Zappi, the consumer insights platform known for speeding up market research, has launched its Brand Health Tracker—a continuous, always-on measurement system designed to give marketers a clearer, faster read on how consumers respond to their brand. The tool promises monthly sentiment data, a unified view of advertising and innovation impact, and a price point aimed at teams tired of paying enterprise premiums for outdated tracking.

The timing makes sense. With consumers moving faster than legacy research cycles can handle, modern brand teams need systems that match that pace. Traditional trackers, often expensive and inflexible, tend to give marketers snapshots instead of the full story. Zappi’s solution aims to flip that dynamic by offering 12 measurement waves per year at a cost 40% lower than typical research models—an advantage supported by an independent study.

Brand Tracking That Doesn’t Fall Behind

In an era where 75% of U.S. consumers are trading down to cheaper products and 71% expect personalized experiences, staying connected to consumer behavior isn’t optional. Yet most tracking programs update quarterly or even annually, creating blind spots at a time when brand sentiment can shift week to week.

Zappi’s Brand Health Tracker is built to cover those gaps. It offers monthly data with at least 400 responses per cycle, providing a read on awareness, consideration, loyalty, and key category behaviors. The tracker collects data autonomously once a survey is built, giving teams the choice to check in every month or take broader quarterly or biannual views.

A Continuous View of Brand Evolution

CEO Aaron Kechley said marketers have been stuck with expensive trackers that freeze moments in time, while real consumer behavior keeps evolving. His pitch: combine brand health, advertising, and innovation testing in one ecosystem, giving brand teams the ability to run more tests, react faster, and double down on what works.

A key differentiator is Zappi’s alignment with principles from the Ehrenberg-Bass Institute for Marketing Science. Rather than tracking standalone KPIs, the system monitors category entry points—those contextual triggers that shape consumer choices. By tying brand data to these moments, companies get a clearer view of how everyday marketing activity influences long-term brand growth.

Features Built for Modern Insights Teams

Zappi’s Brand Health Tracker offers several functions designed to streamline research while keeping costs low:

• Continuous data collection: Real-time monitoring with monthly samples of at least 400 responses. Trend shifts become visible sooner, giving teams more space to act.

• Competitive benchmarking: Up to 15 competitors can be tracked in parallel for ongoing market comparison.

• Self-serve setup: Pre-built templates and automated data flows simplify onboarding and reduce internal bottlenecks.

• Professional services: Optional consulting support helps teams pull deeper insights from months or years of data.

For brands that don’t need ongoing tracking, Zappi also offers a Brand Health Snapshot, a single-time measurement solution designed for quick reads after a major campaign or sudden market change.

A Market Ready for Faster Tracking

The launch arrives as the research industry pushes toward automation and cost efficiency. Many platforms offer speed or affordability—but not both. Zappi is positioning its tracker as a rare middle ground: fast enough to keep up with shifting consumer trends while affordable enough for continuous use rather than one-off budget allocations.

For marketers under pressure to justify spend, the promise of lower research costs and more frequent data points could be compelling. As competition intensifies and consumer behavior becomes less predictable, continuous tracking may soon shift from a luxury to a necessity.

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Storyblocks is pushing into the AI era—carefully. The subscription-based stock media platform unveiled its new AI Toolkit, a set of integrated features designed to help creators work faster without replacing the human-made content that anchors its six-million-asset library. Instead of churning out synthetic media, Storyblocks is betting on AI tools that adapt, enhance, and personalize existing footage.

The move lands at a pivotal time for the creator economy. Video teams across industries are under pressure to deliver more content with fewer resources, and stock media has long helped fill that gap. But customization has traditionally been the Achilles' heel of stock footage. Storyblocks is taking aim at that problem by weaving AI-powered voice and video tools directly into its workflow—making it easier to shape assets into something that feels purpose-built instead of generic.

Bringing AI Into the Workflow—Not the Footage

At the heart of the new toolkit are two major integrations: ElevenLabs for voice and Runway for video editing. The blend allows creators to generate high-quality voiceovers, clone voices for personalized narration, and even transform tone or style on demand. On the visual side, Runway’s Aleph model enables adjustments to lighting, color, and background elements, giving creators near-immediate ways to adapt footage for different environments or brand styles.

TJ Leonard, CEO of Storyblocks, said speed is essential but shouldn’t compromise creativity. The AI Toolkit aims to solve the customization gap by letting users refine human-made footage—not replace it with machine-generated alternatives. It’s a nuanced stance in an industry racing toward AI automation, and Storyblocks frames it as a feature, not a limitation.

A New Take on Stock Media Flexibility

Video creators often spend hours adjusting stock clips to fit a narrative. Runway’s integration promises to cut that time to minutes, enabling footage transformation directly inside Storyblocks' platform. Users no longer need to export video into a separate editing tool just to tweak lighting or adjust colors to match a brand palette.

That’s a notable departure from how most stock platforms approach AI today. Many marketplaces are leaning into asset generation, which risks flooding systems with synthetic media of wildly inconsistent quality. Storyblocks is instead doubling down on curation—and enhancing those assets with adaptable AI workflows.

Runway co-founder and Chief Design Officer Alejandro Matamala Ortiz called the integration a “novel approach,” highlighting that creators can now modify and reimagine stock video with unprecedented control. His view aligns with a broader trend in the industry: AI as a collaborator rather than a content factory.

Expanding Creative Freedom Without the Bloat

With features like voice cloning, stylistic narration, and video transformation baked in, the AI Toolkit turns Storyblocks' existing library into a flexible foundation rather than a one-size-fits-all solution. For creators balancing tight deadlines, shrinking budgets, and increasing output demands, this shift could meaningfully reduce friction.

It also signals a wider movement in the creator-tech space. AI isn’t simply about generating content faster—it’s becoming a tool for making content fit faster. Storyblocks’ strategy taps into that momentum, aiming to meet creators where they are: in need of efficiency, but not willing to sacrifice authenticity.

The AI Toolkit is available now, and Storyblocks has published additional details at storyblocks.com/ai-toolkit.

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Tamr, known for its AI-native master data management platform, has launched Curator Hub, a new module designed to take on one of the most persistent blockers to trustworthy AI: bad data. The tool pairs LLM-driven agents with human expertise to fix inconsistencies, resolve edge cases, and strengthen data readiness for generative AI applications.

As companies rush to integrate AI across operations, data quality has emerged as the most expensive—and often overlooked—problem. Gartner predicts that 30% of GenAI projects will be abandoned by 2026 due to poor data quality, weak safeguards, or escalating costs. Curator Hub aims to blunt that trend by offering a scalable method to clean and connect critical enterprise records.

Solving the 5% That Causes 95% of Problems

Tamr already automates most of the data mastering workflow: matching, unifying, cleaning, and enriching records at scale. But the last 5%—messy edge cases—remains notoriously difficult to automate. Whether it’s deciding if “Java Town” and “JavaTown Café” refer to the same entity or sorting out inconsistent values across dozens of systems, these decisions often require human judgment. They also drain time, budget, and patience.

Curator Hub’s LLM-based agents tackle those scenarios by identifying potential issues, suggesting resolutions, and explaining their reasoning in plain language. Data stewards then review, refine, or approve the recommendations in a central workspace built for speed and clarity.

CHG Healthcare, which uses Tamr to unify provider data, sees massive potential. Faster curation could mean faster onboarding, cleaner records, and better placement workflows—all mission-critical in healthcare staffing.

A New, More Intuitive Workflow for Data Stewards

Traditional data curation forces stewards to bounce between spreadsheets, source systems, and custom tools to resolve issues. Curator Hub centralizes that work and replaces manual triage with a guided, human-in-the-loop experience.

Stewards can now:

• Compare problematic records side-by-side

• See why an issue was flagged, complete with labels and confidence scores

• Preview how an update will change the master record before approval

The goal is to make curation both efficient and trustworthy. As Paul Balas of 303Computing notes, the best workflows are the ones stewards actually want to use.

Features Designed for Scalable Data Quality

Curator Hub ships with a set of tools built for enterprise-grade curation:

• Prioritized issue queue: Agents surface duplicates, anomalies, and missing values ranked by urgency.

• Decision-ready views: Clear explanations and change previews streamline judgment calls.

• Golden record refinement: Reassign or realign source records for cleaner entity mastering.

• Customizable workflows: Route issues, set triggers, and determine when AI recommendations require human review.

• Transparent history: Full audit logs for governance and compliance.

• System health insights: Track issue volume, resolution speed, and quality trends.

• Expanding AI agent library: Prebuilt reusable agents, with industry-specific additions coming.

• Bring Your Own Agent (BYOA): Integrate proprietary agents with a supported low-code framework.

CEO Anthony Deighton calls Curator Hub a turning point in how organizations trust, review, and scale data improvements. By giving stewards a more intuitive control panel—and by letting AI agents handle the grunt work—Tamr hopes to accelerate data projects that often stall before delivering value.

Preparing Data for the AI-First Future

Curator Hub is more than a workflow upgrade; it’s a strategic move to help enterprises prepare for generative AI at scale. High-quality, well-curated data is the foundation for everything from predictive analytics to LLM-powered automation. Tamr’s bet is that combining machine intelligence with human oversight is the only viable path to ensure trust and accuracy.

The module is now available for all Tamr Cloud customers as part of the company’s AI-native MDM platform.

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Genesys®, a global leader in AI-powered experience orchestration, has enabled Aterian — the multibrand consumer products powerhouse behind Squatty Potty, Healing Solutions, and more — to transform its customer experience (CX) operations. Leveraging the Genesys Cloud™ platform, Aterian has redefined how it manages interactions across marketplaces like Amazon, Walmart, eBay, Temu, and Shopify, achieving a 65% decrease in cost of ownership, greater efficiency, and more emotionally intelligent customer engagement.

Aterian’s CX Challenges Before Genesys

• Rapid expansion across major ecommerce marketplaces increased operational complexity.

• Over 70% of customer interactions were handled through asynchronous channels such as emails and buyer messages.

• The company needed a unified CX foundation to personalize customer journeys without sacrificing emotional connection.

• Fragmented tools made it difficult to scale, maintain consistency, or optimize agent performance.

How Genesys Cloud Transformed Aterian’s Customer Experience

1. Unified CX Infrastructure for Faster, More Personal Support

• Aterian rebuilt its entire customer experience foundation using the Genesys Cloud platform.

• Consolidated systems streamlined communication and simplified agent workflows.

• The company created a cohesive, omnichannel experience, enabling agents to handle inquiries seamlessly across channels.

2. Intelligent Automation to Scale Empathy and Efficiency

• Aterian integrated its own AI models with Genesys Cloud AI, creating a hybrid “human + AI” support environment.

• Nearly 50% of all customer interactions are now automated, freeing agents for complex or emotionally sensitive cases.

• Automation delivered consistent support quality while reducing handle time by almost 25%.

3. Empowered Agents Through AI-Driven Coaching and Real-Time Insights

• AI-powered guidance is embedded natively in the Genesys interface — no additional dashboards or external tools.

• Agents receive contextual insights and next-best-action recommendations during live interactions.

• This resulted in a 33% increase in agent satisfaction, showcasing how AI can reduce friction, enhance performance, and support agent confidence.

4. Delivering Empathy at Scale: A Strategic Advantage

• Genesys Cloud enabled Aterian to maintain its core brand philosophy — emotionally intelligent customer care — even as it scaled rapidly.

• The platform ensured customers feel heard and valued whether interacting with a human or a virtual agent.

• The company now turns each interaction into an opportunity to strengthen loyalty and trust.

5. Supporting Aterian’s Diversifying Product Portfolio

• The new CX foundation supports expansion into categories like:

  • Squatty Potty Flushable Wipes

  • Healing Solutions Tallow Skincare

• Genesys Cloud ensures consistent brand experiences across all new product lines.

• The platform prepares Aterian for smooth seasonal surges, especially during high-volume periods like the holidays.

Aterian’s partnership with Genesys marks a pivotal shift in how consumer brands approach customer experience at scale. By merging AI-powered orchestration with human empathy, the company has built a modern CX ecosystem that supports growth, consistency, and deeper customer connection. With intelligent automation, real-time agent coaching, and a unified platform, Aterian is positioned to deliver meaningful, emotionally resonant experiences across every touchpoint — today and as it expands into the future.

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Cloud voice platforms have long been judged on reliability, call quality, and how quickly they can untangle the messy business of diagnosing failures. Now AVOXI wants to rewrite that playbook with Proactive Service, an AI-enabled diagnostic tool that claims to spot voice issues before customers ever notice—97% of the time, according to the company.

For global contact centers juggling thousands of conversations per hour, that kind of foresight isn’t just convenient—it’s a competitive advantage.

A New Spin on Voice Reliability

Traditional voice troubleshooting typically leans on ticket queues, manual testing, and a fair amount of guesswork when outages or quality hiccups occur. AVOXI’s pitch is simple: replace the legacy break-fix workflow with continuous monitoring powered by automation and AI.

Proactive Service scans for call flow disruptions, number-level availability problems, and traffic anomalies. When it detects suspicious behavior, it automatically runs diagnostics, gathers context, and opens support cases—no human intervention needed. AVOXI says this approach cuts issue resolution time in half.

It’s a timely upgrade. According to the 2025 State of International Voice for the Contact Center Report, the industry is wrestling with a three-headed monster:

• 80% cite voice security concerns

• 77% struggle with call quality

• 78% see gaps in global coverage

With contact centers expanding across regions and marketplaces, the pressure to maintain uptime and route calls intelligently has never been higher. Competitors like Genesys, Twilio, and Zoom have all been weaving AI into their communications fabrics, but diagnostics—particularly proactive diagnostics—remain an area where few have staked a definitive claim.

Replacing “Break/Fix” With “Predict/Prevent”

AVOXI frames Proactive Service as a shift from reactive to preventive support. Instead of waiting for angry customers to call about dropped connections, the platform preemptively identifies the underlying risk—whether it’s a misbehaving virtual number, a routing misconfiguration, or a traffic spike that hints at fraud.

For enterprises running contact centers in five or more countries, these early warnings can translate into avoided downtime, protected revenue, and fewer blown SLAs. And with call quality continuing to define customer experience, the stakes are only getting higher.

“Every second counts for enterprises that rely heavily on contact centers to engage with callers,” says CEO Barbara Dondiego. “Proactive Service sets a new standard for protecting global voice more actively and intelligently.”

Built Into AVOXI’s New Premium AI Cloud Package

Proactive Service is part of AVOXI’s new Premium AI Cloud SaaS package, designed for organizations that want deeper oversight and stronger threat resilience in their voice infrastructure. The suite combines analytics, call flow monitoring, issue triage, security insights, and automated case creation in one dashboard.

For companies operating across Amazon, Walmart, Walmart Connect, and emerging global marketplaces, a diagnostic tool that works quietly in the background—and works fast—could deliver a measurable edge.

In a market where AI is reshaping everything from workforce scheduling to fraud detection, AVOXI’s focus on automated diagnostics feels like a natural next step. Whether it becomes the new normal across cloud voice platforms will depend on how effectively Proactive Service scales—but the early numbers give it a strong opening argument.

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In a digital landscape where video is king and attention spans are the ultimate currency, KERV.ai is doubling down on its ambition to make every frame count—literally. The Austin-based startup just closed its Series B funding round, led by Coral Tree Partners, to accelerate its push into interactive, shoppable, and data-rich video experiences across online and connected TV (CTV).

KERV.ai has been building momentum for months, reporting record commercial and partnership growth. Now, with fresh capital in hand, the company wants to expand globally, pour more fuel into R&D, and build out its contextual commerce engine—the same engine quietly powering clickable product moments inside ads, shows, and creator content.

Video’s New Currency: Object-Level Metadata

While much of the industry talks about AI-powered video, KERV.ai’s pitch is more granular. Its platform parses videos frame-by-frame, identifying products, objects, scenes, and contextual cues with proprietary object-level metadata. That data then drives everything from shoppable overlays to dynamic creative optimization to first-party data targeting.

In a world where advertisers are staring down the deprecation of third-party cookies and increasingly opaque attribution, KERV.ai’s approach offers something rare: actionable, privacy-safe intelligence extracted directly from content itself. Brands and publishers get smarter targeting and measurable outcomes; consumers get interactive moments that feel less like ads and more like discovery.

It’s a formula that’s resonating with agencies and CTV publishers searching for ways to improve performance without cramming more ads into their streams.

Investors See a Convergence Moment

Coral Tree Partners, known for backing companies at the intersection of media and technology, says KERV.ai is well-positioned to lead a long-overdue shift.

“KERV.ai has built a proprietary technology that combines creative storytelling, commerce activation, and data-driven performance,” said Coral Tree’s Alan Resnikoff. “This team is poised to lead the convergence of content, commerce and contextual intelligence.”

That convergence is already happening across the ecosystem. Amazon has been experimenting with shoppable streaming formats, Roku continues to invest in retail media tie-ins, and TikTok is pushing deeper into AI-powered product recognition. KERV.ai’s differentiation is its ability to apply these capabilities across all screens, not just its own walled garden.

CTV Growth, Ad-Supported Tiers, and the Commerce Flywheel

A big tailwind behind this raise is the explosive growth of ad-supported streaming. As more platforms—from Disney+ to Netflix—launch or expand AVOD tiers, the pressure is on to make ads more effective without increasing volume.

That’s where contextual commerce comes in.

Instead of relying on broad demographics or third-party segments, object-level metadata allows advertisers to target based on exact on-screen relevance. A character carries a particular handbag? A viewer can buy it. A cooking show features a specific spice blend? One tap takes you to checkout.

Publishers benefit too: interactive formats often deliver higher engagement and superior CPMs.

KERV.ai’s CEO Gary Mittman frames it as the start of a new era of performance video:
“Video remains the most powerful medium for connection, and KERV.ai is redefining how data, commerce and creativity come together,” he said. “With Coral Tree’s partnership, we’ll continue scaling our contextual commerce and AI video-intelligence solutions to drive measurable results for our clients.”

What Comes Next

With the new funding, KERV.ai plans to invest in:

• Expanded R&D for advanced AI video intelligence

• Global infrastructure and engineering talent

• New strategic partnerships across retail media and CTV

• Scalable tools for brands and agencies to build interactive creative

The company’s raise also underscores a broader industry trend: interactive video is becoming a competitive differentiator, not a novelty. As CTV continues its march toward retail media integration and AI personalization, expect more players to double down on contextual commerce.

KERV.ai—armed with fresh capital, growing demand, and a maturing tech stack—appears ready to push video deeper into the shoppable, measurable, data-enriched future marketers have been chasing.

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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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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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