In a move that signals where marketing intelligence is headed next, ZeroToOne.AI has acquired GroundTruth to create what it calls a large-scale predictive intelligence platform for real-world consumer behavior.

The ambition is bold: move enterprises beyond analyzing what already happened—and into predicting what customers will do next, in the physical world.

From Measurement to Prediction

For years, marketing and analytics platforms have focused on attribution—tracking clicks, impressions, and conversions after the fact. ZeroToOne is taking aim at that model with a system designed to forecast behavior before it happens.

By combining its proprietary Large Behavioral Model with GroundTruth’s massive network of real-world signals, the company says it can anticipate when, where, and how consumers are likely to act—with reported accuracy exceeding 85%.

That’s a meaningful shift. Instead of reacting to customer journeys, brands could proactively influence them—adjusting campaigns, inventory, and operations in near real time.

Why GroundTruth Matters

GroundTruth brings scale—and distribution. The platform processes billions of daily interactions across physical locations and serves more than 2,000 enterprise customers globally.

That gives ZeroToOne something most AI startups lack: immediate access to real-world data streams and an established customer base to deploy into.

It also strengthens the offline side of the equation. While many AI marketing platforms rely heavily on digital signals, GroundTruth specializes in connecting digital engagement to physical outcomes like store visits—a critical metric for retail and omnichannel brands.

The Tech Behind the Deal

At the heart of the combined platform is ZeroToOne’s Large Behavioral Model, developed by researchers from Carnegie Mellon University—a heavyweight in AI research.

The model ingests privacy-safe behavioral signals from both digital and physical environments, generating predictive insights at scale. The result: a system designed not just to interpret intent, but to forecast it.

Early deployments across select GroundTruth customers are already showing results, according to the company—cutting media costs by 70% and increasing ROI by 45%, alongside improved store visit conversions.

Those are eye-catching numbers, though as with any vendor-reported metrics, enterprises will want independent validation.

A Bigger Bet on Predictive Marketing

The acquisition reflects a broader industry shift toward predictive and prescriptive analytics in marketing. As AI matures, the competitive advantage is moving upstream—from insights to foresight.

This puts ZeroToOne into a rapidly evolving competitive landscape that includes data giants, adtech platforms, and AI-native startups all chasing the same goal: turning fragmented data into actionable predictions.

Where ZeroToOne could stand out is its focus on real-world behavior. Many platforms excel at predicting online actions; fewer can reliably connect that intelligence to offline outcomes like foot traffic and in-store purchases.

Leadership and Market Positioning

As part of the deal, marketing veteran John Costello joins ZeroToOne’s board as Vice Chair, bringing deep brand and growth expertise from his time at Dunkin’ Brands.

GroundTruth’s leadership team will also transition into the combined company, ensuring continuity for customers and partners—a critical factor in large platform integrations.

The Bottom Line

ZeroToOne’s acquisition of GroundTruth isn’t just about scale—it’s about redefining how enterprises make decisions.

If the company delivers on its promise, marketing teams may spend less time analyzing dashboards and more time acting on AI-driven predictions—shifting from reactive campaigns to anticipatory strategies.

That’s a compelling vision. The real test will be whether predictive accuracy holds up across industries—and whether enterprises are ready to trust AI with decisions that directly impact revenue.

Get in touch with our MarTech Experts.

Vertical SaaS continues its steady march into niche industries—and now it’s the performing arts’ turn.

Opus1 has appointed Sharad Mohan as CEO while closing a Series B funding round, signaling a push to scale its platform across a fragmented but fast-modernizing performing arts education market.

Founder Sam Lellouche will step into the role of Chief Strategy Officer, remaining closely involved in product direction and long-term vision.

A Vertical SaaS Playbook, Now for Performing Arts

Opus1 isn’t trying to reinvent SaaS—it’s applying a familiar playbook to an underserved category.

The platform offers an all-in-one system for music and performing arts schools, covering scheduling, billing, communications, marketing, and analytics. In other words, it replaces the patchwork of spreadsheets and disconnected tools that many schools still rely on.

That approach is gaining traction. Opus1 now supports more than 200,000 active students and facilitates over 10 million lessons annually—numbers that position it as a serious contender in a niche that’s historically lacked purpose-built software.

Why This Leadership Change Matters

Mohan’s appointment is more than a routine executive shuffle—it’s a signal of intent.

As co-founder and former CEO of Trainerize, he helped build a category-defining platform in the fitness industry, another vertical that transitioned from analog operations to SaaS-driven management. That experience is directly relevant as Opus1 looks to scale beyond early adopters and capture a broader share of the performing arts market.

The transition also reflects a common pattern in growing SaaS companies: founders shifting into strategic roles while experienced operators take over day-to-day execution.

Funding to Accelerate Product and Expansion

While the company didn’t disclose the size of the Series B round, the funding will go toward product development and platform expansion.

That likely means deeper functionality, broader integrations, and potentially expansion beyond music schools into adjacent performing arts segments like dance, theater, and other class-based programs.

The opportunity is significant. The performing arts education market is large but highly fragmented, with thousands of independent schools operating without modern infrastructure. That makes it fertile ground for vertical SaaS platforms that can standardize operations and improve business efficiency.

The Bigger Trend: SaaS Goes Niche

Opus1’s momentum aligns with a broader shift in enterprise software: horizontal tools are giving way to vertical, industry-specific platforms.

From fitness to healthcare to education, SaaS companies are increasingly winning by tailoring solutions to the unique workflows of specific industries. These platforms often deliver faster ROI because they solve highly specific pain points out of the box.

In that context, Opus1 is positioning itself as the system of record for performing arts schools—a role similar to what other vertical SaaS leaders have achieved in their respective domains.

What Comes Next

Under Mohan’s leadership, expect Opus1 to double down on customer-led product development—working closely with school owners and administrators to refine its platform.

The company’s long-term vision is clear: become the foundational operating system for performing arts education businesses.

Whether it can achieve that will depend on execution, especially as competitors inevitably take note of the category’s potential.

For now, though, Opus1 is hitting the right notes—pairing fresh capital with experienced leadership at a moment when its market is finally ready for digital transformation.

Get in touch with our MarTech Experts.

As enterprises race to operationalize AI, partnerships—not just platforms—are becoming a key differentiator.

Qubika has been elevated to Gold Partner status in the Databricks Brickbuilder Partner Network (formerly “Elite”), signaling its growing role in delivering enterprise-scale data and AI solutions on the Databricks stack.

The upgrade isn’t مجرد branding. It reflects deeper technical validation, certified expertise, and a track record of deploying complex AI and data architectures at scale.

A Bet on the Lakehouse—and What Comes Next

Qubika’s elevation comes as the lakehouse model continues to gain traction as a unified approach to data warehousing and analytics.

The company has been investing heavily in this architecture, particularly around emerging capabilities like Databricks Lakebase—a next-gen evolution that brings transactional (OLTP) workloads directly into the lakehouse ecosystem.

That’s a notable shift. Traditionally, transactional systems and analytics platforms have lived in separate worlds. If Lakebase delivers on its promise, it could further blur that line—making real-time, AI-driven applications more feasible within a single environment.

Scaling AI From Experiment to Production

At the center of Qubika’s pitch is its Data and AI practice, which focuses on helping enterprises move from “digital-native” to “AI-native.”

With more than 200 Databricks-certified professionals—including Champions and Solutions Architects—the company is positioning itself as more than an implementation partner. It’s aiming to guide organizations through the full lifecycle: from data modernization to production-grade AI deployment.

Its capabilities span a wide range of Databricks tools and frameworks, including:

• Lakehouse architecture built on Delta Lake
• Data governance and quality with Unity Catalog
• Machine learning pipelines using MLflow
• Conversational analytics via Databricks Genie
• AI agents powered by Mosaic AI

Qubika has also developed its own accelerators, including the QBricks Agentic Accelerator, designed to speed up enterprise AI adoption.

Community, Ecosystem, and Influence

Beyond client work, Qubika is investing in ecosystem visibility—a key factor in partner-tier recognition.

The company has been active in the Databricks community, hosting user groups across North and South America and presenting at flagship events like the Data + AI Summit in San Francisco. That presence helps reinforce its credibility while contributing to knowledge-sharing across the ecosystem.

It’s a strategy many top-tier partners are adopting: technical depth paired with community leadership.

Why This Matters

As organizations prioritize AI readiness, the bottleneck is no longer just tooling—it’s execution.

Platforms like Databricks provide the foundation, but enterprises still need experienced partners to architect, deploy, and govern AI systems at scale. That’s where firms like Qubika come in.

The Gold Partner designation signals that Qubika has crossed a threshold—from capable integrator to trusted transformation partner.

The Bigger Picture

The competition among Databricks partners is intensifying as demand for AI and data modernization accelerates. Achieving Gold status puts Qubika in a stronger position to win large enterprise deals, particularly in sectors like financial services, healthcare, retail, and media.

At the same time, it underscores a broader trend: the rise of specialized AI services firms that combine deep platform expertise with industry-specific knowledge.

For enterprises navigating complex AI transformations, those partnerships may prove just as critical as the technology itself.

Get in touch with our MarTech Experts.

Connected TV (CTV) has long promised the best of both worlds—premium TV reach with digital-style targeting. The reality? Fragmentation, murky attribution, and limited performance visibility.

Now, Keynes is aiming to fix that—with fresh capital to back it up.

The company has secured a $40 million minority investment from Volition Capital, positioning itself to double down on turning CTV into a measurable, performance-driven channel rather than just a branding play.

CTV’s Measurement Problem Isn’t Going Away

As streaming continues to pull audiences away from linear TV, advertisers are pouring billions into CTV. But unlike search or social, CTV still struggles with transparency and consistent performance metrics.

Different platforms, inconsistent data, and limited attribution models have made it difficult for marketers to treat CTV as a true performance channel.

That’s the gap Keynes is targeting.

A Platform Built for Performance, Not Just Reach

Founded in 2018, Keynes has built its platform around a simple idea: CTV should be accountable to business outcomes, not just impressions.

Its approach combines audience targeting, AI-driven optimization, and transparent reporting to help brands track real impact—whether that’s conversions, incremental growth, or ROI.

In a space often criticized for “black box” reporting, that emphasis on transparency is a key differentiator.

Where the $40M Goes

The new funding isn’t about survival—it’s about acceleration.

Keynes plans to invest in:

• Technology infrastructure to support more advanced campaign execution
• Data integrations to unify fragmented CTV signals
• Measurement capabilities to improve attribution and performance tracking
• Team expansion to meet growing advertiser demand

The goal is clear: make CTV behave more like performance marketing channels such as paid search and social media.

Why Investors Are Paying Attention

Volition Capital’s backing signals confidence not just in Keynes, but in the broader evolution of CTV.

As streaming platforms mature, advertisers are demanding the same level of accountability they expect from digital channels. That shift is creating opportunities for platforms that can bridge the gap between brand advertising and measurable outcomes.

Keynes appears to be capitalizing on that moment, with a focus on disciplined execution and client retention—two factors investors tend to value highly in adtech.

The Bigger Trend: Convergence of TV and Performance Marketing

CTV is no longer just a top-of-funnel channel. The industry is moving toward full-funnel measurement, where TV advertising can be tied directly to conversions and revenue.

That puts Keynes in competition with a growing ecosystem of adtech vendors, DSPs, and measurement platforms all racing to define the future of TV advertising.

The winners will likely be those that can simplify complexity—integrating fragmented data sources while delivering clear, actionable insights.

The Bottom Line

Keynes isn’t just raising money—it’s betting on a fundamental shift in how TV advertising works.

If it succeeds, CTV could finally deliver on its long-promised potential: combining the scale of television with the accountability of digital marketing.

For advertisers, that’s not just appealing—it’s overdue.

Get in touch with our MarTech Experts.

Prompt engineering fatigue is real—and Picsart is betting designers are ready to move past it.

The company has integrated Recraft V4’s new “Exploration Mode” into its platform, introducing a different approach to generative AI: one that prioritizes visual discovery over perfectly crafted prompts. It’s a notable pivot in a market still obsessed with prompt tuning as a core skill.

From Prompting to Exploring

Most generative AI tools demand precision—write the right prompt, get the right output. Miss the mark, and you’re stuck iterating endlessly.

Recraft V4 flips that model.

Instead of requiring detailed instructions, users can input a simple creative direction—like “retro poster” or “minimal logo”—and instantly receive eight distinct design variations. The idea is to mimic how designers actually work: exploring options, reacting visually, and refining from there.

It’s less about describing the end result and more about discovering it.

Why This Matters for Creative Workflows

This shift toward exploration isn’t just a UX tweak—it addresses one of the biggest friction points in AI-assisted design.

Prompt engineering has become a barrier, especially for non-technical users. By reducing reliance on precise inputs, Picsart is making AI more accessible to its base of over 130 million creators, many of whom aren’t trained in prompt syntax.

At the same time, it aligns more closely with professional design workflows, which are inherently iterative and visual.

Built for Designers, Not Just Image Generation

Recraft V4 also leans into design quality—an area where many generative models still fall short.

Key differentiators include:

• Typography that works: Clean, readable text—something most AI image tools still struggle with
• Native vector outputs (SVG): A rare capability that allows designs to scale without losing quality
• Balanced layouts and color harmony: Outputs that require less post-editing
• Professional export formats: Including SVG, PNG, JPG, PDF, TIFF, and Lottie

Taken together, these features position Recraft V4 closer to a design tool than a pure image generator.

Exclusive Access Signals a Platform Shift

Picsart isn’t just adding another model—it’s changing its platform strategy.

By making Recraft its first exclusive AI model partner, the company is moving beyond the “model marketplace” approach adopted by many competitors. Instead of offering every model under the sun, it’s curating access to select technologies and giving its users early exposure.

That could become a competitive advantage as the AI design space grows more crowded.

Part of a Larger AI Push

The integration also fits into Picsart’s broader AI roadmap. The company recently announced a Creative AI Agent Marketplace, signaling a move toward more automated, workflow-driven creative tools.

Recraft V4 is now available in Picsart Flow for full-scale design projects, as well as in its AI Playground, where users can experiment with over 100 models side by side.

The Bigger Picture

The generative AI design market is evolving quickly—from raw image generation to more structured, workflow-oriented tools.

Picsart’s latest move suggests the next phase will be less about technical mastery and more about usability and creative control. In other words, AI that adapts to designers—not the other way around.

If that vision holds, “prompt engineering” might soon feel like a transitional phase rather than a permanent skill.

Get in touch with our MarTech Experts.

Audience targeting has a freshness problem—and Cognitiv thinks it has the fix.

The company has launched AudienceGPT™, a new AI-powered tool designed to help marketers move beyond static audience segments and outdated behavioral data. Instead of relying on past actions, AudienceGPT focuses on predicting what consumers are likely to do right now.

It’s a shift from retrospective targeting to real-time intent modeling—and one that could reshape how campaigns are planned and executed.

From Static Segments to Living Audiences

Traditional audience targeting hasn’t evolved much: group users based on past clicks, visits, or searches, then keep targeting them long after those signals have gone cold.

AudienceGPT takes a different approach.

Powered by Cognitiv’s deep learning engine—the same foundation behind ContextGPT—the platform analyzes behavioral signals to map where consumers are in their purchase journey. It then builds predictive audience profiles that update as frequently as every 15 minutes.

That means campaigns can adapt in near real time, aligning spend with current intent rather than historical behavior.

How It Works: AI Meets Media Buying

Instead of manually building segments or relying on predefined taxonomies, marketers can describe their ideal audience in plain language via a chat-based interface.

AudienceGPT then:

• Generates dynamic persona recommendations
• Explains the reasoning behind each audience profile
• Identifies similar users programmatically across channels
• Adjusts audience size and precision based on campaign needs

Under the hood, the system uses LLM-powered reasoning and synthetic consumer journey modeling—essentially simulating how people move through decision-making processes.

The result is a targeting model that evaluates individuals, not just segments.

Built for Omnichannel Activation

AudienceGPT isn’t limited to a single channel. It’s designed to work across CTV, digital audio, social, and programmatic environments.

Marketers can activate audiences through Cognitiv’s DSP or export them as Deal IDs and segments into external DSPs and SSPs—making it flexible within existing adtech stacks.

Key integrations include:

• Magnite for CTV and streaming inventory
• Index Exchange for real-time supply-side activation
• LiveRamp Marketplace for scalable audience distribution

These partnerships help extend AudienceGPT’s reach across display, video, CTV, and audio—areas where targeting precision has historically lagged.

Why This Matters Now

The timing is critical.

Consumer behavior is shifting faster than ever, especially across streaming and digital audio platforms. Yet most targeting systems still operate on delayed signals, creating a gap between insight and action.

AudienceGPT is designed to close that gap—bringing targeting closer to real-time decision-making.

It also addresses a growing industry challenge: signal loss. As cookies fade and privacy regulations tighten, marketers need new ways to understand intent without relying on traditional identifiers.

Cognitiv’s approach—using deep learning to infer intent rather than track it directly—could offer a path forward.

The Bigger Picture: AI Reshapes Audience Strategy

Cognitiv’s launch reflects a broader trend in adtech: the move from rule-based targeting to AI-driven prediction.

Instead of defining audiences manually, marketers increasingly describe goals and let AI systems build and optimize segments dynamically. That’s faster, more scalable, and potentially more accurate—if the models hold up.

AudienceGPT also builds on Cognitiv’s recent momentum. Its ContextGPT product saw significant growth in 2025, suggesting strong demand for AI-driven targeting solutions.

The Bottom Line

AudienceGPT isn’t just another targeting tool—it’s an attempt to redefine how audiences are created, updated, and activated.

If it works as advertised, marketers could spend less time building segments and more time acting on real-time insights—reaching consumers when intent is highest, not after it’s passed.

In a market where timing is everything, that’s a compelling advantage.

Get in touch with our MarTech Experts.

Startup infrastructure is expensive—especially when it comes to building reliable, global customer communications from scratch. Infobip has spent the past five years trying to remove that barrier.

The company says its Startup Tribe Programme has now supported thousands of startups and scaleups across more than 120 countries since its launch in 2021, offering a mix of financial credits, technical infrastructure, and ecosystem access.

A Shortcut to Global Communication Infrastructure

At its core, the program is designed to help early-stage companies skip one of the most resource-intensive steps: building and managing communications systems.

Participants get access to up to $60,000 in credits for Infobip’s services—covering messaging, email, authentication (like OTP), and channels such as WhatsApp—along with connections to advisors, investors, and accelerators.

That’s not just a cost-saving measure. It’s a strategic one.

By outsourcing communications infrastructure, startups can redirect resources toward product development, sales, and growth—areas where speed often determines survival.

Real-World Impact: From Cost Savings to Growth

For startups like HotelSync and Cleanster, the benefits go beyond free credits.

The program enables automation of core workflows—transactional messaging, user verification, and customer engagement—while reducing operational overhead. More importantly, it frees up budget that can be reinvested into go-to-market strategies.

That’s a common pain point for startups: infrastructure costs can quietly consume capital that would otherwise fuel growth.

Why This Matters Now

The timing aligns with a broader shift toward API-first, cloud-based communications platforms (CPaaS), where companies plug into existing infrastructure instead of building their own.

Infobip’s approach mirrors similar ecosystem plays by major cloud providers—but with a sharper focus on startups and scaleups navigating early growth stages.

As AI-driven customer engagement becomes more critical, access to scalable, intelligent communication tools is increasingly a competitive advantage.

More Than Credits: Building an Ecosystem

Infobip is positioning Startup Tribe as more than a perks program—it’s an ecosystem play.

Beyond credits, startups gain access to:

• A global network of investors and accelerators
• Technical mentorship and onboarding support
• Scalable communication tools powered by AI

That combination aims to create long-term platform loyalty while helping startups grow into enterprise customers.

It’s a familiar strategy in SaaS: invest early in startups, and grow with them over time.

The Bigger Picture: Competing in the CPaaS Market

The cloud communications space is crowded, with major players competing on pricing, global reach, and developer experience.

Infobip’s differentiation lies in its global footprint and its focus on enabling omnichannel engagement—from SMS and email to OTT platforms like WhatsApp—all under one roof.

Programs like Startup Tribe help the company expand its footprint among early-stage companies, a segment that can deliver outsized long-term value.

The Bottom Line

Five years in, Infobip’s Startup Tribe reflects a simple but powerful idea: remove infrastructure friction, and startups can move faster.

As the company celebrates its 20th anniversary, the program underscores its broader strategy—embedding itself deeper into the growth journeys of startups worldwide.

For founders, that means one less system to build—and one more lever to scale.

Get in touch with our MarTech Experts.

Attribution has a trust problem—and it usually shows up when marketing, finance, and BI teams compare numbers.

MessageGears is aiming to fix that with a new conversion reporting suite built directly on top of the data warehouse. The pitch is simple: if everyone relies on the same data source, everyone should see the same results.

It’s a pragmatic take on a long-standing issue in marketing analytics—fragmented data pipelines and inconsistent reporting across platforms.

Why Warehouse-Native Changes the Game

Most marketing platforms rely on SDKs and APIs to pull in conversion data. That works—until it doesn’t.

Because those systems only capture partial customer interactions, they often diverge from the “source of truth” housed in enterprise data warehouses. The result? Conflicting metrics, endless reconciliation, and diminishing trust in marketing reports.

MessageGears flips that model.

Instead of copying or syncing data into another system, it reads data directly where it already lives—in the warehouse. Conversion reporting is built on that same foundation, tying revenue and campaign performance directly to trusted datasets.

In theory, that eliminates discrepancies between marketing dashboards and financial reporting.

Aligning Marketing, BI, and Finance

For large enterprises, attribution isn’t just about measuring performance—it’s about organizational alignment.

When marketing reports don’t match finance numbers, decision-making slows down. Teams spend more time debating data than acting on it.

MessageGears’ approach aims to solve that by:

• Using the warehouse as the single source of truth
• Feeding attribution data back into the same environment
• Ensuring consistency across departments

That means marketers, analysts, and executives are all working from the same dataset—no translation required.

What’s New in Conversion Reporting

The new capabilities go beyond basic campaign tracking.

Key features include:

• Real-time event ingestion: Live purchase and behavioral data pulled directly from the warehouse
• Cross-channel attribution: Revenue tied to email, SMS, push, in-app, and embedded messaging
• Custom event tracking: Teams can define and standardize event labels across reports
• Unified dashboard: A centralized view of conversions, revenue, and performance trends
• Campaign-level insights: Metrics like purchases, revenue, and average order value

Attribution defaults to a 24-hour last-click model, with customizable windows by channel.

Flexibility That Legacy Platforms Lack

One of the more notable differences is flexibility.

Most platforms require marketers to define conversion events before launching campaigns—and lock those definitions in place. Change your mind later, and you’re out of luck.

MessageGears removes that constraint.

Once events are configured at the warehouse level, they can be applied to any campaign at any time—even retroactively. That opens the door to deeper analysis without forcing teams to predict every reporting need upfront.

The Bigger Trend: Data Warehouses as the Marketing Core

MessageGears’ launch reflects a broader shift in martech architecture.

As companies centralize data in cloud warehouses, the traditional model—where each tool maintains its own dataset—is starting to break down. Increasingly, the warehouse is becoming the operational hub, not just a storage layer.

That shift is driving demand for “warehouse-native” tools that operate directly on centralized data rather than duplicating it.

The Bottom Line

MessageGears isn’t introducing a flashy new AI feature—it’s solving a more fundamental problem: data trust.

By anchoring conversion reporting in the warehouse, the company is betting that accuracy and alignment matter more than additional layers of analytics.

If it works, marketers may finally spend less time defending their numbers—and more time improving them.

Get in touch with our MarTech Experts.