VERB Products, the salon-quality haircare brand known for one accessible price point across its lineup, has selected Listrak as its unified cross-channel marketing partner. The move aims to boost e-commerce performance and deepen customer engagement through a single AI-powered platform for email, SMS, and web personalization.

The partnership has already delivered early wins. VERB reports a 300% year-over-year increase in automations and a 30% lift in SMS conversions since integrating Listrak’s tools. The gains reflect a broader shift among beauty brands toward data-driven personalization, where seamless communication across channels is now table stakes for growth.

AI-Driven Personalization at Scale

Listrak’s platform brings data, identity resolution, and predictive intelligence under one roof. VERB now uses the system to power hyper-personalized experiences across email campaigns, SMS flows, and on-site interactions. That includes predictive product recommendations and dynamic content tailored to individual customer behavior.

The brand also collaborates closely with Listrak’s beauty industry specialists, using benchmarks and trend insights to sharpen campaign execution. This mix of platform automation and expert guidance helps VERB scale personalization without adding operational burden.

Nicole Johnson, VERB’s Digital Marketing Director, said the mission is simple: help customers feel confident while discovering products that fit their unique style. She emphasized the value of having both an integrated platform and a hands-on partner to deepen retention and CRM strategy.

A Collaborative Push Toward Loyalty

Listrak’s CRO, Jamie Elden, noted that VERB’s focus on individuality extends from its diverse product range to its online shopping experience. The company’s role is to elevate those journeys with smarter targeting and more cohesive messaging across touchpoints. As beauty e-commerce gets increasingly competitive, consistent personalization becomes a key differentiator for loyalty.

Fueling Growth With Creative Digital Campaigns

VERB’s momentum also stems from inventive digital campaigns that reinforce brand identity. One example is Ghost Month, an October-long interactive experience celebrating the brand’s cult-favorite Ghost Oil and the wider Ghost collection. The series blends storytelling, product education, and playful digital engagement—an approach Listrak’s tech now helps amplify.

As the holidays approach, VERB plans to roll out high-intent campaigns including Black Friday promotions and its annual December Mystery Box event. With Listrak’s automation and predictive targeting in place, the brand is positioned to drive stronger conversions during peak shopping season.

VERB’s partnership with Listrak signals a broader trend: beauty brands leaning into unified automation platforms that merge intelligence, creativity, and cross-channel execution. With early results already hitting triple-digit lifts, VERB appears set to scale faster while keeping personalization at the center of every customer interaction.

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FICO is pushing credit scoring into new territory. The analytics giant revealed a strategic partnership with Plaid to introduce the next generation of the cash flow–enhanced UltraFICO Score, designed to give lenders deeper, real-time insight into consumer financial health. Instead of relying solely on traditional credit metrics, lenders can now tap live transaction data to make faster, sharper, and more inclusive decisions.

The move builds on FICO’s earlier UltraFICO Score, but this version raises the stakes. Plaid’s infrastructure connects to more than 12,000 financial institutions and processes nearly one million secure financial connections every day. By merging that reach with the reliability of the FICO Score—still used by 90% of top U.S. lenders—the two companies aim to deliver a unified score that strengthens risk assessment without adding operational drag.

Cash Flow Intelligence Becomes a Core Credit Signal

The enhanced UltraFICO Score analyzes inflows and outflows across checking, savings, and money market accounts. This gives lenders real-time visibility into stability, spending behavior, and liquidity—critical factors for understanding credit readiness, especially among consumers who fall outside traditional scoring models.

FICO’s vice president and general manager of B2B Scores, Julie May, highlighted the market’s demand for broader credit perspectives. She said the partnership represents nearly a year of work focused on creating “the foundation for more comprehensive lending decisions.” The collaboration marks a shift toward more responsible and inclusive scoring at a time when lenders are eager to expand access without compromising precision.

Easier Integration, Faster Adoption

The updated UltraFICO Score will be distributed through Plaid’s Consumer Reporting Agency, Plaid Check, which streamlines onboarding for lenders. The companies say the model aligns with the flagship FICO Score, allowing institutions to adopt cash-flow-enhanced scoring without lengthy testing or risk-model overhauls.

Lenders also gain universal compatibility, so they can use the enhanced model alongside the traditional FICO Score in any channel. This flexibility reduces friction and clears the path for faster implementation across underwriting workflows.

A Step Toward Smarter, More Inclusive Lending

Plaid’s head of partnerships, Adam Yoxtheimer, emphasized the rising importance of real-time financial data. He noted that high-quality cash flow visibility is becoming essential for lenders looking to capture a fuller picture of consumer credit readiness. The combined score gives institutions stronger risk signals while offering borrowers more ways to demonstrate financial strength.

With banks under pressure to innovate, modernize underwriting, and reach underserved markets, this partnership positions FICO and Plaid at the center of a major shift. The next-generation UltraFICO Score aims to make lending both smarter and fairer—without complicating the systems lenders rely on.

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Skybeam, Simulmedia’s self-serve TV advertising platform, just made one of the industry’s most stubborn barriers disappear. The company launched Traditional TV Advertising inside its platform, allowing users to run broadcast and cable campaigns alongside streaming—without the old maze of rate cards, ad reps, and minimum budget requirements.

The move brings digital-style simplicity to linear TV, a channel that still commands significant attention. More than 40% of U.S. viewers continue to watch traditional television, especially for local news, primetime entertainment, and live sports. Yet smaller advertisers have rarely been able to tap into that audience because linear TV has long favored large brands with deep pockets and specialized teams.

A Unified Path to Streaming and Linear

Skybeam now positions itself as the first self-serve platform to merge Streaming + Traditional TV buying in one workflow. Users simply pick their market, set a schedule, define a budget, and upload a spot. The platform then handles planning, placement, and optimization—tasks that previously required expertise or expensive intermediaries.

The update leverages Simulmedia’s 15 years of national TV buying experience, turning what used to be a complex transaction into a straightforward campaign setup. For many local advertisers, that shift could unlock audiences they’ve never been able to reach.

Closing the Accessibility Gap

Until now, traditional TV buying created four persistent hurdles:

• Complex negotiations with reps and opaque rate structures

• High entry budgets that sidelined local players

• Limited expertise to plan and optimize linear campaigns

• Fragmentation between streaming and linear buying tools

Skybeam’s streamlined experience removes these friction points and gives local businesses real access to premium TV inventory. It also helps agencies consolidate workflows that previously required juggling multiple vendors.

A Level Playing Field for TV

Simulmedia CEO Dave Morgan described the launch as an overdue shift. “For years, access to traditional TV advertising was limited to large brands with big budgets,” he said. Skybeam now extends that reach to smaller businesses “with the same power and precision we’ve provided national advertisers for over a decade.”

For advertisers still chasing trust and high engagement, traditional TV remains hard to beat. The channel continues to hold some of the most attentive audiences in U.S. media. Skybeam’s update brings that value within reach of local brands that have long viewed TV as off-limits.

The platform is available now. Users can sign up, set up a campaign, and get their brand on-air in the same market as major national advertisers—no negotiations, no contracts, and no steep buy-ins.

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Qlik is tightening its grip on the data quality race. The company unveiled a fresh set of AI-powered capabilities in Qlik Talend Cloud, designed to make governed, trusted data easier for enterprises to use wherever work happens. The update offers what many data teams have long wanted: faster publishing, cleaner datasets, and fewer hours swallowed by tedious documentation.

The headline feature is a one-click ability to publish secure, standards-based API endpoints for governed data products. This unlocks a wider ecosystem, letting teams push the same curated data directly into Power BI, Tableau, Excel, Salesforce, and custom internal apps without creating duplicate pipelines. For organizations already drowning in data silos, that alone is a lifeline.

The platform also introduces automated dataset documentation through a feature Qlik calls auto-describe. It generates field-level descriptions at scale, improving data discoverability for business users and eliminating the manual busywork that data stewards have endured for years.

But the biggest productivity kicker may be the AI-powered data quality assistant, which analyzes datasets and proposes validation rules based on their profile. Instead of manually crafting checks, teams can now cover far more scenarios while spending far less time building logic.

Qlik didn’t stop at cleanup tools. The release includes agentic, sprint-style remediation workflows that bring domain experts, analytics leaders, and data stewards together. Rather than funneling issues through endless tickets, teams can collaborate in real time to validate fixes, raise trust, and accelerate delivery.

“Customers want flexibility. If your best data is stuck in one tool, it becomes a bottleneck,” said Drew Clarke, EVP of Product & Technology at Qlik. He emphasized that standards-based APIs reduce friction across the stack, while AI-driven stewardship “removes repetitive tasks and helps teams deliver trusted outcomes faster.”

Eva Chrona, CEO of Climber, put it more bluntly: “Qlik has turned stewardship into a team sport.” Her team has already seen time savings from auto-describe and AI-generated quality checks, and she expects AI-guided workflows to deepen that impact.

Qlik says the new Data Product APIs are available today. Auto-describe and the DQ Rule Assistant are rolling out, and an early access program for enhanced data stewardship features is open now. The move puts Qlik in a strong position as enterprises push for unified, governed data that can move across tools without losing trust—or time.

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Clootrack just crossed a threshold that few enterprise AI platforms can claim: more than 100 billion OpenAI tokens processed across its customer base. The milestone signals a shift in how global organizations approach Voice of the Customer (VoC) analytics, pushing beyond dashboards and sentiment scores into real operational impact.

The spike in usage follows two major upgrades. In 2024, Clootrack migrated its patented unsupervised thematic analysis engine to OpenAI. Then, in early 2025, it launched an Agentic Workflow Builder designed specifically for VoC teams. Combined, these enhancements have turned customer feedback—long treated as a noisy afterthought—into a high-resolution decision system.

Clootrack CEO Shameel Abdulla frames the achievement as a trust signal rather than a technical one. “This achievement is not about token volume; it is about trust at scale,” he said. According to Abdulla, enterprises are rebuilding their decision loops around AI, and Clootrack’s accuracy claims of 98% or higher make that shift possible.

And the outcomes are hard to ignore. Brands report double-digit reductions in ecommerce returns, NPS improvements within a single quarter, and threefold acceleration in product development. Contact centers have lowered average handling time by as much as 15%, while some teams cut agent churn by 20%. Private equity firms have even shortened diligence cycles by weeks. These gains illustrate a broader trend: enterprise AI is now graded on business results, not novelty.

Reaching the 100-billion-token mark didn’t come easy. Clootrack rebuilt core algorithms, workflows, and internal development systems to meet enterprise expectations for transparency and control. Abdulla said the team “hit walls almost every day,” but precision remained non-negotiable. The milestone suggests the rebuild paid off.

The platform now blends unsupervised thematic analysis with agentic AI workflows to interpret emotion, context, and intent across 55+ languages and more than 1,000 data sources. Retail giants, SaaS leaders, banks, healthcare providers, and private equity firms rely on Clootrack to unify scattered feedback into real-time intelligence that supports faster decisions and measurable growth.

While many AI-powered CX tools still offer surface-level sentiment snapshots, Clootrack’s trajectory shows where the market is heading. Enterprises want clarity, impact, and systems that scale without breaking. Surpassing 100 billion tokens is less a victory lap and more a preview of what the next generation of customer intelligence will look like.

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In the era of agentic AI—where autonomous systems rely on constant, high-quality, contextual data—data observability isn’t a nice-to-have anymore. It’s survival gear. Telmai, the AI-powered data quality and observability platform, is stepping into that gap with a new partnership aimed squarely at Microsoft Fabric users.

The company announced that its data reliability engine now integrates natively with Microsoft OneLake, bringing real-time monitoring, validation, and trust signals directly into the heart of the Fabric ecosystem. The result: faster insight, fewer broken pipelines, and analytics models that don’t need a rescue mission every time the data shifts.

Why This Partnership Matters Now

Organizations building agentic AI and real-time analytics systems face a fundamental bottleneck: traditional data validation isn’t built for low latency, distributed architectures, or constant context shifts. Fabric users—many of whom are already grappling with data spread across domains—need observability that keeps pace with the speed of automation.

Telmai is positioning its platform as an answer to that shift. Rather than validating data downstream—after it hits dashboards or AI workflows—it monitors and checks data as soon as it lands in OneLake, across structured, semi-structured, and even unstructured formats.

CEO and co-founder Mona Rakibe puts it bluntly: “Ensuring data reliability is no longer optional—it’s table stakes.” For agentic AI, where decisions happen autonomously and instantly, bad data isn’t just costly; it’s dangerous.

What’s New: Real-Time, Source-Level Validation in Fabric

Telmai’s integration with OneLake brings a few capabilities that stand out:

1. Continuous Validation at the Source

Data is checked the moment it arrives in OneLake—catching anomalies before they propagate into dashboards, models, or downstream apps. This ensures Fabric users can maintain low-latency access to validated, contextualized data, eliminating blind spots that slow decision-making.

2. Custom Business Logic and Alerting

Telmai’s engine allows teams to configure their own validation rules, anomaly detection thresholds, and alerting policies. Rather than generic “something broke somewhere” notifications, users get targeted, actionable insights tied to business context.

3. AI-Powered Data Reliability Agents Across Fabric

Here’s where Telmai differs from traditional observability tools: its Data Reliability Agents allow both technical and non-technical users to query issues, troubleshoot anomalies, and deploy monitoring policies using plain-language commands.

This decentralized model is critical for Fabric’s domain-first architecture, reducing the burden on engineering teams and making data trust a shared—and accessible—capability.

4. Contextual Explanations for Root Causes

Instead of dumping a list of anomalies on data teams, Telmai provides explanations and supporting context about why issues occurred. Faster troubleshooting means shorter time-to-resolution and less operational drag on analytics pipelines.

The Fabric Landscape—and Telmai’s Place in It

Microsoft Fabric has quickly become a central hub for enterprises consolidating analytics, governance, and AI workloads. But this consolidation raises the bar for data quality: errors travel farther, faster, and into more systems.

Telmai’s integration signals Microsoft’s growing emphasis on vetted, explainable, production-ready data. Dipti Borkar, VP & GM of Microsoft OneLake & ISV Ecosystem, noted that accuracy and trust are “critical to the success of any analytics and AI project,” emphasizing that Telmai’s capabilities help users “quickly and easily build AI-ready, trusted data products.”

In a market filled with observability contenders—Monte Carlo, Bigeye, Soda, Databand—Telmai is carving out a space that leans heavily into AI explainability and domain-level trust, aligning closely with Fabric’s own architectural philosophy.

The Bottom Line

Agentic AI won’t tolerate laggy, inconsistent, or context-poor data. Telmai’s partnership with Microsoft is a strategic play to make Fabric not just a unified analytics platform, but a trusted one—with real-time validation baked in at the source.

For enterprises scaling AI-driven analytics, this integration may prove to be not just a convenience but a competitive necessity.

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Customer support bots are everywhere now—but most of them still suffer from goldfish-level memory. Sendbird wants to fix that. The company today launched Delight.ai, a branded AI concierge designed to remember every interaction, follow customers across channels, and actually act on behalf of a brand. Think of it as a customer support agent that doesn’t forget you the moment the chat window closes.

Sendbird, which already powers conversations for more than 300 million people per month, says Delight.ai is meant to be deployed anywhere customers communicate: in-app chat, voice, SMS, email, and social channels. The draw? Long-term memory that adapts, anticipates, and personalizes over time—something most AI agents don’t even attempt.

Why This Matters Now

Consumers have made their preferences clear: 62% now choose automated support over waiting for a human, and 75% of service leaders are increasing their AI budgets this year. If customer experience is a revenue engine—and for many brands it is—the AI servicing it can’t be amnesiac.

Most AI support systems are reactive, instantly forgetting conversation context and forcing users to repeat themselves across channels. Not only is that inefficient, it’s a fast track to customer churn. Sendbird argues that Delight.ai shifts the equation from transactional service to proactive, memory-driven engagement.

CEO John Kim doesn’t mince words: conventional AI agents “fail customers,” he says, limiting trust and revenue. By contrast, Delight.ai aims to deliver “personal, present and trustworthy” experiences—less chatbot, more concierge.

What’s New: How Delight.ai Works

Sendbird positions Delight.ai as the first branded AI concierge built on long-term memory, anchored around three strategic pillars:

1. Persistent Memory That Actually Persists

Instead of relying on static CRM records or short-lived session data, Delight.ai absorbs signals from every interaction—actions, preferences, behaviors—to build an evolving customer profile. The promise: personalization that matures over time rather than resetting with each ticket.

2. Omnichannel Continuity and Proactive Follow-Up

Switching from SMS to chat mid-conversation? Delight.ai carries context with you. Drop off halfway through a conversation? It proactively re-engages. This continuity is key for brands juggling multiple touchpoints—and tired customers who hate repeating themselves.

3. Enterprise-Grade Governance via Trust OS

Concerns about AI autonomy? Sendbird has an answer: Trust OS, a governance layer offering observability, policy controls, traceability, and guardrails. The pitch is clear—give your AI agent autonomy, but never let it color outside the brand lines.

A Real-World Proof Point

Hanssem Furniture, an early adopter, claims Delight.ai now nails 90% of first-touch engagements and delivers interactions that feel “natural,” according to CEO Eugene Kim. The metric that matters: customers “feel remembered”—a rarity in today’s fractured support landscape.

Where Delight.ai Fits in the Market

AI support tools like Intercom Fin, Zendesk’s AI agent, and Ada have pushed personalization and efficiency forward—but none emphasize persistent, customer-specific memory as a core feature. That’s where Sendbird is positioning its differentiator.

If Delight.ai delivers on its promise, it could redefine what brands expect from their AI agents—moving from fast responses to relationship-driven engagement that impacts lifetime value.

Who It’s For—and What Comes Next

Delight.ai is available now for mid-market and enterprise companies across retail, travel, on-demand services, SaaS, fintech, and healthcare. Because it can work across the full lifecycle—sales, marketing, support, loyalty—it’s pitched as a revenue driver, not just a support tool.

The bigger question is whether persistent-memory AI becomes the new standard in customer experience. If it does, Delight.ai may have arrived right on time.

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In the world of investment management, clean data has always been the dream; usable data, a luxury; and conversational data? Practically science fiction—until now. Rivvit Inc., known for its data management and reporting tools used by investment firms, is launching an AI-powered virtual analyst designed to let professionals query their portfolios, documents, and reports as casually as talking to a colleague.

If it works as advertised, Rivvit isn’t just bolting AI onto old infrastructure. It’s positioning itself as a pioneer of “explainable, governed AI” in an industry where messy data is often the single biggest obstacle to automation.

Why This Matters: AI Only Works If the Data Does

Generative AI has flooded nearly every corner of finance, but the industry’s biggest pain point hasn’t changed: garbage in, garbage out. Rivvit CEO Matt Biver is leaning directly into that problem.

“Data is the fuel for AI,” he says. “But AI only works when the data beneath it is clean, organized, and reliable.”

That’s where Rivvit’s long-standing pitch comes into play. The company already centralizes, validates, and governs investment data across portfolio management systems, custodians, internal documents, and reporting workflows. Now the same infrastructure powers a conversational layer capable of answering natural language questions.

This stands in sharp contrast to generic AI copilots that operate on loosely connected data lakes or static documents. Rivvit’s point of differentiation: a fully governed, institution-grade data backbone that ensures answers are trustworthy and traceable, not “AI guesses dressed up as facts.”

The Virtual Analyst: A New Interface for Investment Intelligence

Rivvit’s virtual analyst can handle a variety of investment tasks without requiring SQL skills, BI dashboard builds, or specialized reporting knowledge. Users simply ask:

• “How has our allocation to global equities shifted over the last three quarters?”

• “Explain the change in AUM for Fund X.”

• “What are the emerging risk exposures across the portfolio?”

• “Pull notable performance trends for tomorrow’s investment committee.”

The platform promises conversational intelligence layered over deterministic, governed data—something that’s rare even among modern data-focused fintech firms.

In practice, the system touches nearly every functional group in an investment organization:

• Portfolio managers get allocation, attribution, and macro trend insights.

• Risk teams get immediate explanations behind anomalies and performance swings.

• Operations and accounting get fast reconciliation and AUM movement analysis.

• Executives and committee members get instant briefings and narrative summaries.

It’s essentially the pitch: Why wait for next week’s reporting cycle when you could ask a question right now?

Filling the Gaps Left by BI and Reporting Tools

For years, asset managers have stitched together dashboards, spreadsheets, SQL queries, and static PDF reports. The result: fragmented visibility and heavy analyst workloads spent preparing (not analyzing) data.

Rivvit argues that the virtual analyst doesn’t replace analysts or BI tools—it eliminates the tedious layers between business questions and answers.

This marks the next step in the company’s five-stage data evolution:

1. Data foundation — unify and clean data
2. Reliable reports — provide validated, consistent output
3. Governance — track lineage, quality, and availability
4. Trusted queries — enable self-service exploration
5. AI intelligence — layer natural language understanding on top

Most vendors try to start at Stage 5, leaving clients to untangle their messy foundations. Rivvit is taking the opposite route: build the plumbing first, then build AI.

It’s a difference that institutional investors will not overlook.

Competitive Landscape: A Race Toward Explainable AI in Finance

Rivvit’s move comes as investment managers increasingly experiment with generative AI—JPMorgan is building investment copilots, BlackRock is investing heavily in AI models, and dozens of emerging fintechs promise AI-enabled insights. But many of these tools rely on static or incomplete data, and few integrate with existing pipelines deeply enough to guarantee reliability.

Rivvit’s strength is that it lives inside the data layer itself. It doesn’t just access data; it governs it.

That’s a meaningful differentiator in an industry where regulators expect explainability and firms expect precision.

The Future: AI as the Reward for Doing Data Right

Biver puts it bluntly:
“AI isn’t the end of the data journey. It’s the reward for doing data right.”

By that logic, Rivvit’s virtual analyst is less a feature launch and more a culmination of years of infrastructure work. It also signals a broader shift—investment firms no longer want analytics tools that require technical expertise. They want natural language, fast answers, and reliable data.

If Rivvit can deliver all three without compromising accuracy, it could set a new benchmark for AI-enabled data intelligence in financial services.

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