MTS Staff Writer
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0
Retailers Are Missing Revenue by Getting Personalisation Wrong

New Amperity research reveals real-time relevance drives conversion, but identity and execution gaps continue to hold brands back

Australian retailers are keen to capitalise on the power of personalisation, but execution remains challenging. Most acknowledge their capabilities are lagging, even as they recognise personalisation as critical to business success.

New research from Amperity, the leading customer data cloud for consumer brands, offers insights into what drives personalisation effectiveness. The study finds that real-time personalisation has become a direct revenue lever, influencing purchase behaviour and retention when retailers act on customer intent in the moment.

The 2026 State of Personalisation in Retail report, based on a survey of 1000 U.S. consumers, reveals that personalisation only delivers meaningful impact when it reflects live customer intent, not static profiles or delayed batch updates.

The findings are also relevant to Australian audiences, as this market grapples with similar strategic priorities around personalisation while facing execution challenges that prevent many retailers from delivering on customer expectations.

Key findings reveal how missed moments are costing retailers revenue

Real-time personalisation directly drives conversion:

  • 74% of consumers are more likely to purchase when they receive a truly personalised offer or recommendation

  • 69% are more likely to buy when retailers adjust offers instantly while they browse

High-intent moments are being missed:

  • 57% say shopping experiences still feel generic, despite retailers claiming to personalise

  • 79% report that retailers frequently get personalisation wrong, citing irrelevant or mistimed messages

Consumers expect recognition, but rarely get it:

  • 83% want retailers to remember them, including preferences and past purchases

The data shows a growing disconnect between what shoppers expect in moments like browsing, cart consideration, and email engagement, and what retailers actually deliver. When brands fail to act in these moments, they create friction and lose potential revenue.

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More than half of consumers believe brands should personalise their experience in real-time rather than days later, and nearly one-third expect relevant offers to start from their very first interaction. Email remains the preferred channel for personalised outreach, placing even greater pressure on accuracy and timing.

AI is, of course, also expected to play a growing role in personalisation, and consumers favour a balanced approach. Nearly half want personalisation delivered through a combination of human associates and AI assistants, reinforcing the need for systems that blend automation with human judgement to deliver relevance and trust.

Australian market reflects similar challenges, with critical gaps in execution

While this global consumer research reveals the scale of the personalisation opportunity, Australian research that Amperity participated in last year with Arktic Fox shows local retailers face structural barriers to capitalising on it.

The Digital, Marketing & eComm in Focus 2025 report found that 88% of Australian retailers view personalisation as important or very important to their business, yet 57% of marketing leaders overall say their personalisation capability is lagging in the market. This capability gap persists despite 59% of brands experimenting with or scaling AI and GenAI to drive personalisation efforts.

The research revealed a critical disconnect in how retailers approach the foundation of personalisation. While more than half of all brands prioritise unifying customer data, only 25% consider identity resolution a key area of investment.

For Amperity Area Vice President and General Manager for Australia, Billy Loizou, this disconnect is exactly why personalisation continues to underdeliver.

“For companies generating more than a billion dollars in revenue, unifying customer data was ranked as the top priority. But identity resolution barely made the list,” he said.

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“That’s a real concern. You can’t talk about a unified customer view if you don’t know with certainty who the customer actually is. Identity resolution is what turns fragmented data into something usable. Without it, personalisation is guesswork and AI simply scales the noise.

“If retailers want real-time relevance that drives conversion and loyalty, they need to invest in the foundation first. Otherwise, they’re building advanced capabilities on unstable ground.”

The challenge is compounded by resource constraints. Marketing and digital budgets for Australian retailers have remained the same or declined over the past 12 months for 78% of brands, while 65% cite balancing short-and-long-term priorities as their biggest challenge.

Despite the focus on AI for personalisation, only 17% of Australian retailer marketing and digital leaders believe they are effectively leveraging AI to optimise digital content creation processes.

“With budgets under pressure, retailers can’t afford to invest in capabilities that don’t convert,” Loizou said.

“The global findings reinforce what we’re seeing locally. Real-time personalisation drives revenue, but only when the identity foundation is solid. The brands that get this right will grow. The ones that don’t will keep wondering why their AI investments aren’t paying off.”

Download the 2026 State of Personalisation in Retail report to explore how real-time, data-driven personalisation affects purchasing decisions, loyalty, and customer trust, and what retailers must do to close the execution gap in 2026 and beyond.

Amperity’s Customer Data Cloud empowers brands to transform raw customer data into strategic business assets with unprecedented speed and accuracy. Through AI-powered identity resolution, customizable data models, and intelligent automation, Amperity helps technologists eliminate data bottlenecks and accelerate business impact. More than 400 leading brands worldwide, including Alaska Airlines, DICK’S Sporting Goods, BECU, Virgin Atlantic, and Wyndham Hotels & Resorts, rely on Amperity to drive customer insights and revenue growth. Founded in 2016, Amperity operates globally with offices in Seattle, New York City, London, and Melbourne.

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

MarTech Interview with Sherry Smith, President of Retail Media @ Criteo

By
Paroma Sen
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0
MarTech Interview with Sherry Smith, President of Retail Media @ Criteo

Sherry Smith, President of Retail Media at Criteo shares more on how marketers today can drive better results with agentic AI powered experiences:

___________

Hi Sherry, tell us about yourself and your role at Criteo.

My career has grown alongside retail media itself. I was part of the early days of the industry, helping build some of the first retail media programs with Walmart and later leading Triad Retail Media. Back then, we were proving that retailers could turn their first-party data and shopper relationships into a powerful growth engine for brands.

Over the past two decades, I’ve seen retail media evolve from a nascent idea into a core pillar of modern commerce. As President of Retail Media at Criteo, I focus on helping retailers and brands scale that opportunity globally and build for the future of commerce, where retail media plays a central role in driving growth, loyalty, and measurable results across every touchpoint.

How is Retail Media shaping up today, and what top trends will define the market through 2026?

Retail media is entering its next phase of maturity. Over the past decade, growth has been fueled by sponsored search, onsite display, offsite media activation, and marketplace advertising. But as commerce becomes more connected and responsive to shopper behavior, discovery is evolving beyond simple keyword search toward more intuitive, personalized experiences.

Looking toward 2026, I see three major shifts shaping the market.

First, retail media will become more seamlessly embedded across digital touchpoints. This will support richer product discovery experiences while preserving retailer control over inventory, pricing, and shopper relationships.

Second, we’ll see the emergence of new, more native ad formats that feel less like traditional ads and more like helpful recommendations, creating incremental opportunities for brands rather than simply reallocating existing spend.

Third, advanced automation and optimization will become essential. As digital shelf space becomes more competitive, retailers will rely on sophisticated decisioning systems to balance sponsored and organic results, maximize performance, and protect the customer experience.

For brands resetting their agentic commerce workflows and experience: what top tips would you share with them?

As agentic commerce evolves, brands should start by recognizing that discovery is becoming more conversational and context-driven, but it is still anchored in retailer environments. AI-driven experiences rely heavily on structured product data, clear attributes, and strong content signals. Brands that invest in making their product information accurate, differentiated, and easy to interpret will be better positioned as recommendations become more dynamic.

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Can you talk about a few brands from around the world who you’ve seen build unique retail experiences with agentic AI?

While retailers ultimately own and operate the commerce experience, we’re seeing innovative brands lean into these new environments in thoughtful ways.

In markets like the U.S., as retailers introduce more guided or conversational shopping features, leading brands are investing in richer product content, enhanced attributes, and contextual storytelling that help their products surface naturally within those experiences.

Globally, the brands that stand out aren’t necessarily building standalone AI experiences themselves. Instead, they’re partnering closely with both retailers and emerging players like LLMs to support incremental discovery and ensure their brand is presented in these new shopping environments.

Five thoughts on the future of retail from your perspective?

First, retailers remain central to commerce because they control the fundamentals: trust, pricing, loyalty, fulfillment, and customer relationships. Technology will continue to evolve, but those assets are enduring competitive advantages.

Second, discovery will continue to diversify. Consumers will move fluidly across retailer sites, marketplaces, social platforms, and emerging interfaces depending on need and context. Winning retailers will meet shoppers wherever they are while maintaining a consistent, trusted experience.

Third, trust will become an even more powerful economic driver. As commerce grows more personalized and automated, transparency and reliability will directly influence conversion, loyalty, and long-term brand value.

Fourth, digital shelf space will become more strategic. As assortments expand and attention becomes scarcer, retailers and brands will need smarter merchandising, better data, and more sophisticated optimization to ensure relevance and performance.

Finally, retail media will solidify its role as a foundational revenue engine. When integrated thoughtfully into the commerce experience, it strengthens partnerships with brands and supports sustainable, incremental growth.

Top of mind best practices for brands looking to optimize their retail media outlook and output in 2026.

Brands need to think beyond campaigns and focus on impact. In 2026, the winners will be those who align retail media investment with merchandising strategy, category growth, and customer lifetime value — not just short-term ROAS.

They should also move early on emerging formats and experiences, but with discipline. Testing is critical, yet every activation should be measured against incrementality and long-term brand equity.

Most importantly, retail media performance will hinge on partnership. The brands that treat retailers as strategic growth collaborators, rather than media channels, will unlock the greatest value.

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Criteo

Criteo is the global commerce media company that enables marketers and media owners to drive better commerce outcomes.

About Sherry Smith

Sherry Smith is President of Retail Media at Criteo

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