At most service businesses, data isn’t the problem—interpretation is. Owners and managers sit on mountains of operational, financial, and field data, yet still struggle to answer the question that matters most: what should we do next?

At its 2026 Beyond Service Customer Conference, WorkWave introduced what it believes is the missing link. The company announced Wavelytics™ Decision Intelligence, a new AI-powered hub designed to move service organizations beyond dashboards and reports—and into prescriptive, role-specific guidance baked directly into daily workflows.

Rather than treating analytics as a separate destination, Decision Intelligence is embedded inside WorkWave’s core industry platforms, reframing reporting as a navigation system for running a service business.

From Reporting to Decision-Making

Traditional business intelligence tools excel at showing historical performance. Decision Intelligence is meant to do more. Built as part of the Wavelytics ecosystem, the new hub continuously ingests data from operations, finance, and the field through the Wavelytics Data Factory, a unified data layer that cleans, normalizes, and standardizes information across systems.

That foundation enables something service operators have long lacked: context-aware, prescriptive insight.

Instead of simply flagging a revenue dip, the system surfaces potential causes and recommended actions—whether that’s coaching a technician, rebalancing territories, or accelerating follow-up on aging leads. The result is a shift from reactive management to guided execution.

Kevin Kemmerer, CEO of WorkWave, framed the problem succinctly: “Service business owners are often drowning in data but starving for insights. They don’t need another report to read; they need to know what to do next.”

Built for the Realities of Service Industries

Unlike horizontal analytics platforms, Wavelytics Decision Intelligence is tailored specifically for service verticals such as pest control, lawn care, cleaning, and security. That industry focus shows up in both the metrics and the workflows the system supports.

The platform is embedded across WorkWave’s industry-specific solutions, including PestPac®, RealGreen®, and TEAM Software®, ensuring that insights align with how each business actually operates.

More importantly, the intelligence is persona-driven. Owners, CEOs, dispatchers, CFOs, and branch managers each see dashboards, insights, and recommendations relevant to their responsibilities—reducing noise and speeding decision-making.

A Modular Intelligence Framework

Decision Intelligence is structured around four core components, each answering a different operational question.

Dashboards: The “What” and “Where”
High-level dashboards provide visibility into macro trends such as sales pipeline health, revenue retention, and operational efficiency. Rather than forcing users to stitch together multiple reports, the system organizes complex data into a unified intelligence hub.

Scorecards: The “Who”
Scorecards drill down to individual performance, highlighting metrics such as salesperson close rates, technician productivity, or chemical usage. This granular view helps managers identify both top performers and areas that need attention.

Alerts: The “Now”
Real-time alerts act as proactive nudges. Whether it’s a spike in callbacks, aging leads, or a sudden drop in conversion rates, managers are notified when immediate intervention could prevent revenue loss or customer dissatisfaction.

Ask WAIve: The Orchestrator
Perhaps the most ambitious element is Ask WAIve, a natural language interface that functions as a unified intelligence layer. Users can query the system conversationally, generate reports, surface insights, and even direct specialized agents to recommend next steps—all without leaving the data environment.

Together, these components aim to close the gap between insight and action—a persistent challenge in service management software.

Hybrid Workforce, Practical AI

WorkWave positions Decision Intelligence as the “context-aware engine” of the Hybrid Workforce, blending human expertise with AI-driven guidance. The emphasis is notably pragmatic. This isn’t AI for experimentation’s sake; it’s AI embedded in daily decisions that affect revenue, efficiency, and customer satisfaction.

That approach reflects a broader trend in enterprise software. As AI matures, value is shifting away from standalone tools toward embedded intelligence that operates quietly inside existing systems.

In this case, the intelligence doesn’t replace managers—it augments them, highlighting opportunities and risks that might otherwise be missed in the noise of daily operations.

Competitive Context

The service management software market is crowded, but analytics remains uneven. Many platforms still rely on static reports or generic BI integrations that require interpretation and expertise to be useful.

WorkWave’s bet is that vertical-specific, prescriptive intelligence will be a differentiator—especially for mid-sized service businesses that don’t have dedicated data teams. By tying insights directly to operational levers like technician performance, territories, and lead management, Decision Intelligence positions itself as operational guidance rather than abstract analytics.

If successful, it could raise expectations across the category, pushing competitors to move beyond dashboards toward decision-centric design.

Availability and What’s Next

Wavelytics Decision Intelligence is expected to roll out to Wavelytics users in Q2 2026, subject to change. WorkWave is demonstrating the platform live this week at the Beyond Service Customer Conference in Dallas, giving customers a first look at how prescriptive analytics fits into real workflows.

For service businesses under pressure to grow margins, retain talent, and deliver consistent customer experiences, the timing is notable. As labor challenges persist and costs rise, knowing what to do next—not just what happened—may be the difference between scaling and stagnation.

Why This Matters

Decision Intelligence reflects a meaningful evolution in service software. Reporting tells the past. Intelligence guides the future.

By embedding prescriptive insights directly into industry-specific platforms, WorkWave is signaling that analytics should no longer live on the sidelines. For service leaders, the promise is clear: fewer dashboards, fewer guesses, and more confident decisions—made in the moment, not after the fact.

Whether Decision Intelligence becomes a standard feature of service operations will depend on adoption and outcomes. But the direction is unmistakable. In the next phase of service management, insight alone won’t be enough—actionability will be the real metric.

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For many creators, making the video is no longer the hardest part. Publishing it well—writing the right title, crafting a description that actually gets clicks, and building consistent habits that grow a channel—often is.

Videoinu is aiming squarely at that gap with the launch of YouTube Copilot, a new AI agent designed to guide creators through what happens after the video is finished. Rather than focusing on editing or effects, the tool zeroes in on packaging and distribution—two areas that quietly determine whether a video finds an audience or disappears into the algorithmic void.

The move signals a broader shift in creator tech: AI tools are expanding beyond production into the mechanics of growth.

From Video Creation to Video Publishing

Videoinu has built its platform around a simple promise—anyone can turn an idea or script into a high-quality video, no editing background or budget required. The company’s tools are especially popular with creators producing faceless, story-driven content, where speed, consistency, and repeatability matter more than on-camera charisma.

With YouTube Copilot, Videoinu extends that philosophy into publishing. Once a creator finishes generating a video, the agent steps in with practical, platform-ready guidance—suggesting how to refine titles and descriptions based on what’s resonating on YouTube right now.

Instead of relying on intuition or trial-and-error, creators get recommendations informed by patterns across top-performing and trending content. The idea is not to “game” the algorithm, but to reduce guesswork at the moment when creators are most likely to stall or second-guess.

Packaging: The Quiet Growth Lever

Ask successful YouTubers what matters most, and many will point to packaging—titles, descriptions, thumbnails, and consistency—over raw production quality. Videoinu is leaning into that reality.

“YouTube Copilot gives creators a clear playbook at the exact moment they need it,” said Richard Jian, spokesperson for Videoinu. “After the video is generated, creators can write better titles and descriptions, publish with more confidence, and keep improving with every upload.”

That timing is key. Publishing tools often live in separate dashboards or analytics platforms, disconnected from the creative flow. By embedding guidance directly into the post-production step, Videoinu positions YouTube Copilot as a natural extension of the creation process rather than another tool to manage.

Learning From What’s Working Now

Unlike static SEO checklists or generic best practices, YouTube Copilot is designed to learn from patterns in current high-performing content. That includes how titles are structured, how descriptions frame value, and how creators signal relevance to viewers.

For smaller or newer creators, this kind of context can be especially valuable. Understanding why certain videos perform well is often harder than copying surface-level formats. Videoinu’s pitch is that its agent surfaces those insights without requiring creators to constantly monitor trends themselves.

The result is a more informed publishing decision—one grounded in what audiences are actually engaging with, not what worked six months ago.

Built for Consistency, Not One-Off Virality

YouTube Copilot also reflects Videoinu’s emphasis on long-term channel building. Rather than chasing viral hits, the agent is designed to help creators publish consistently and develop repeatable habits—still the most reliable path to audience growth and monetization.

That focus aligns with Videoinu’s broader product design. The platform supports episodic formats and series, enabling creators to maintain consistent characters, scenes, and narratives across uploads. For faceless and story-driven channels, that continuity can be a differentiator, helping audiences recognize and return to familiar formats.

Consistency isn’t glamorous, but it’s how most successful channels are built. Videoinu is betting that creators want tools that support momentum, not just moments.

Structured Workflows for Scale

Under the hood, Videoinu emphasizes structure as much as speed. Its storyboard-driven workflow allows creators and teams to standardize production, refine formats over time, and scale output without starting from scratch each time.

Creators can regenerate individual scenes, tweak outputs, or iterate on story elements without rebuilding entire projects—an important capability for channels publishing frequently. Combined with YouTube Copilot’s publishing guidance, the platform aims to shorten the distance from concept to publish-ready video.

For solo creators, that means less friction. For small teams, it means processes that don’t break as output increases.

A Crowded Creator Tech Landscape—With a Twist

Videoinu enters a market crowded with AI video tools, many of which focus heavily on generation speed or flashy visuals. What differentiates Videoinu is its attention to the full creator lifecycle—from idea to distribution.

While competitors race to make video creation faster, Videoinu is addressing a quieter pain point: creators don’t just need more videos; they need videos that perform consistently over time.

By introducing YouTube Copilot, Videoinu positions itself less as a novelty generator and more as an operating system for repeatable content businesses.

One Million Users—and Growing

Alongside the product launch, Videoinu shared a major milestone: 1,000,000 registered users globally. The company says community activity is strong across YouTube, Discord, Reddit, X (Twitter), Instagram, and TikTok—an indicator of growing interest in AI-powered, creator-first workflows.

That traction suggests a market increasingly open to AI as a collaborator rather than a shortcut. Creators are using these tools not just to save time, but to build systems that support sustained output.

Why This Matters for MarTech and Creators

YouTube Copilot highlights an important evolution in creator tooling. As AI lowers the barrier to content creation, distribution and differentiation become the new bottlenecks. Tools that help creators make smarter publishing decisions—without overwhelming them with data—stand to play an outsized role.

For marketers and brands watching the creator economy, the implications are clear. The next wave of creator platforms won’t just generate content; they’ll guide creators toward behaviors that drive growth, consistency, and monetization.

Videoinu’s bet is that creators don’t just want to make videos—they want channels that grow. With YouTube Copilot, the company is stepping into that space, one title and description at a time.

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Candy brands aren’t usually known for digital experimentation—but Sour Jacks isn’t a typical candy brand. With a new immersive website designed by full-service digital agency eDesign Interactive, the sweet-and-sour favorite is leaning hard into play, personality, and Gen Z-first experiences.

The redesigned Sour Jacks website is less a brochure and more a digital playground. Built around interactivity, gamification, and bold visuals, the experience mirrors the brand’s mouth-puckering wedges and unapologetically electric identity. It’s a reminder that for younger audiences, brand websites are no longer destinations for information—they’re destinations for entertainment.

Turning Browsing Into Play

At the heart of the experience is “Play Mode,” a retro, Pac-Man-inspired mini-game that lets visitors chase candy across the screen. Instead of scrolling through static product pages, users are invited to interact immediately—transforming passive browsing into active participation.

This approach taps into a broader shift in brand experience design. As Gen Z grows increasingly resistant to traditional digital marketing, gamification has emerged as a way to earn attention rather than demand it. Sour Jacks’ site embraces that philosophy unapologetically.

The result feels closer to an indie game or interactive art project than a conventional CPG website—and that’s very much the point.

A Visual Style That Matches the Flavor

Visually, the site is loud in all the right ways. Glitch effects, glowing neon accents, and immersive 3D animations dominate the experience. Product packages spin, pulse, and react, translating the brand’s tangy intensity into motion and color.

The retro-gaming aesthetic pulls from early arcade culture while still feeling native to modern, digital-first consumers. It’s nostalgic without being dated—an important balance when targeting Gen Z, a generation that values irony, remix culture, and shareability.

Every design choice reinforces the same message: Sour Jacks is not here to be subtle.

Storytelling With an Edge

Beyond the visuals, the site leans into interactive brand storytelling. Sections like the “Way of the Wedge” manifesto frame Sour Jacks as rebellious, playful, and proudly sour. Even the navigation feels intentionally glitchy, reinforcing the idea that the brand operates slightly outside the lines.

This kind of experiential storytelling reflects a larger trend in brand marketing. Younger audiences don’t just want to know what a product is—they want to understand the vibe, values, and attitude behind it. Sour Jacks’ new site delivers that narrative without relying on heavy copy or corporate messaging.

Building a Community, Not Just Traffic

The experience doesn’t end with visuals and games. Community features like “Join the Jacks” and the #SpotTheSour social feed invite users to participate beyond the site itself. User-generated content, social integration, and location-based candy finders create ongoing touchpoints that extend the brand experience into the real world.

That community-first approach is increasingly critical in CPG marketing, where loyalty is often driven by culture and identity as much as by taste. By encouraging fans to engage, share, and explore, Sour Jacks positions itself as a brand people can belong to—not just buy from.

A Strategic Bet on Experience-Driven Branding

According to Vincent Mazza, Managing Partner at eDesign Interactive, the goal was to go beyond aesthetics. “Sour Jacks has such a strong personality, and we wanted the site to reflect that energy in every pixel,” he said. “We wanted an experience that leaves a lasting impression—something fun, unexpected, and a little sour in the best way possible.”

Early results suggest the strategy is working. Since launch, Sour Jacks has seen increased site engagement, higher interaction rates, and a rise in fan-generated content—signals that the experience is resonating with its target audience.

Why This Matters for MarTech

The Sour Jacks launch highlights an important shift in digital marketing: brand websites are evolving from static hubs into experience platforms. For Gen Z especially, attention is earned through interactivity, personality, and play—not polished messaging alone.

For marketers, the takeaway is clear. As social platforms become more crowded and algorithms more unpredictable, owned digital experiences are regaining strategic importance. But to compete, they need to feel worth visiting.

Sour Jacks’ new site doesn’t just sell candy. It sells a feeling—and invites users to play along.

Get in touch with our MarTech Experts.

Influencer marketing may be booming, but trust remains its weakest link. As brands pour billions into creator partnerships, the vetting process behind those deals is still surprisingly manual—hours of scrolling, subjective judgment calls, and the constant risk of something being missed. Katch Data wants to change that equation.

The content intelligence company, already trusted by major Hollywood studios and some of the world’s largest social platforms, has launched Katch Verified, its first product purpose-built for agencies and brands working in influencer marketing. The pitch is straightforward but ambitious: apply the same deep, frame-by-frame content analysis used in film and television to influencer vetting—and do it at scale.

From Film Studios to Influencers

Katch Data isn’t a typical MarTech startup. The company built its reputation helping entertainment studios and platforms understand content at a granular level, using what it calls a “genomic” approach. Instead of relying on surface-level tags or keyword detection, Katch analyzes content the way a genome maps DNA—breaking it down into its smallest components to understand meaning, context, and risk.

With Katch Verified, that methodology is being brought into influencer marketing for the first time.

Brands and agencies upload a list of creators, define their requirements—brand values, risk thresholds, categories to avoid—and let the platform do the rest. In minutes, teams can review hundreds of influencers, a task that would typically take days or weeks of manual review.

For an industry built on speed and scale, that time compression alone is a meaningful shift.

Seeing What Humans—and LLMs—Miss

What sets Katch Verified apart is the depth of its analysis. The platform examines every frame, object, sound, and word across an influencer’s historical content. That includes subtle or fleeting elements that are easy to overlook: a background object that implies controversial behavior, a brief audio clip, or contextual cues that generic AI models often fail to interpret correctly.

This matters because influencer risk rarely shows up as an obvious red flag. More often, it’s buried in nuance—patterns of behavior, repeated themes, or contextual associations that only become clear when content is analyzed holistically.

Katch says its proprietary semantic understanding algorithm, powered by multimodal AI and content genomics, is designed specifically to surface those blind spots. Unlike standard brand-safety tools that rely on pre-defined taxonomies, the system explains why something was flagged, giving teams clear reasoning and visual evidence rather than opaque scores.

That transparency could be critical for agencies and brands under pressure to justify decisions to clients and stakeholders.

Influencer Marketing’s Risk Problem

The launch comes at a telling moment for the creator economy. Influencer marketing is more mainstream than ever, but it’s also more scrutinized. Regulatory pressure is rising, brand values are under the microscope, and viral backlash can erase years of brand equity overnight.

Despite that, much of the industry still relies on interns, spreadsheets, and gut instinct to assess creator fit.

Andrew Tight, CEO and co-founder of Katch Data, sees that gap as unsustainable. “Influencer marketing is entering its most important era, and also its riskiest,” he said. “Brands are pouring billions into creators, yet the industry still relies on manual review. Katch Verified ends that.”

His point is hard to argue with. As influencer budgets rival traditional media spend, the tolerance for “gotcha moments” is shrinking fast.

A Different Take on Brand Safety

Katch Verified also reflects a broader shift in how brand safety is being defined. Traditional tools focus on exclusion—blocking keywords, topics, or categories. Katch’s approach is more contextual, emphasizing fit rather than just avoidance.

That makes the platform as useful for identifying the right creators as it is for flagging risky ones. Green flags matter too, especially for brands looking to build long-term creator partnerships aligned with specific values or audience sensibilities.

Dr. Nolan Gasser, Chief Genomic Officer and co-founder of Katch Data, argues that this level of nuance is only possible with a genomic approach. “Our genomics approach makes it possible to detect nuances that even advanced tagging systems simply cannot capture,” he said, pointing to the company’s experience working with top entertainment companies, social platforms, and ad agencies.

Competitive Implications

Katch Verified enters a crowded influencer tech market, but one still dominated by discovery, analytics, and performance tracking tools. Deep content intelligence—especially at this level of granularity—remains relatively rare.

If Katch can deliver consistent accuracy at scale, it could carve out a defensible niche as the “trust layer” of influencer marketing. That’s particularly appealing to large brands and agencies managing global creator rosters, where reputational risk compounds quickly.

It also raises the bar for competitors. As multimodal AI matures, brands may start expecting influencer vetting tools to go beyond follower counts and engagement rates—and into actual content understanding.

Why This Matters for MarTech

Katch Verified underscores a larger MarTech trend: intelligence is moving upstream. Instead of optimizing campaigns after launch, brands are investing more heavily in decision-making before money changes hands.

In influencer marketing, where authenticity and alignment are everything, that shift could be transformative. Less guesswork. Fewer surprises. More confidence that the creators representing a brand actually reflect what it stands for.

For Katch Data, this launch marks a strategic expansion beyond entertainment into one of digital marketing’s fastest-growing—and most fragile—channels. For the industry, it’s a reminder that as creator marketing grows up, its tooling has to grow up with it.

Get in touch with our MarTech Experts.

For years, 3D data has been both a promise and a problem. LiDAR scans, photogrammetry captures, and industrial digital twins are extraordinarily rich—but also painfully heavy, expensive to move, and difficult to deploy beyond controlled lab environments. As AI shifts from screen-bound models to machines that must perceive and act in the physical world, that friction has become impossible to ignore.

Greneta, a deep-tech company focused on high-precision 3D infrastructure, believes it has an answer. This week, the company officially launched its end-to-end SaaS platform at Greneta.ai, positioning it as core infrastructure for what many are calling the next phase of AI: Physical AI.

At its core, the platform is designed to tackle what Greneta describes as the “data gravity” problem in 3D—where massive datasets become so large and unwieldy that they resist movement, sharing, and real-time use. The company’s pitch is ambitious but clear: make high-fidelity 3D data as streamable and usable as 2D video.

Why 3D Data Is Becoming the New Bottleneck

The timing is not accidental. Autonomous vehicles, robotics, digital twins, and spatial computing systems all depend on accurate, high-resolution representations of the real world. Unlike traditional AI models trained on text or images, Physical AI systems must understand geometry, depth, scale, and physics—often down to millimeter-level accuracy.

The problem is that raw 3D data is enormous. A single industrial scan can run into gigabytes. Entire environments can balloon into terabytes. Moving that data across clouds, devices, and simulation environments is slow, expensive, and often impractical.

This is where Greneta is staking its claim. Rather than treating 3D optimization as a downstream step or a custom services project, the company has productized it into a fully automated SaaS pipeline. Upload raw data, click once, and receive optimized assets ready for simulation, visualization, or AI training.

That “one-click” framing matters. In an industry still dominated by bespoke workflows and specialist tooling, ease of use can be just as disruptive as raw technical performance.

Compression Without Compromise

Greneta’s most eye-catching claim is its ability to reduce 3D file sizes by more than 90% while preserving sub-millimeter precision. That’s a bold promise in a field where compression often comes at the cost of accuracy, and accuracy is non-negotiable for industrial and robotic use cases.

According to the company, its proprietary optimization algorithms were refined through years of field testing and industrial proof-of-concept deployments. The result is a system that strips away redundant data while maintaining the geometric and spatial integrity required for digital twins, simulation, and autonomous navigation.

If the numbers hold up in production, the implications are significant. Smaller files mean faster iteration, lower storage costs, easier streaming, and the ability to deploy complex 3D environments across distributed teams and edge devices.

In practical terms, this could help bridge the gap between experimental Physical AI projects and scalable, real-world deployments.

From Raw Captures to Photorealistic Worlds

Compression is only part of the story. Greneta’s platform also integrates a growing set of 3D generative AI and reconstruction tools, including support for Gaussian splatting—a technique that has gained traction for its ability to produce photorealistic, navigable 3D scenes from relatively sparse inputs.

The goal is to shorten the distance between capture and use. Instead of weeks of manual cleanup and reconstruction, Greneta promises environments that can be generated and navigated in minutes.

That’s particularly relevant for industries experimenting with digital twins, remote inspection, training simulations, and spatial analytics. As competitors like Matterport, Bentley Systems, and Autodesk continue to push deeper into industrial digital twins, the ability to rapidly generate usable 3D environments is becoming a competitive differentiator.

Greneta is betting that automation, rather than deeper feature complexity, will be the deciding factor.

World Models: A Strategic Bet on Physical AI

One of the platform’s more forward-looking moves is its integration with World Labs’ world models. While still an emerging concept, world models aim to give AI systems a coherent understanding of space, physics, and causality—essentially a mental model of how the physical world works.

By aligning its 3D environments with world model frameworks, Greneta is positioning itself not just as a data optimization vendor, but as infrastructure for AI training itself.

This matters because Physical AI systems are increasingly trained in simulation before being deployed in the real world. If those simulations lack physical consistency, the models trained on them fail when exposed to real-world conditions. Greneta’s approach suggests a future where optimized 3D data feeds directly into AI systems that understand space, not just pixels.

It’s a subtle but important shift—from visual fidelity alone to spatial intelligence.

Built With NVIDIA in Mind

Greneta’s inclusion in the NVIDIA Inception Program adds another layer of credibility and context. NVIDIA has spent years building an ecosystem around accelerated computing, simulation, and digital twins, with platforms like NVIDIA Omniverse becoming central to industrial and robotics workflows.

Greneta says its optimized assets are fully compatible with NVIDIA’s high-performance computing environments, making it easier for developers to move data between capture, simulation, and deployment.

That interoperability could be critical. As enterprises invest more heavily in NVIDIA-powered simulation stacks, tools that slot cleanly into that ecosystem gain a structural advantage. Greneta effectively positions itself as a bridge between raw 3D data and NVIDIA-driven simulation and AI pipelines.

In a market where vendor lock-in is a growing concern, compatibility is no small selling point.

From Proofs of Concept to Product

The SaaS launch follows a period of technical validation for Greneta. The company says its core technology was refined through demanding industrial PoCs across multiple sectors, though it has not publicly disclosed customer names.

That groundwork appears to have paid off. Greneta was recently named a CES 2026 Innovation Awards Honoree, a signal that its approach resonated beyond niche technical circles.

Awards don’t guarantee market success, but they do suggest that Greneta is tapping into a real and growing pain point. As Physical AI moves from hype to deployment, infrastructure players—often less visible than model builders—stand to capture outsized value.

A Crowded but Fragmented Landscape

Greneta is not alone in tackling 3D data challenges. Startups and incumbents alike are racing to simplify spatial data pipelines. What differentiates Greneta is its focus on automation, extreme compression, and Physical AI readiness rather than visualization alone.

Many existing tools excel at rendering beautiful 3D scenes for humans. Fewer are optimized for machines that need to reason about space at scale. Greneta’s emphasis on precision, world models, and simulation compatibility places it closer to infrastructure than media.

That positioning could prove decisive as enterprises look to standardize their 3D pipelines rather than stitch together point solutions.

Why This Launch Matters

The launch of Greneta.ai reflects a broader shift in enterprise AI. As models leave the screen and enter factories, warehouses, cities, and vehicles, the quality and usability of 3D data becomes foundational.

If 2D images and text were the fuel of the last AI wave, high-fidelity, lightweight 3D environments may be the fuel of the next. Greneta’s platform is an attempt to build the refineries.

“Our goal is to make high-precision 3D data as accessible and streamable as 2D video,” a Greneta spokesperson said. It’s an ambitious comparison—but one that captures the company’s intent clearly.

Whether Greneta becomes a standard layer in the Physical AI stack will depend on adoption, performance at scale, and how quickly the ecosystem around world models matures. But the direction is unmistakable: 3D data is moving from specialist asset to core infrastructure.

And companies that can make it lighter, faster, and smarter may quietly shape the future of autonomous systems.

Get in touch with our MarTech Experts.

For all the hype around AI in go-to-market teams, much of today’s “AI” still amounts to smarter chat interfaces, better copy generation, or faster dashboards. FlashLabs is aiming higher—and riskier.

The company has launched FlashLabs SuperAgent, positioning it not as an assistant or copilot, but as a fully hosted, enterprise-secure AI Revenue Worker that operates 24/7 across sales, marketing, and revenue operations. The pitch is blunt: SuperAgent doesn’t just suggest actions. It executes them.

In a market increasingly saturated with AI copilots, FlashLabs is betting that the next phase of enterprise AI is less about conversation—and more about autonomous work.

From conversational AI to execution engines

SuperAgent is designed to handle revenue workflows end to end, operating with persistent memory, business context, and multi-step autonomy. Rather than waiting for prompts inside a UI, it continuously runs in the background, monitoring systems, data, and performance—even when teams are offline.

According to FlashLabs, SuperAgent can:

• Automate email, calendar, CRM, invoicing, and RevOps workflows

• Execute browser-level actions across the web

• Identify and qualify customers by scanning multiple data signals

• Generate decks, proposals, images, videos, research, and GTM plans

• Manage pipeline hygiene, forecasting, deal QA, and follow-ups

• Integrate with thousands of systems, including CRMs, ERP, finance tools, email platforms, and social networks

• Monitor business systems continuously for changes, risks, and opportunities

This positions SuperAgent closer to an autonomous digital operator than a traditional AI tool—more RPA meets agentic AI than chatbot meets analytics.

Messaging becomes the command line

One of the more unconventional aspects of SuperAgent is how it’s controlled.

Instead of requiring users to log into a proprietary interface, FlashLabs turns messaging platforms into the control plane. Teams can operate SuperAgent through:

• Telegram

• iMessage

• SMS

Additional channels are planned, but the idea is already clear: a single message can trigger complex, multi-system workflows.

In practice, that means a sales leader could request pipeline cleanup, forecasting updates, or deal follow-ups via a simple message—while SuperAgent handles the orchestration behind the scenes. It’s a sharp contrast to the dashboards and workflow builders that dominate today’s RevOps stacks.

Built for enterprises, not experiments

FlashLabs is also leaning hard into enterprise-readiness, an area where many agentic AI projects stall.

SuperAgent is fully hosted and production-ready, requiring:

• No hardware deployment

• No infrastructure management

• No exposed credentials

• No complex authentication flows

By abstracting away infrastructure and security concerns, FlashLabs is clearly targeting organizations that want outcomes without adding operational burden—or risk—to already complex tech stacks.

This matters because autonomous AI raises uncomfortable questions for security, compliance, and governance. FlashLabs’ approach suggests it wants to remove friction not just from usage, but from approval.

AI as a revenue workforce

The most provocative framing around SuperAgent is how FlashLabs describes its role: not software, but labor.

Early adopters report SuperAgent autonomously progressing deals, updating pipelines, managing follow-ups, and delivering revenue insights around the clock. In effect, it behaves like a tireless revenue operations employee—one that doesn’t log off, forget tasks, or drop handoffs between systems.

That framing aligns with a broader industry shift. As AI agents mature, vendors are increasingly positioning them as digital workers rather than productivity tools. Microsoft, Salesforce, and a wave of startups are racing to define this category—but most still rely on human-in-the-loop execution.

FlashLabs is attempting to push past that boundary.

Why this matters for MarTech and RevOps

Revenue teams are under pressure from both sides: rising expectations for personalization and speed, and shrinking tolerance for headcount growth. At the same time, RevOps stacks have become notoriously fragmented, with automation spread across CRMs, sales engagement tools, finance systems, and analytics platforms.

SuperAgent’s promise is to sit above that stack, coordinating actions across systems without requiring teams to stitch workflows together manually.

If it works as advertised, this could signal a shift away from tool-centric RevOps toward agent-centric execution layers—where AI handles the operational glue and humans focus on strategy, relationships, and judgment.

The competitive landscape

SuperAgent enters a crowded but unsettled space. Established players like Salesforce and HubSpot are embedding AI deeper into their platforms, while startups push agentic automation, browser control, and multi-step reasoning.

What differentiates FlashLabs is its insistence on full autonomy and messaging-first control, combined with enterprise hosting and security. That combination may appeal to teams frustrated by AI tools that still require heavy configuration and constant supervision.

The risk, of course, is trust. Autonomous execution demands confidence that the AI understands context, priorities, and boundaries—especially when revenue, compliance, and customer relationships are on the line.

The bigger picture

FlashLabs SuperAgent reflects a growing belief in B2B tech: the future of AI isn’t more suggestions—it’s more execution.

As agentic systems mature, the line between software and workforce continues to blur. Whether SuperAgent becomes a blueprint or a cautionary tale will depend on how well it balances autonomy with control.

Either way, it’s a clear signal that the era of “AI that helps” is giving way to AI that works.

Get in touch with our MarTech Experts.

For decades, in-practice marketing in medical aesthetics has been stuck in a time warp—brochures on countertops, posters in exam rooms, and little to no insight into whether any of it actually influenced patient decisions.

Vrtly, Inc. wants to end that era.

The point-of-care (POC) marketing platform has announced a major expansion of its digital ecosystem, positioning itself as a true end-to-end, in-practice marketing solution for medical aesthetics brands. The goal: replace static, paper-based tactics with a fully measurable, digitized sales channel that connects brand marketing spend directly to patient behavior and treatment selection.

In an industry where timing, trust, and context heavily influence decisions, Vrtly is betting that the clinic itself—not social media, not search—is the most underutilized marketing surface of all.

Fixing a 20-year blind spot in healthcare marketing

In-practice marketing has barely evolved in more than two decades. While digital marketing outside the clinic has become hyper-targeted and data-rich, the moment when patients are most primed to decide—inside the practice—has remained largely unmeasured.

That disconnect has created a massive blind spot between brand exposure and actual treatment adoption.

Vrtly’s expanded platform is designed to close that gap by digitizing the entire in-clinic experience and capturing patient engagement at every stage of the visit. Instead of guessing what worked, brands can now see what patients interacted with, when they engaged, and how that engagement translated into real outcomes.

“In-practice marketing falls flat when it’s built on paper and guesswork,” said Vojin Kos, CEO of Vrtly. “Patients need to be prompted at the exact moment of influence. We’ve digitized the entire in-practice experience to mirror the real patient journey.”

From waiting room to treatment room—and beyond

At the core of Vrtly’s approach is the idea that the clinical visit is not a single moment, but a sequence of decision points. The platform turns that journey into a connected, always-on engagement loop.

Key capabilities include:

Always-on patient engagement
Vrtly synchronizes high-impact brand content across in-clinic screens, interactive consultation tools, and patient mobile devices. Its patent-pending Info Packs deliver relevant educational and promotional content directly to patients’ phones, extending engagement beyond the appointment itself.

The result is persistent brand presence—from the lobby to the exam room, and after the patient leaves.

From exposure to verified outcomes
Through beta EMR integrations, Vrtly links in-practice engagement data with actual treatment selection. For brands, this represents a long-awaited breakthrough: the ability to see how marketing exposure converts into verified product usage, not just impressions.

AI-driven precision at peak intent
Led by Chief Product Officer Joe Schooler, whose background includes Google, Amazon, and Apple, Vrtly’s machine-learning models analyze engagement behavior and EMR signals to determine which brand message to show, to which patient, and at what moment.

This turns the clinic from a passive environment into a measurable, adaptive sales channel—one that can support cross-sell and upsell strategies with far more accuracy than traditional tactics.

Why this matters now

Medical aesthetics is a fast-growing, cash-pay segment where patients often make decisions during consultations rather than long research cycles. That makes the point of care uniquely influential—and uniquely valuable.

Yet most marketing dollars are still optimized for pre-visit discovery, not in-clinic decision-making.

Vrtly’s expansion reflects a broader trend across healthcare and MarTech: bringing measurement and personalization into physical spaces, not just digital ones. Similar shifts are happening in retail media, digital out-of-home (DOOH), and in-store analytics. Healthcare, historically slower to modernize marketing infrastructure, is now catching up.

Early momentum and rapid deployment

Vrtly isn’t positioning this as a long-term vision—it’s already seeing traction.

The company has paid brand pilots underway, with additional campaigns launching in Q1. To reduce deployment friction, Vrtly has rolled out native Smart TV and tablet apps, allowing practices to activate campaigns in minutes rather than weeks.

That speed matters. For brands running national campaigns across distributed clinics, ease of rollout can be the difference between experimentation and scale.

“We’re building the infrastructure that makes cross-selling actually work,” said Schooler. “Connecting exposure to behavior is the unlock for repeatable revenue growth.”

A new channel for healthcare brands—and beyond

Looking ahead to 2026, Vrtly is refining pricing and packaging to support rising demand while exploring non-endemic advertising opportunities. Cash-pay healthcare environments tend to attract high household income (HHI) demographics, making them increasingly attractive to adjacent brands looking for premium, context-rich exposure.

If successful, Vrtly’s model could redefine how marketers think about clinical spaces—not as static environments governed by compliance constraints, but as data-enabled engagement channels.

The bigger picture

Vrtly’s expansion highlights a growing realization across healthcare marketing: digital transformation doesn’t stop at the clinic door.

As brands demand accountability, attribution, and measurable ROI, paper brochures and posters simply don’t cut it anymore. By digitizing the point of care and tying engagement to outcomes, Vrtly is pushing in-practice marketing into the same performance-driven era that has reshaped the rest of MarTech.

Whether competitors follow—or incumbents scramble to modernize—one thing is clear: the waiting room is no longer just a waiting room.

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G2, the world’s largest and most trusted software marketplace, announced that its Answer Engine Optimization (AEO) software category has grown from just seven products at launch to more than 150 in under a year, marking over 2000% growth since March 2025. The category reached a major milestone with the release of its first G2 Grid® Report in the Winter 2026 Reports, signaling that AEO has matured into a recognized and essential market.

AI is now the starting point of the buyer journey

The explosive growth of AEO software reflects a fundamental change in buyer behavior. According to an August G2 survey:

• 50% of B2B software buyers now begin their purchasing journey in an AI chatbot, not a traditional Google search

• 74% of buyers name ChatGPT as their preferred large language model (LLM)

As AI chatbots increasingly deliver direct answers, recommendations, and comparisons, visibility within platforms like ChatGPT, Gemini, Copilot, and Google AI Mode has become a top priority for B2B vendors.

Rather than competing solely for page rankings and clicks, companies are now competing to be the answer.

What is Answer Engine Optimization (AEO)?

Answer Engine Optimization (AEO) software helps brands improve their visibility and representation across AI chatbots and LLM-powered search experiences.

These platforms go beyond traditional SEO by enabling organizations to:

• Optimize content for AI-generated answers and conversational interfaces

• Track brand mentions and citations within LLM responses

• Identify ranking and recommendation factors used by AI systems

• Detect misinformation, bias, or hallucinations in how AI describes a brand

“The modern buying journey is compressed by AI, and winning today means winning the answer, not just the click,” said Emily Greathouse, Director of Market Research at G2. “Companies need tools that move beyond traditional SEO metrics to focus on AI visibility and LLM ranking factors.”

First G2 Grid® Report for AEO released

A software category becomes eligible for a G2 Grid® Report once it reaches:

• At least six products, each with a minimum of 10 reviews

• A total of 150+ reviews across the category

The first Winter 2026 G2 Grid® Report for AEO featured nine products across four performance tiers:

Leader

• Profound

High Performers

• Otterly.AI

• Scrunch AI

Contenders

• Semrush

• BrightEdge

• Conductor

Niche

• Quattr

• GetCito

• GenRank.io

Since the Winter 2026 Reports launched on December 3, 2025, additional vendors—including AirOps, Hall, Waikay, Brandi, and Visby AI—have earned placement on the AEO Grid as of January 26, 2026. G2 updates category Grids daily to reflect the latest review and market data.

Vendors race to control AI-driven brand perception

As AI increasingly mediates software discovery, vendors are under pressure to ensure their brands are accurately represented—and favorably positioned—inside AI-generated answers.

“The way people discover, evaluate, and trust software has fundamentally changed,” said Trevor Pyle, Head of Marketing at Profound. “As buyers turn to answer engines for fast, direct guidance, demand is rising for software that powers AEO strategies.”

Profound, named a Leader in the inaugural Grid, focuses on mapping real buyer questions to how AI models interpret and cite brands—an approach that reflects the new mechanics of AI-powered discovery.

How G2 evaluates AEO software

To qualify for the AEO category, products must deliver clear value across four core capabilities:

1. Visibility into AI-generated answers
   Track where, how often, and in what context a brand appears in LLM responses.

2. Trustworthy brand interpretation
   Identify inaccuracies, bias, or hallucinations in how AI platforms describe a company or product.

3. Transparency into AI rankings and recommendations
   Reveal the signals influencing AI-driven citations and reduce the “black box” effect of LLMs.

4. Competitive benchmarking
   Compare AI visibility and positioning against competitors and category peers.

The bigger picture

AEO’s rapid growth on G2 underscores a broader reality: AI has become the front door to B2B software discovery. As search evolves from links to answers, companies that fail to understand—and optimize for—AI visibility risk disappearing from the buyer journey entirely.

The emergence of AEO as a formal software category marks a turning point: optimizing for AI isn’t experimental anymore—it’s foundational.

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