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Harmonic Introduces Breakthrough AI Orchestration Service for Live Video Workflows

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Harmonic Introduces Breakthrough AI Orchestration Service for Live Video Workflows

New Service Simplifies Integration of AI Applications with Exceptional Reliability and Security

Harmonic announced a breakthrough new AI orchestration service that enables broadcasters, streamers and service providers to seamlessly integrate AI applications into live video workflows with broadcast-grade reliability and security. The service empowers media companies to efficiently and securely enhance their workflows with best-of-breed AI applications developed by Harmonic and its trusted partners. As a result, service providers can lower AI-related investments and accelerate time to market for more personalized, cost-efficient live streaming and broadcast services.

“As video workflows increasingly become more complex and distributed, media companies face growing challenges with fragmented AI tools, complex integrations and inconsistent reliability,” said Yaniv Sibony, vice president of growth product management at Harmonic. “Our new AI orchestration service changes the game by enabling multiple AI applications to operate from a unified control plane, with built-in reliability and security, helping media companies innovate faster, reduce complexity and confidently bring AI-powered video experiences to market.”

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Boosting Video Workflow Efficiency with a Unified AI Control Plane
Harmonic’s AI orchestration service aggregates, orchestrates, maintains and updates AI applications implemented by Harmonic and best-in-class partners. Through a unified interface, service providers can easily configure, schedule and activate AI applications dynamically for simpler, more cost-effective operations. The service coordinates AI-based processing for live content, synchronizes outputs across multiple AI engines and ensures seamless failover through built-in redundancy mechanisms.

Available as a stand-alone SaaS offering, the orchestration service supports a suite of AI-driven applications, including real-time audio translation and transcription, dubbing and localization, automated live sports highlight generation, scene-level metadata generation, ad-break detection and in-stream ad triggering. Built for the broader ecosystem, the service works natively with Harmonic’s full suite of media processing solutions including VOS®360 Media SaaS, VOS Media Software, XOS media processor and Spectrum™ X Plus.

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Delivering Broadcast-Grade Reliability
Harmonic’s AI orchestration service offers media companies a host of benefits, including:

  • Unified Governance: Service providers can manage multiple AI vendors from a single, secure control plane.
  • Synchronized Processing: Precise alignment of AI outputs across multiple engines enables media companies to deliver frame-accurate broadcast and streaming services.
  • Built-in Redundancy: Seamless failover mechanisms, combined with security and health monitoring for AI tasks, help service providers prevent on-air disruptions.
  • Dynamic Scheduling: Service providers can effortlessly schedule and activate AI applications on a per live event basis.

Simplifying AI Adoption
By unifying multiple AI applications into a single platform, the AI orchestration service enables media companies to avoid costly integration cycles and rip-and-replace upgrades as AI technologies evolve. Ultimately, the service accelerates time to market for new AI-powered offerings by eliminating the need for complex integrations and repeated platform migrations.

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GRAX to Debut Zero-Code AI for Salesforce at TDX

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GRAX to Debut Zero-Code AI for Salesforce at TDX

GRAX, Inc. Logo

GRAX unveils a new solution for turning verified, governed Salesforce historical data into business-ready answers — inviting TDX attendees to see it live!

GRAX, Inc., a leading data platform built for Salesforce data protection and AI, announced it will be showcasing its latest platform innovation for governing, querying, and activating Salesforce data at TDX. GRAX will be offering an early look at GRAX AI: a zero-code AI product that leverages verified and governed data to accelerate answers to real-life data-driven business questions.

Your AI is only as good as the data behind it. GRAX AI debuts at #TDX2026 — zero code, full Salesforce history, 100% yours. Booth #9. Come see it.

With GRAX, users can obtain full context and history to fuel AI, and now, the intelligence layer has been added on top — unlike other tools in the market relying on simple snapshots of CRM data. GRAX AI is designed to accelerate answers to business questions without a single line of code. Key features include:

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  • Clicks, Not Code™: Anyone from Salesforce admins to business users can generate reports and deploy AI assistants. GRAX provides deep historical context for Agentforce agents, ensuring they act on the full history.
  • Business-Ready Reporting: Create beautifully branded and executive-ready departmental reports with precise insights and prescriptive actions
  • Streamlined Time-to-Value: Out-of-the-box knowledge packs and agentic jobs for Salesforce and IT admins, sales, service and marketing to drive faster ROI on Salesforce investments
  • 100% Data Ownership and Control: Complete control with verified, comprehensive historical context

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“When you own 100% of your history, your agents and AI models are powered by deeper knowledge and comprehensive context that empowers you to trust the answers,” said Joe Gaska, GRAX CPO, “In the age of AI, trust and data ownership isn’t optional — it’s everything.” As regulators increasingly require enterprises to prove integrity of data powering AI, accountability flows back to the data owner. GRAX AI is built on governed, customer-owned data foundation with full Digital Chain of Custody data provenance, and immutable history that satisfies audit requirements in the age of AI.

This builds upon GRAX’s existing AgentExchange solution, GRAX for SFDC Backup & Recovery, Archive, Data Seeding, Pipelines & Reuse.

GRAX Exhibiting at TDX 2026

GRAX will be exhibiting at booth #9 on Level 2 of Moscone West, showcasing GRAX AI and the entire suite of products, ranging from data backup and recovery to archive to seeding and more. Stop by the booth to see what’s possible.

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FreeWheel Launches First-of-its-Kind Context Engine to Deliver Scaled, AI-Driven Contextual Targeting Globally Across Premium Video

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FreeWheel Launches First-of-its-Kind Context Engine to Deliver Scaled, AI-Driven Contextual Targeting Globally Across Premium Video

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New features enable smarter ad alignment and enhanced brand safety for advertisers, improved monetization for publishers, and a better viewer experience for consumers

FreeWheel, the leading global technology platform for the streaming advertising ecosystem, announced the launch of Context Engine, a new AI-driven feature within FreeWheel’s Streaming Hub that helps advertisers align ads to relevant premium video content with greater ease, precision, and scale. At the same time, publishers can unlock incremental value from programming based on more granular attributes in content that have high relevancy to the viewer such as emotion, location and objects.

Contextual alignment drives up to +40% more brand recall and delivers brand outcomes across a range of content, allowing publishers to monetize entire catalogs, not only the most “obvious” environments for a given brand*. However, adoption of contextual tools has been slow.

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

“Contextual advertising is an increasingly important lever for performance and monetization in premium video. Yet the high cost of implementation has limited adoption at scale for many publishers,” said Larry Allen, Vice President of Global Strategy Addressable, Data, and Measurement, FreeWheel. “Context Engine solves these challenges, providing smarter ad alignment and stronger brand safety to drive higher engagement for advertisers, and enabling publishers to activate premium contextual signals in weeks versus months.”

Context Engine, which is integrated into FreeWheel Streaming Hub and powered by Comcast Technology Solutions’ VideoAI, combines computer vision, machine learning, and generative AI to analyze millions of hours of premium video content and assign contextual segments and keywords from closed captions and visual signals. These classifications make inventory more discoverable and brand-safe for buyers, enabling them to deliver ads at scale alongside content that resonates with audiences, expanding reach and enhancing the viewer experience.

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This is especially important in markets such as the UK and France that have limited access to audience data for targeting.

“As the advertising sales house of the public service broadcaster, brand trust and suitability are essential,” said Marianne Siproudhis, CEO of FranceTV Publicité. “Through this pilot of Context Engine, where we’re proud to be the first in France to test the technology, we’re advancing the way we classify video more consistently at scale, so advertisers can align messages to the right programming while we strengthen monetization across our premium inventory.”

Context Engine is designed to help publishers monetize their programming more effectively such as differentiating content and unlocking previously blocked budgets, while maintaining full control over their inventory classification.

“A+E Global Media (A&E, HISTORY, and Lifetime) is built on premium storytelling and innovation in brand-safe environments,” said Stacie Danzis, SVP, Digital Ad Sales, A+E Global Media. “Advertisers want confidence their messages align with that experience and appear at the right moments. In our pilot with FreeWheel, Context Engine translates our programming into scalable contextual signals—making it easier for buyers to align campaigns without adding complexity for our teams.”

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Wizz App, Trusted by 16 Million Users, Partners with Cinder to Power AI-Driven Content Moderation At Scale

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Wizz App, Trusted by 16 Million Users, Partners with Cinder to Power AI-Driven Content Moderation At Scale

Wizz – Make New Friends & Build Real Connections

Committed to building a platform where every user feels safe and valued, Wizz App is consolidating its Trust & Safety infrastructure into Cinder.

Every month, more than 20 million videos and images move through Wizz App, a social discovery platform for Gen Z who want real connections and shared experiences. Behind every one of those uploads is a young adult looking for their people. And behind every one of those uploads is a Trust & Safety team responsible for making sure they find them safely.

Committed to building a platform where every user feels safe and valued, Wizz App is taking a decisive step forward by consolidating its Trust & Safety infrastructure into Cinder, which provides mission-critical infrastructure that protects the world’s most important platforms from junk, abuse, manipulation, and harm at scale.

From different providers to one unified system
Until now, the tools that power Trust & Safety at most social platforms tended to live in separate systems, managed by separate teams. This is why Wizz App selected Cinder as its unified digital safety operating system. The partnership brings together Wizz App’s human review teams and automated moderation workflows under one unified platform, giving the company the speed, consistency, and control needed to keep its community safe.

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

How it works
At the core of the integration are AI agents trained specifically on Wizz App’s content policies. The platform enforces 11 community policy areas covering everything from identity and age verification to child sexual abuse and exploitation (CSAM), harassment and bullying, hate speech, and personal privacy, and holds itself to a standard that reflects its primary user base: Gen Z users aged 16 to 24 who deserve a platform that takes their safety seriously.

Cinder’s agents evaluate every image and video uploaded or shared on the platform, distinguishing between violating and non-violating content with precision, helping human moderators make better and faster decisions with high confidence. Cinder’s AI understands the nuances of Wizz App’s community standards, reducing both over-enforcement and harmful content slipping through. Cinder agents help human moderators work faster and smarter, while also supporting fully automated decision-making that bypasses the review queue entirely.

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“Wizz App has always put community safety first. Partnering with Cinder is the natural next step, bringing even greater speed, consistency, and visibility to a Trust & Safety program we’re proud of,” says Alexandra Ryabova, Head of Operations. “Cinder amplifies our safety measures, giving our teams and AI systems a unified platform to operate at their best.”

Cinder also manages Wizz App’s user reports and content appeals, processing reports per month through Cinder Managed Moderation, creating a seamless workflow from initial flagging through final resolution. Human reviewers and AI systems work in concert inside the same environment, ensuring nothing falls through the cracks. Every decision is traceable. Every outcome is auditable.

By consolidating moderation, reporting, and appeals into Cinder’s platform, Wizz App gains full operational visibility into its Trust & Safety program. Based on results with similar platforms, teams can expect to reduce operational time by 50% or more, with room to improve further as workflows mature. Cinder’s infrastructure also delivers sub-second latency for synchronous workflow processing, ensuring that speed never becomes a bottleneck as case volume grows. Teams can respond more consistently and scale their operations without sacrificing quality, all critical capabilities as the platform continues to grow.

“Wizz App is exactly the kind of company we built Cinder for: a fast-growing platform that understands safety challenges are core to every product,” says Glen Wise, Cinder CEO. “By bringing together their human review teams, AI workflows, and appeals process into a single place, we’re giving Wizz App mission-critical infrastructure so the team can move fast without compromising on safety. We’re proud to be their partner.”

Trust & Safety has always been at the heart of the Wizz App experience as a priority. Cinder is the next evolution of that commitment—expanding their capabilities while staying true to the community they’ve built.

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Springbig Launches AI Audience Builder, Giving Regulated Retailers a Smarter Way to Target, Engage, and Grow

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Springbig Launches AI Audience Builder, Giving Regulated Retailers a Smarter Way to Target, Engage, and Grow

New natural language segmentation capability accelerates campaign execution, reduces marketer effort, and drives measurable revenue impact for regulated retail operators

Springbig (OTCQX: SBIG), the AI-powered marketing and customer engagement platform serving regulated retailers and brands, announced the launch of AI Audience Builder, a breakthrough capability that enables retailers to create precisely targeted customer segments using plain, natural language powered by artificial intelligence. No filters. No manual logic. No data expertise required.

The launch positions Springbig at the forefront of AI-driven retail marketing, delivering a tangible productivity and revenue advantage to dispensary operators competing in an increasingly data-intensive market.

Instead of navigating complex segmentation workflows, marketers can now describe an audience in their own words and watch Springbig’s AI instantly build it, dramatically compressing the time from insight to campaign execution.

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

Transforming How Marketers Work With Data

Audience segmentation has long been one of the most powerful, yet consistently underutilized, levers in retail marketing. The barrier has never been data availability. It has been accessibility.

Traditional segmentation requires marketing teams to build logic manually, configure multiple filters, and maintain a working knowledge of underlying data structures. For operators running lean teams across multiple locations, that friction translates directly into missed revenue opportunities.

AI Audience Builder removes that barrier entirely. Key capabilities include:

  • Natural language audience creation based on intent, purchase behavior, or campaign goals
  • Instant segmentation without manual filter configuration or technical training
  • Faster campaign execution that compresses setup time from hours to seconds
  • Higher targeting precision grounded in real, real-time customer data
  • Broader team access, empowering any marketer, not just data specialists, to run sophisticated campaigns

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“For too long, the most powerful tools in retail marketing required technical expertise to unlock,” said Jaret Christopher, Chief Executive Officer of Springbig. “AI Audience Builder changes that equation. When a marketer can describe who they want to reach in plain English and have that audience built instantly, it is not just a better workflow. It is a direct driver of revenue. We built this because speed and precision at the campaign level are where wins are made, and we are giving every one of our customers that edge.”

Speed and Precision Where It Matters Most

In high-velocity retail environments, the window between consumer intent and purchase decision is narrow. AI Audience Builder allows operators to react in real time, building and launching hyper-relevant campaigns without the delays historically caused by manual segmentation.

This capability delivers outsized impact during peak promotional periods, product launches, and high-traffic events, where message relevance directly determines conversion and revenue outcomes.

Operators leveraging AI Audience Builder can expect to:

  • Launch campaigns faster with dramatically reduced setup time
  • Reduce segmentation errors that dilute message relevance and waste spend
  • Increase customer lifetime value through smarter, behavior-driven targeting
  • Unlock deeper intelligence from existing customer data without additional infrastructure
  • Scale marketing output without scaling headcount

The result is a more agile, more profitable marketing operation, one that keeps pace with consumer behavior and operational demands simultaneously.

A Platform Built for AI-Native Marketing

AI Audience Builder is the latest addition to Springbig’s expanding suite of AI-powered capabilities, which span campaign automation, personalized messaging, loyalty optimization, and performance analytics. Each capability is designed with the same goal: reduce friction for the marketer and increase the return on every customer interaction.

For investors, AI Audience Builder represents a meaningful advancement in platform stickiness, operator efficiency, and the revenue impact Springbig delivers to its client base. Features that measurably improve operator outcomes strengthen retention, expand usage, and accelerate the platform’s competitive differentiation in the regulated retail technology sector.

“Springbig has made a deliberate and committed transition to being an AI-first company,” said Christopher. “We are embedding artificial intelligence at the core of our MarTech platform and across our entire organization. The goal is straightforward: automate what is manual, simplify what is complex, and use AI to drive operational efficiencies both inside Springbig and for every customer we serve. AI Audience Builder is one example of that commitment in action, but it reflects a much larger transformation underway across our business.”

“The opportunity in AI is not abstract,” Christopher added. “Every campaign that gets built faster, every audience that gets targeted more precisely, every message that lands at the right moment drives measurable revenue for our customers. That is what we are building toward: an AI layer that makes the entire marketing operation smarter, faster, and more effective at every touchpoint.”

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Webflow Launches Webflow AEO, A Closed-Loop Agentic Answer Engine Optimization Solution for Modern Marketing Teams

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Particular Audience Launches PA DiscoveryOS on Shopify

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The end-to-end agentic solution enables enterprise marketing teams to measure AI search visibility, get brand-aware recommendations, and ship improvements at scale — all natively in Webflow

Webflow, the agentic web marketing platform, today announced Webflow AEO, a closed-loop answer engine optimization (AEO) solution that helps enterprise marketing teams get discovered, understood, and cited by AI answer engines. Webflow AEO, now in private beta, uses AI agents to bring measurement, recommendations, and execution directly into Webflow, so teams can go from insight to shipped improvements quickly and seamlessly.

“Most teams know AEO matters, but the problem is they can’t execute on it fast enough because their tools weren’t built for it and the context isn’t there. We’re helping close that execution gap with agents,” said Rachel Wolan, Chief Product Officer at Webflow. “Webflow allows customers to work inside a system that already knows their brand, their voice, and what they’re trying to say. We’ve automated closing the loop with Answer Engines because of this, so Webflow agents can continuously analyze your AEO presence, surface what to fix, and ship user-approved improvements all without ever leaving the platform where their site lives.”

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Webflow AEO is built around a closed loop designed to make AEO operational for teams at scale:

  • Measure AI visibility: Webflow Analyze will be expanded with dedicated AEO analytics, so enterprise teams can easily understand how often their brand is cited in answer engines, which prompts they appear in, and how AI visibility connects to on-site engagement and conversions. No data expertise or instrumentation required.
  • Recommend discoverability improvements: AEO agents surface prioritized, brand-specific recommendations across technical and content optimization — from broken links and outdated metadata to new content opportunities that are likely to boost citations for the prompts you’re tracking.
  • Act in a closed loop: AEO agents help teams turn recommendations into shipped changes across their site at scale, with review-before-publish safeguards that allow teams to move fast while staying in control.

This launch builds on a year of investment in AEO education and foundational tools: our widely-adopted maturity model and assessment, support for llms.txt and Markdown for agents, LLM-referred traffic insights, and our first AI-assisted technical SEO auditing tool that drove 75% more monthly organic traffic for customers who adopted it. Webflow AEO is the culmination of that work: purpose-built for marketing teams to measure, execute, and scale their AEO strategy.

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“Search continues to evolve from static results pages to more intelligent, answer-driven experiences, and at Walker & Dunlop, we’ve been investing in structured data, answer-ready content, and a more integrated web and content approach,” said Allison Facciani, Sr. Digital Marketing Manager, Walker & Dunlop. “What’s exciting about Webflow AEO is the potential to bring greater visibility into how our brand is showing up in answer engines, and a more streamlined way to act on those insights. We see it as a natural next step in scaling our AEO efforts.”

”Our decision to migrate to Webflow was in large part due to their continued investment in AI tools, and Webflow AEO is a perfect example of how we plan to use their platform to drive business growth,” said Dylan Zaitsoff, Director of Product Management, Daily OM (an Everyday Health Group company). “Despite having a strong SEO foundations, AEO is new to everyone on our team, so having a tool that gives us visibility into how we show up in answer engines — along with optimization recommendations that a non-technical editor can easily publish — is exactly what we need to keep up and stay ahead of what ‘organic search’ is becoming.”

Webflow AEO is currently in private beta, and will be available soon to all Enterprise customers. To book a demo or learn more about what Webflow AEO can unlock for your team, contact sales here or reach out to your account team.

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ProGEO.ai Survey Finds 75.9% of Marketing Professionals Use GenAI for Work Daily

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CaliberMind Launches B2B Marketing Mix Modeling, Unifying Tactical Attribution and Strategic Planning in One Platform

ProGEO.ai - Generative Engine Optimization (GEO)

Three-quarters (76.8%) of survey respondents report a corporate usage policy, but only 43.8% report technical controls

ProGEO.ai, a data-driven generative engine optimization (GEO) agency, today published the March 2026 “AI Marketing Maturity (AIMM) Index,” a survey of 112 marketing professionals conducted at RSAC, the world’s largest cybersecurity conference.

Maturity models are common in cybersecurity. The Cybersecurity Maturity Model Certification is a Department of Defense (DoD) framework required for its contractors. MITRE, a cybersecurity non-profit, published the MITRE AI Maturity Model.

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The AI Marketing Maturity (AIMM) Index is intended to benchmark enterprise adoption rates and marketing use cases to establish a baseline for marketing maturity. The AIMM Index reveals that 91.1% of respondents have a corporate-paid subscription to GenAI and 75.9% are using GenAI for work on a daily basis.

Key findings from the “AI Marketing Maturity (AIMM) Index” include:

  • A gap between policy and enforcement – 76.8% of marketing professionals report their company has a corporate usage policy for GenAI, but only 43.8% report their company enforces this policy with technical controls.
  • Content creation is the top use case – 83% of marketing professionals are using GenAI to brainstorm content at least once a week; 82.1% are creating content, and 75% are repurposing content.
  • Vibe coding is on the rise – 23.2% of marketing professionals are using GenAI for vibe coding at least once a week.

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

“Most marketing professionals are using AI every day, but less than half of their organizations are enforcing corporate usage with technical controls,” said Clinton Karr, CMO, ProGEO.ai. “Adoption without governance shows a lack of maturity, which calls into question whether marketing teams are using AI strategically.”

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Ecer.com: Redefining the Future of Cross-Border B2B with a Mobile-First Ecosystem

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Ecer.com: Redefining the Future of Cross-Border B2B with a Mobile-First Ecosystem

Ecer.com: search for China Manufacturers, wholesalers, Suppliers, Exporters  & Factories from the leading B2B marketplace

In the traditional foreign trade model, cross-timezone communication and physical factory inspections often resulted in grueling decision cycles and high costs. That landscape is being radically transformed by the rise of the mobile internet.

The Mobile Efficiency Leap
Recent data reveals that over 70% of international buyers now utilize mobile devices as their primary tool for sourcing suppliers, sending inquiries, and conducting remote factory audits. In response, Ecer.com, a global leader in mobile B2B marketplace, is pushing the boundaries of technical innovation to usher in an era of “anytime, anywhere” high-efficiency collaboration.
“Business opportunities are now reachable at a moment’s notice,” a spokesperson for Ecer.com noted. Through the Ecer.com mobile platform, buyers can initiate instant video inspections and discuss product specifications in real-time. Furthermore, an AI-powered inquiry system facilitates multi-language interaction, effectively eliminating communication barriers and converting time-zone delays into 24/7 business connectivity.

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

Building Trust Through Immersive Technology
Trust remains the most critical variable in cross-border B2B transactions. To address the high costs of traditional on-site audits, Ecer.com has introduced a mobile-based Panoramic Factory Inspection solution.
By leveraging VR technology and 360° real-time mobile views, buyers can conduct immersive observations of production lines and product details. This innovation shrinks a process that once took days into a matter of hours.
 Case Study: Hunan CTS Technology Co,.ltd recently used ECER’s mobile tools to secure a deal with a skeptical German buyer.
 The Result: By providing a live video tour of the workshop, testing procedures, and warehouse management—supplemented by VR structural analysis—the enterprise turned a “preliminary inquiry” into “deep trust” in under two hours. The initial order was confirmed within the same week.

A Full-Link Digital Trade Loop
Moving beyond simple information matching, Ecer.com is evolving into an integrated digital trade powerhouse. The platform seamlessly connects every stage of the trade journey—from inquiry acquisition and remote inspection to logistics tracking—all within a mobile interface.
The fusion of AI, VR, and live interaction reduces the complexity of global collaboration, allowing supply chains to respond more with greater agility to market fluctuations.

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

The “Always-On” Era of Foreign Trade
As “Mobile First” becomes the industry standard, the workplace for foreign trade has shifted from the confines of the office to any space with an internet connection. By merging mobile technology with intelligent capabilities, Ecer.com is helping enterprises achieve instant response and precision docking. In an increasingly competitive global market, being “Always-On” is no longer an advantage—it is the new core competency for growth.

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zMaticoo SDK 2.0 Officially Released: Full Technical Breakthroughs for Global Ad Monetization

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zMaticoo SDK 2.0 Officially Released: Full Technical Breakthroughs for Global Ad Monetization

Fresh off its successful offline event recently, zMaticoo has officially launched zMaticoo SDK 2.0, a comprehensively upgraded programmatic ad monetization SDK designed for global mobile developers. The event brought together industry professionals from Google, PubMatic, Tencent and other leading companies for technical exchanges and ecosystem collaboration.

Backed by proprietary AI algorithms and deep learning models, SDK 2.0 has been validated by rigorous global A/B testing, delivering exceptional improvements across core monetization metrics: 26.7% higher ad fill efficiency, 27.4% growth in theoretical revenue, and 3.9% positive eCPM lift, breaking the industry trade-off between fill rate and unit price.

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

The upgrade features five key technical optimizations covering the entire ad lifecycle.

  1. Low-latency loading cuts initialization time to under 100ms with intelligent caching and progressive asset loading.
  2. Load-show decoupling eliminates rigid binding, boosting ad show success rate by over 30%.
  3. Native rendering replaces WebView, reducing template size by 80% and accelerating speed by 90%+.
  4. Optimized click paths adopt DeepLink/CCT for e-commerce and in-app Google Play installation for CPI campaigns, lifting conversion efficiency by 20%+.
  5. Built-in GDPR, CCPA, and COPPA compliance ensures global readiness, while mandatory two-week gray release validation guarantees stability.

zMaticoo also previewed its upcoming AIGC-powered smart template system, which will dynamically optimize creatives based on user behavior, region, and app scenarios to further improve CTR and conversion.

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

With developer-centric design, zMaticoo SDK 2.0 balances performance, user experience, and revenue, helping global publishers maximize traffic value and achieve sustainable growth.

zMaticoo holds IAB certification and Google Index validation, ensuring full compliance, transparency, and secure integration for global publishers.

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Envisionit Names Tiffany Kirby President and Chief Growth Officer

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Envisionit Names Tiffany Kirby President and Chief Growth Officer

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Envisionit named Tiffany Kirby President and Chief Growth Officer, expanding her mandate to lead the agency into its next phase of growth as the marketing industry reaches an inflection point.

Since joining in 2022, Kirby has driven double-digit growth year over year by doing something most agencies still can’t: delivering truly integrated solutions across brand, media, and technology without sacrificing depth in any of them.

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That integration-first mindset is now more critical than ever. Agencies across the industry have rushed to attach the word “AI” to their capabilities, but few have done the work to make it operational or meaningful for clients. Envisionit has. In 2023, CEO Todd Brook founded Unchained, a specialized AI consultancy focused on AI strategy, education, and development. Kirby has been central to bridging that capability back to Envisionit’s clients, partnering with Unchained to apply AI in ways that strengthen marketing outcomes rather than just streamline internal processes.

As President, Kirby’s scope now extends beyond growth to encompass operations, client strategy, and team development. She is joined in the leadership structure by Jason Goldberg, an owner and Chief Innovation Officer, who reports directly to Kirby and ensures that innovation and AI integration remain embedded in how the agency serves clients.

The result is a leadership model built for where the industry is heading: fully integrated services with AI applied practically to help clients work smarter and maintain a competitive edge.

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“AI without a marketing lens risks efficiency at the expense of experience. Marketing without AI means falling behind. Our clients get both: integrated marketing excellence and specialized AI working together, so the technology behind the scenes works to improve the brand experiences people feel,” said Kirby.

“What Tiffany has built at Envisionit speaks for itself: double-digit growth and real results for clients driven by truly integrated thinking,” said Todd Brook, CEO of Envisionit. “She’s carried our AI vision forward, made sure every department has the resources to be successful, and leads with a people-first mindset that gets the best out of the team. This role puts her at the center of all of it.”

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AI Won’t Transform Your Business. Your Data and Systems Will.

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AI Won't Transform Your Business. Your Data and Systems Will.

Eighty-eight percent of organizations are now using AI in at least one business function. Only 34% are genuinely rethinking how their business operates around it. And just 20% have seen real revenue growth from their investments. That gap is not a technology problem. It’s a readiness problem. Most organizations are dropping AI onto broken data, misaligned teams, and workflows that were never intentionally built. Then they wonder why the results aren’t coming.

The Foundation Was Already Broken

Here’s something I’ve seen time and again: AI doesn’t fix broken systems. It runs faster inside them. When CRMs hold conflicting records, AI produces conflicting outputs. When sales and marketing can’t agree on what a qualified lead looks like, AI accelerates that disagreement. When workflows were patched together through years of reactive decisions, AI speeds up the chaos. The vast majority of enterprise AI initiatives fail to deliver a measurable return, and the reasons have nothing to do with the technology itself. Poor strategy, misaligned teams, and disconnected systems and data are driving those failures.

Revenue leaders feel this directly. They’ve invested in CRMs, tech stacks, and sales enablement tools. None of them talk to each other the way they should. Teams spend more time pulling reports than acting on them. And now there’s pressure to layer AI on top of it all. Adding AI to a broken operating model is a liability, not a strategy.

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Data Quality Is a Revenue Problem

Before any AI initiative can deliver, the data underneath it has to be clean and connected. Most organizations aren’t there yet. Data quality consistently ranks among the top operational challenges for senior leaders, and the financial cost of bad data is significant. Organizations that ignore it aren’t just dealing with reporting headaches. They’re leaving real revenue on the table.

The fallout extends directly into AI deployments. Some companies are already reporting negative results from their AI investments, and the most common barrier isn’t budget or technology access. It’s on the people side. Teams that don’t have the skills or the clean inputs to work effectively with the tools they’ve been handed. Many data and analytics leaders will say their data strategy needs a serious overhaul before their AI ambitions can work. Companies are regularly drawing wrong conclusions from data that lacks business context, and AI accelerates that problem rather than correcting it.

The Martech Stack Isn’t the Answer

The tools were supposed to solve this. They haven’t. McKinsey’s martech research found that 47% of martech decision-makers say stack complexity and integration problems are the main reasons they can’t get value from their tools. Not one of the 50+ senior marketing leaders interviewed at Fortune 500 companies could clearly explain the ROI of their martech investment.

The organizations winning with AI in 2026 built the foundation before they built the model. They started with infrastructure. They treated data governance as a revenue priority, not an IT task. They rebuilt workflows to operate with AI. And they aligned their sales, marketing, and customer success teams around shared data and shared definitions before adding any new technology layer. The pattern is consistent across high-performing organizations: workflow redesign comes before model deployment. The operating system built around the model is what defines leaders, not the sophistication of the model itself.

What Comes Next

The organizations that will pull ahead in 2026 are the ones that got the basics right before deploying advanced AI. The ones still chasing model sophistication are building on unstable ground. Start with one clean source of truth. Sales, marketing, and customer success data all in one place, no exceptions. Align your teams around shared KPIs before you even think about deploying tools that depend on that alignment to work. Treat workflow redesign like the business priority it actually is, not something you get to when things slow down.

And be honest with yourself: AI will move you faster in whatever direction your operations are already pointed. If the foundation is broken, AI just accelerates the break. Get the foundation right, and AI becomes a real growth driver. Skip it, and you’re adding speed to a system that was already headed in the wrong direction.

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Lucid Software integrates real-time visual context into ChatGPT with MCP server app

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Lucid Software integrates real-time visual context into ChatGPT with MCP server app

Lucid Software, the leader in AI-driven work acceleration, announced the launch of its Lucid app for ChatGPT, which enables users to search, summarize, and generate Lucid documents directly in ChatGPT.

As teams leverage AI to ideate and execute work, fragmented information across applications hinders them from accessing the context they need to move work forward. The Lucid app for ChatGPT bridges Lucid’s visual intelligence and the conversational power of ChatGPT, so users can:

  • Find Lucid documents instantly by asking ChatGPT to locate diagrams and boards related to a topic or project.
  • Unlock instant insights by generating summaries of visual work from intricate diagrams or large-scale brainstorms in seconds.
  • Turn complex ChatGPT discussions into editable diagrams and open them directly in Lucid.
  • Share diagrams and documents with teammates directly from a conversation.

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“The Lucid app for ChatGPT is yet another example of how Lucid is bringing the systems and context teams rely on into the AI tools they use every day,” said Jamie Lyon, Chief Product & Strategy Officer at Lucid Software. “When that context is readily available, teams can move faster and stay aligned. That’s where Lucid excels—bringing clarity to how teams work with AI so they can execute with confidence.”

The integration is powered by Lucid’s MCP server, which connects large language models with Lucid documents so AI tools can securely search for diagrams, fetch document content, create visualizations, and share documents.

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UJET Launches New Channel-Led Global Sales Motion with Google Cloud, Accelerating Agentic AI and CX Transformation in Midmarket and SMB Segments

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UJET Launches New Channel-Led Global Sales Motion with Google Cloud, Accelerating Agentic AI and CX Transformation in Midmarket and SMB Segments

New partnership delivers rapid deployment of Google Cloud CCaaS and Gemini Enterprise for Customer Experience through AVANT’s Global Network of Trusted Advisors

UJET, a leader in AI-powered contact center innovation, announced the launch of Google Cloud CCaaS by UJET, a new managed service offering and strategic sales motion designed to bring Google Cloud’s enterprise-grade agentic AI, CX, and contact center solutions to the small-to-medium business (SMB) and midmarket sectors through AVANT’s Technology Solutions Distributor (TSD) channel.

“This launch represents a significant expansion of our long-standing relationship with Google Cloud,” said Vasili Triant, CEO of UJET. “Through this managed service approach, we are providing the channel with Google Cloud’s industry-leading, AI-first CX solutions, backed by world-class support and dedicated customer success resources. This motion allows small business and midmarket organizations to easily purchase and adopt the same powerful AI tools used today by the largest global enterprises.”

Trusted Advisors (TA) can now offer Google Cloud CCaaS and the full Gemini Enterprise for Customer Experience (CX) suite to businesses of any size looking to leverage Google Cloud’s leading agentic AI capabilities without the requirement for large Google enterprise agreements or Google Cloud Platform spend commitments.

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“A hyperscaler entering the TSD channel is a turning point for CX. By bringing Google Cloud’s AI-powered platform into the channel exclusively through AVANT, organizations gain both a stable, scalable foundation for innovation and the guidance of a Trusted Advisor to navigate it,” said Andrew Pryfogle, Head of CX/AI and Global Education Lead at AVANT. “With Google Cloud’s Gemini-powered CX stack at the core, this is what an AI-first customer experience truly looks like.”

Bridging the Digital Divide with AI-First CX

The new Google Cloud CCaaS by UJET offering brings Google’s brand recognition, AI innovation and leadership, along with transactional speed and simplicity, to the Trusted Advisor Channel, including:

  • Google Cloud CCaaS – self-service and routing, omnichannel, mobile/digital, conversational insights, analytics, CRM-first and standalone agent desktop.
  • Gemini Enterprise for CX – native Google AI embedded across the end-to-end customer journey.
  • Google CX Agent Studio – low-code/no-code agentic agent builder, supporting shopping and service/support use cases.
  • Google Agent Assist – AI coach, next-best action, real-time transcription, and post-interaction summarization.
  • White-glove customer success managers and technical support – value-added services designed to ensure success for SMB-Midmarket CX buyers.

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“Our goal is to make every business agentic and every customer experience streamlined. By launching this motion with UJET and AVANT, we’re giving SMBs sophisticated AI tools and foundations they need to succeed in the agentic era,” said Darshan Kantak, VP, Applied AI, Google Cloud.

Google Cloud CCaaS by UJET and Gemini Enterprise for CX are available immediately through AVANT and its network of Trusted Advisors. Partners can also offer standalone, over-the-top, point solutions, such as virtual agents, agent assist, and conversational analytics, to provide incremental value to customers on their existing stacks, giving them flexibility to move to the end-to-end solution and CCaaS platform in the future.

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Prosper Insights & Analytics Says AI’s Next Competitive Battleground Is High-Quality, Forward-Looking Data

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Prosper Insights & Analytics Says AI’s Next Competitive Battleground Is High-Quality, Forward-Looking Data

Prosper Insights and Analytics

Forbes article by CEO Gary Drenik and Prosper podcast briefing highlight why reliable signal—not just larger models—will define the next era of AI

Prosper Insights & Analytics is calling attention to what it believes is the most important emerging issue in artificial intelligence: the growing shortage of high-quality, forward-looking data needed to make AI systems more reliable, explainable and economically useful. That perspective is outlined in a recent Forbes article by Prosper co-founder & CEO Gary Drenik, “AI’s Real Bottleneck Isn’t Algorithms, It’s The Rare Earths Of Data,” which argues that AI’s biggest constraint is no longer model architecture alone, but the scarcity of trustworthy data inputs that reflect real human intent and behavior over time.

When data captures consumer intent, confidence and changing behavior before it appears in conventional reports, AI becomes more than an automation tool—it becomes a strategic intelligence asset.”

— Phil Rist, EVP-Strategic Initiatives, Prosper Insights & Analytics

In the article, Drenik compares scarce, high-value data to rare earth elements—inputs that are difficult to source, yet essential to powering modern technology at scale. He explains that many AI systems continue to struggle because they are trained primarily on backward-looking digital exhaust such as clicks, logs, scraped text and transactions, rather than clean, auditable and longitudinal signals that help explain why people make decisions and how they are likely to behave next. Prosper believes this distinction is becoming increasingly important as enterprises push AI from experimental demos into mission-critical forecasting, planning and decision support.

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That same theme is explored in a Prosper Spotify podcast episode, “Prosper Insights AI & Innovation Briefing 2026 E25: The Rare Earth Moment: AI’s High-Quality Data Bottleneck.” As described, the podcast argues that the AI industry’s central challenge has shifted from algorithmic complexity to the scarcity of premium data capable of producing dependable, forward-looking insight. The episode emphasizes that proprietary datasets with longitudinal depth are becoming more valuable than broad open-web scraping when the goal is to predict macroeconomic shifts, market trends and consumer behavior with greater confidence.

“AI will not create durable enterprise value simply because a model is bigger or faster,” said Phil Rist, co-founder of Prosper Insights & Analytics and EVP-Strategic Initiatives. “The real advantage comes from the quality of the signal going in. When data captures consumer intent, confidence and changing behavior before it appears in conventional reports, AI becomes more than an automation tool—it becomes a strategic intelligence asset.”

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Prosper notes that this issue has major implications for businesses, investors and policymakers alike. As accountability, regulation and performance expectations rise, organizations will increasingly need AI systems built on transparent, representative and well-governed data foundations. In Prosper’s view, the winners in the next phase of AI will not simply be those with the largest models, but those with access to scarce, high-integrity datasets that improve prediction, explainability and decision quality over time.

Drenik’s Forbes article also points to the growing market value of proprietary signal-rich data as AI leaders seek more dependable inputs for real-world applications.

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Qualytics Launches Data Control Layer to Govern Context for AI Systems

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Qualytics Launches Data Control Layer to Govern Context for AI Systems

2024 AI & Data Winners - Products That Count

As AI systems take autonomous action, bad data doesn’t just produce bad reports, it drives bad decisions. Qualytics introduces the Data Control Layer to ensure AI systems only act on governed, trustworthy context.

Qualytics, the AI-augmented data quality platform, today launched the Data Control Layer: a new approach to governing the context AI systems reason and act on.

As AI systems move from answering questions to executing decisions, the cost of bad data is no longer limited to inaccurate reports. Bad data now drives automated actions, financial postings, and cross-system workflows at machine speed. Traditional validation models were not designed for this shift: static checks assume predictable data flows, but AI systems retrieve, combine, and act on data dynamically, often without human review.

Qualytics introduces a model it calls validate-at-use, where data is evaluated at the moment it drives decisions. Rather than treating data quality as a set of downstream checks, Qualytics integrates it directly into the context AI systems use to reason and act.

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“The control point for AI has shifted,” said Gorkem Sevinc, Co-founder and CEO of Qualytics. “If validation only happens in data pipelines, you’re already too late. AI systems need to understand whether the context they rely on is trustworthy at the exact moment they reason or act.”

The Qualytics Data Control Layer brings together AI-inferred rules, human-defined policies, anomaly detection, and historical signals into governed, real-time context that can be used by humans, copilots, and autonomous systems alike. Customers today run over 20,000 rules in production on average, with 95% inferred by AI. At its core are three capabilities:

  • Augmented data quality coverage where AI handles scale and humans guide governance
  • A shared foundation for business teams, data teams, and AI systems to define and apply quality
  • Real-time signals that act as controls wherever data is used

The validate-at-use model is designed for how data drives decisions today. Business and data teams work through the Qualytics platform’s purpose-built UX for refining rules, investigating anomalies, and managing governance, with AgentQ adding a conversational interface through natural language. External copilots such as ChatGPT, Claude, and Microsoft Copilot access governed quality signals through Model Context Protocol (MCP), while autonomous systems use the Qualytics API to evaluate data quality and enforce thresholds in real time. Across all interaction models, the same governed context is used.

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“Observability tells you what happened. The Data Control Layer governs what happens next,” said Eric Simmerman, Co-founder and CTO of Qualytics. “We architected quality signals to function as real-time controls that shape how systems behave. That’s not an evolution of observability. It’s a different model entirely.”

As copilots and agents become embedded in core business workflows, the gap between data that’s been validated and data that’s being acted on continues to widen. With the launch of the Data Control Layer, Qualytics establishes a new standard for how data quality operates in the AI era: not as a downstream check, but as a system of controls that governs how AI systems reason and act.

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Astronomer Announces Matt Simontacchi as President of Field Operations

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Astronomer Announces Matt Simontacchi as President of Field Operations

Enterprise open-source veteran brings nearly two decades of Red Hat leadership to scale Astronomer’s go-to-market as Airflow becomes the foundation for AI-driven data engineering

Astronomer, the company behind Astro, the leading unified orchestration platform powered by Apache Airflow®, announced that Matt Simontacchi has joined as President of Field Operations. Simontacchi comes from Red Hat, where he spent nearly 19 years in expanding field leadership roles as the company scaled from an emerging business commercializing open-source to an industry-defining enterprise technology provider with nearly $7 billion in annual revenue. He will report to Pete DeJoy, CEO and Co-Founder.

“Matt knows what it takes to build a high-performing GTM engine around popular open-source technology,” said Pete DeJoy, CEO and Co-Founder, Astronomer. “He is also a hands-on leader who has a passion for rolling up his sleeves and building. As AI reshapes the enterprise data fabric, we’re lucky to have his leadership in evolving Astronomer to ensure we’re doing everything we can to service our customers in this important moment.”

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Simontacchi joined Red Hat during a formative period in the company’s growth, helping to forge its culture and shape the go-to-market strategy that would propel it to become a global leader in enterprise open source. Starting as an Account Executive in 2006, he quickly distinguished himself as a natural leader, builder, and operator. This led him to take on progressively broader leadership roles as the business scaled, ultimately serving as Senior Vice President and General Manager of North America during his final four years, during which he oversaw nearly $3 billion in annual revenue. Throughout his tenure, Simontacchi was at the forefront of translating emerging open-source innovation into enterprise adoption, playing a pivotal role in building the field organization and customer engagement models that helped technologies like Linux and Kubernetes evolve from developer-driven projects into foundational elements of global infrastructure.

“What I learned at Red Hat is that when an open-source community earns the trust of the world’s best engineering teams, something powerful happens,” said Matt Simontacchi, President of Field Operations, Astronomer. “I saw it with Linux, JBoss, and Kubernetes, and the playbook for bringing that value to the enterprise is changing fast. AI is rewriting how companies build, deploy, and operate software. The opportunity at Astronomer is to take what I know about scaling enterprise field organizations and adapt it for a world where reliable, governed data pipelines are the bridge to the enterprise context that makes agentic AI work at scale. Airflow already has that trust, and now our task is to help organizations turn it into their competitive advantage.”

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Simontacchi joins on the heels of Astronomer’s two most successful quarters in company history. The company continues to see accelerating enterprise demand as organizations adopt orchestration as the foundation for getting AI out of prototype and into production, and recently recorded:

55% year-over-year growth
120%+ NRR
122% ARR growth in EMEA
Apache Airflow® has continued its expansion across the enterprise alongside the rise of large language models and agentic workflows:

3,700+ contributors, more than any other Apache Software Foundation project, including Apache Kafka® and Apache Spark®
80,000+ global organizations currently use Airflow, up from 25,000+ in 2020
89% of Airflow users expect to use Airflow for more revenue-generating or external solutions this year, per the State of Apache Airflow 2026 Report, the largest data engineering survey ever conducted

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ShengShu Launches Vidu Q3 Reference-to-Video with Expanded Visual and Audio Capabilities

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ShengShu Launches Vidu Q3 Reference-to-Video with Expanded Visual and Audio Capabilities

Alibaba Leads RMB 2 Billion Series B to Support General World Model Development

ShengShu Technology, a global leader in multimodal generative AI, announced the global launch of Vidu Q3 Reference-to-Video, a new model capability designed for story-driven video creation through flexible reference-based generation.

Built for storytelling, Vidu Q3 Reference-to-Video enables creators to generate high-quality videos by referencing and combining a wide range of inputs—including subjects, environments, costumes, props, and visual styles—within a single workflow, significantly improving creative control, consistency, and efficiency.

The release expands capabilities across visual effects, audio, and scene composition. It supports six types of cinematic visual effects, including particle systems, fluid simulation, dynamic motion, camera movement, transitions, and lighting, enabling more expressive visual outputs. In parallel, the model enhances audio generation with five categories of sound capabilities for more natural and expressive results, covering ambient sound, motion-driven audio, atmospheric layers, foley effects, and emotion-driven cues. Together, these improvements enable greater scene diversity and more immersive, production-ready video outputs.

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Designed for use cases including short-form series, animation, film and television, as well as advertising and e-commerce, Vidu Q3 Reference-to-Video enables faster production of high-quality video content for both creators and enterprises. This performance is further reflected in third-party benchmarks, where Vidu Q3 ranked No.1 in the first global Reference-to-Video leaderboard released by SuperCLUE.

Built on the Vidu Q3 model foundation, Vidu has been fully integrated across its product ecosystem, including Vidu Agent, Vidu Claw, and the Vidu App. This creates a unified system supporting the full workflow from creative ideation and content production to deployment, enabling greater efficiency and consistency across diverse use cases.

The release reflects ShengShu’s broader progress in advancing its world model capabilities across digital environments.

In parallel with the product release, ShengShu announced it has raised RMB 2 billion in its Series B financing round, led by Alibaba Cloud, with participation from Andon Haitang, China Internet Investment Fund, TAL Education Group, Luminous Ventures, and others. Existing investors, including LINK-X CAPITAL, Delta Capital, and Baidu Ventures, also increased their investment.

The funding will support ShengShu’s broader vision of building a general world model that bridges the digital and physical worlds. The company is advancing both its World Generation Model (WGM), which powers digital content creation through the Vidu model family, and its World Action Model (WAM), designed for physical-world interaction. Together, these systems aim to enable unified modeling, prediction, and action across environments.

ShengShu is among the first globally to pursue a unified world model architecture that connects digital and physical domains. At the core of this system is its Foundation World Model, which underpins both WGM and WAM.

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Within this framework, the Vidu model family focuses on content generation and interaction in digital environments. It supports synchronized audio-visual generation, extended video duration, strong temporal and spatial consistency, and cinematic-quality visuals. Its proprietary reference-based video generation capability addresses consistency challenges in multi-subject video production.

At launch, Vidu Q3 ranked No.1 globally on the benchmark published by Artificial Analysis. It supports up to 16 seconds of synchronized audio and video generation, multi-shot composition and camera control, background music and sound effect generation, and multilingual dialogue. The launch of Reference-to-Video further expands its capabilities across visual effects, audio, and application scenarios.

Vidu is available to global developers, creators, and enterprises through both MaaS (Vidu API platform) and SaaS offerings, and has been integrated into Alibaba Cloud Model Studio to support text-to-video, image-to-video, and reference-to-video generation across industries including internet, advertising, film and animation, education, and cultural tourism.

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Salient Systems Named 2026 Video Surveillance Platform of the Year by Enterprise Security Magazine

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Salient Systems Named 2026 Video Surveillance Platform of the Year by Enterprise Security Magazine

Salient Systems

Salient Systems wins 2026 Video Surveillance Platform of the Year from Enterprise Security Magazine for its CompleteView VMS platform.

Salient Systems, a leading provider of enterprise video management software, today announced it has been named the 2026 Video Surveillance Platform of the Year by Enterprise Security Magazine. The recognition acknowledges Salient’s CompleteView VMS platform for delivering context-driven video management that strengthens security and supports operational decision-making at scale.

Enterprise Security Magazine’s 2026 award program recognized Salient for its ability to unify video, access control, and system data into a single operational view — spanning on-premises, cloud, and hybrid environments. The platform’s open architecture supports more than 25,000 conformant cameras and devices, and its hybrid-cloud design allows organizations to extend existing infrastructure without displacing it.

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“We’re not in the business of creating alert fatigue. Our goal is situational awareness that helps organizations respond with intent — acting decisively when needed, and staying out of the way when it’s not.”

— Stacey Steiger, VP of Product, Salient Systems

The award underscores Salient’s disciplined approach to enterprise VMS design. The platform correlates video footage with access credentials, activity history, and system data so operators can act on real incidents rather than respond to noise. Governance features including Active Directory-based role control ensure users have access only to data relevant to their responsibilities.

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Salient’s commercial model reflects how modern enterprises plan and purchase technology. Per-camera pricing with no hidden fees, perpetual license options for capital investment, and subscription models for OPEX-aligned IT organizations ensure the platform serves both traditional security teams and the IT departments increasingly taking ownership of physical security infrastructure.

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Commvault Introduces Innovations to Advance Secure, Controlled Agentic Transformation in the Enterprise

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Commvault Introduces Innovations to Advance Secure, Controlled Agentic Transformation in the Enterprise

Next-generation AI capabilities will leverage Commvault Cloud to safely activate AI and build agentic workflows with trusted data, governance, and recovery

Commvault, a leader in unified resilience at enterprise scale, announced new and forthcoming AI capabilities that help organizations confidently adopt AI while maintaining control over data, agents, and recovery. These capabilities will enable enterprises to activate AI safely, discover and govern AI agents, and build and control agentic workflows, all from Commvault Cloud.

As organizations deploy AI, many struggle to balance innovation with the potential exposure of sensitive data and losing control over AI agents. According to Deloitte, 60% of AI leaders cite risk and compliance concerns and legacy system integration as the top barriers to agentic AI adoption.1 Building on AI resilience, Data Activate, AI Protect, and AI Studio will address these challenges by helping teams understand the impact of agent-driven changes and roll back when necessary.

“Every enterprise era has produced a system of record – ERP for business operations, CRM for customers, and now AI for the enterprise,” said Sanjay Mirchandani, President and CEO, Commvault. “If data powering AI is compromised, AI is compromised. If data can’t be recovered, AI can’t be trusted. Commvault Cloud is the system of record for AI resilience.”

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Activating Data Safely for the Agentic Era
Data Activate enables organizations to classify and curate data from protected backup copies and prepare approved datasets in formats like Apache Iceberg and Parquet for use with large language models and AI data platforms. The offering continuously publishes updated and vetted datasets, making it easier to keep AI pipelines in sync with trusted data. For example, teams can identify and exclude personally identifiable information before activating datasets for model development. Built on Commvault Cloud’s governance and zero-trust architecture, Data Activate helps reduce the possibility of exposing sensitive data through AI while enabling safe, controlled data activation.

Extending Next-Level Control to Agentic AI
AI Protect will help organizations identify vulnerabilities, understand the impact of agent-driven changes, recover affected applications, and perform full-stack recovery across AI-driven environments. As the application stack evolves and more agents can enact unwanted and unbounded changes across data, systems, and configurations, organizations need a unified approach to manage both complexity and threats. AI Protect will discover and inventory agents across environments and map their activity to AI stacks.

Unlocking a New Era in Agentic Automation 
AI Studio will enable organizations to create and utilize agents that address their specific needs, including a repository of built-in agents for common resilience use cases. Teams will be able to build custom agents that automatically and securely utilize Commvault’s Model Context Protocol (MCP) server and integrate with other enterprise systems.

With these innovations, teams will continue to advance an AI resiliency lifecycle that starts with safely activating data for AI purposes all the way through bringing discovery, governance, protection, and recovery to AI agents across on-prem, SaaS, and hybrid cloud environments.

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“As we deploy AI agents across our organization, a key concern will be maintaining visibility and control over what these agents can access and how they interact with our sensitive data,” said Marius Horja, CCoE Compute Architecture and Engineering with Emerson Electric. “Having the ability to view, manage, govern, and orchestrate our agent ecosystem in real time from a single platform will give us greater confidence to scale AI innovation without sacrificing safety or resiliency.”

“As enterprises accelerate AI adoption, they need solutions that reduce risk while delivering real business value,” said Sean Alexander, SVP Connected Ecosystem, Lumen. “Our partnership with Commvault, including our Lumen Validated Design for Cyber Resilience, brings together trusted cyber and AI resilience from Commvault with secure, scalable connectivity from Lumen—helping customers deploy with confidence and innovate safely in the agentic era.”

“In agentic environments, agents mutate state across data, systems, and configurations in ways that compound fast and are hard to trace,” said Pranay Ahlawat, Chief Technology and AI Officer, Commvault. “When something goes wrong, teams need to recover not just data, but the full stack — applications, agent configurations, and dependencies — back to a known good state. That’s what AI Protect delivers.”

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Qlik and ServiceNow Partner to Bring Trusted Enterprise Context into AI-Powered Workflows

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Qlik and ServiceNow Partner to Bring Trusted Enterprise Context into AI-Powered Workflows

Qlik

New collaboration helps enterprises connect trusted enterprise context, richer insight, and smarter action across ServiceNow workflows and AI agents

Key takeaways:

  • Trusted context and insight move closer to workflow execution: Qlik and ServiceNow are partnering to help enterprises combine ServiceNow Workflow Data Fabric with broader, governed enterprise context so workflows and AI agents can act with even better judgment.
  • Strengthened governance and discovery inside ServiceNow: New Qlik metadata collectors for ServiceNow Data Catalog will help customers improve visibility into data lineage, movement, and structure across their environments.
  • The path from insight to action gets stronger: The Qlik Analytics® Engine and AI can combine ServiceNow Workflow Data Fabric with broader enterprise signals to surface relationships, patterns, and recommendations that support more intelligent action.

Qlik announced a new partnership with ServiceNow designed to help enterprises bring trusted enterprise context and richer insight into the workflows and AI-driven processes where decisions turn into action.

As organizations push AI deeper into operations, one opportunity stands out: workflows and agents that act with richer context deliver better outcomes. ServiceNow Workflow Data Fabric provides a powerful foundation — and the operational picture gets even stronger when paired with signals from ERP, CRM, billing, supply chain, support, and other systems. When those connections come together, teams can surface important relationships, respond faster, and act with greater confidence.

Qlik and ServiceNow are building on that opportunity by strengthening the connection between governed enterprise data, better insight, and workflow execution. Qlik brings the ability to combine ServiceNow Workflow Data Fabric with broader enterprise context, use the Qlik Analytics Engine and AI to surface patterns and relationships across systems, and help customers identify what should happen next. The partnership extends that power further by adding a robust governance and discovery layer inside ServiceNow through new Qlik metadata collectors for the ServiceNow Data Catalog.

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“Workflows and AI agents are being asked to do more than route work. They are being asked to interpret business conditions and act with better judgment,” said James Fisher, Chief Strategy Officer, Qlik. “That takes more than system data on its own. It takes the ability to combine ServiceNow signals with broader enterprise context, apply analytics and AI, and feed that intelligence back into the workflow where action happens.”

The partnership reflects a broader shift underway in enterprise software. Companies want AI that fits inside the systems where work already happens, but they also want those systems to operate with a fuller understanding of the business. That means connecting workflow execution to governed context, explainable insight, and signals drawn from across the enterprise, not just one application view.

“The decisions people and agents make every day are only as good as the data behind them,” said Pramod Mahadevan, VP, Data & Analytics Product Ecosystem, ServiceNow. “Our partnership with Qlik connects those insights from third-party data directly to action inside ServiceNow, extending the reach of Workflow Data Fabric to the systems where critical data already lives. The result: people and agents that act on trusted, governed intelligence, and decision-ready data in the workflows where work gets done.”

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What’s new

  • Qlik metadata collectors for ServiceNow Data Catalog: ServiceNow Data Catalog now offers Qlik metadata collectors that integrate with a broad range of Qlik products, improving discovery, lineage visibility, and governance for shared data assets.
  • Analytics and AI that strengthen action: Qlik’s analytics engine sends insights directly into ServiceNow workflows and agents, improving decision-making with cross-system relationships, emerging patterns, and operational context—helping teams act with greater confidence.

For enterprises, the significance of the partnership is not just greater data visibility inside ServiceNow. It is the ability to pair workflow execution with insight derived from a broader enterprise context. By connecting governed data, stronger analysis, and operational workflows more directly, Qlik and ServiceNow are helping their customers create a more practical path from enterprise intelligence to enterprise action.

This announcement is part of a broader set of releases at Qlik Connect 2026, Qlik’s annual customer and partner event, where the company is outlining a more complete approach to enterprise AI across agentic analytics, trusted data foundations, operational trust, and deployment architectures designed for real-world constraints.

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