The blank canvas has met its match.

Picsart, the all-in-one design platform with more than 130 million monthly active users, has unveiled Picsart Aura, a voice-first AI creative collaborator designed for real-time co-creation. The pitch is simple: talk, and it creates.

Aura represents a significant evolution of Picsart’s image editor—one of its most-used mini apps—and signals the company’s push deeper into conversational, adaptive AI creation. Instead of mastering layers, brushes, and toolbars before producing something usable, creators can now describe what they want and watch it materialize.

For an industry crowded with AI image generators, the differentiator here isn’t just generative capability. It’s workflow integration and personalization.

From Prompting to Partnering

Aura is built on research analyzing more than 100,000 user prompts, giving it a data-informed foundation for common creative use cases. According to Picsart, it’s grounded in what the company calls “Vibe Design”—a philosophy aimed at lowering the barrier to creative entry as close to zero as possible.

In practical terms, Aura addresses what Picsart sees as the real bottleneck: not technical ability, but creative friction. Many users don’t struggle with tools—they struggle with knowing where to begin.

Aura’s response is conversational creation. Users can issue natural language or voice commands, replacing multi-step layered editing processes with spoken instructions. The AI maintains context across sessions and adapts over time, learning aesthetic preferences and workflow habits.

CEO Hovhannes Avoyan frames the shift as a move away from tool mastery toward AI familiarity. Instead of learning software, the software learns you.

Unified Image and Video in One Flow

One of Aura’s more ambitious claims is unifying image and video workflows inside a single conversational thread.

Users can:

• Create a product photo

• Transform it into a marketing visual

• Animate it into a video ad

• Extend the video into a longer story using Video Extend

• Add music—all within one guided flow

That cross-format continuity matters. Many AI tools excel at generating a single output—an image, a clip, a design—but require users to switch apps or workflows to continue building.

Aura’s integration aims to collapse that fragmentation.

For content creators, that could mean transforming static photos into trending animated posts in under a minute. For small businesses, it could mean upgrading basic product shots into campaign-ready assets without hiring a production team. For casual users, it could mean fixing group photos or jumping on viral trends with simple voice commands.

Voice-First, but Not Voice-Only

Voice interaction is central to Aura’s design. Speaking ideas rather than typing them is intended to accelerate ideation and lower intimidation for non-designers.

But Picsart hasn’t abandoned manual control. Users can move seamlessly between Aura’s conversational interface and the full Picsart editor for detailed precision work—brush edits, typography, sticker placement—before returning to conversational mode.

This hybrid approach positions Aura somewhere between a generative AI tool and a traditional design suite. It’s less about replacing professional-grade editing and more about compressing time-to-output.

Adaptive Intelligence and Context Retention

Aura’s adaptive intelligence is another differentiator. The AI suggests styles and “vibes” based on context, tracks preferences over time, and retains conversation history across sessions.

In theory, that makes Aura more of a long-term creative partner than a one-off prompt engine. It also reflects a broader shift in AI product design: personalization as a competitive moat.

As AI platforms proliferate, differentiation increasingly hinges on how well they understand user intent over time—not just how impressively they generate a single output.

Competitive Landscape: AI Creation Gets Conversational

The generative design space is crowded. Standalone image generators, video AI tools, and integrated productivity suites are all vying for creator attention. Many offer powerful outputs but require users to craft precise prompts or navigate fragmented toolchains.

Picsart’s advantage lies in its scale and ecosystem. With over 2.5 billion lifetime downloads and an established base of 130 million monthly users, it can embed AI capabilities directly into familiar workflows.

Aura also builds on Picsart’s recent launches, including Flow—its AI workflow automation tool—and AI Assistant for more detailed, structured creative tasks. Together, these tools signal a strategy: turn Picsart from an editing app into an AI-powered creative operating system.

The emphasis on voice-first interaction adds another layer. While voice interfaces are common in virtual assistants, they’re still emerging in creative production environments. If widely adopted, voice-powered design could reshape how non-professionals approach content creation.

The Bigger Picture: Lowering the Creative Barrier

Aura’s core insight is deceptively simple: creative hesitation often stems from uncertainty, not inability.

By guiding users from idea to output through conversation, Picsart is betting that the future of design isn’t about teaching everyone to be an expert editor—it’s about embedding expertise into the tool itself.

For businesses, creators, and everyday users, that shift could mean faster iteration cycles and broader participation in digital content production.

Whether Aura becomes a default creative companion or just another AI feature depends on execution and user adoption. But one thing is clear: the race to make AI creation more natural—and more personal—is accelerating.

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Developers already know how to ship code. What many haven’t been formally taught is how to ship media—at scale, efficiently, and without wrecking performance.

That’s the gap Cloudinary aims to close with the launch of the Cloudinary Creators Community, a global developer network focused on image and video optimization. The initiative combines free coursework, structured nonprofit-led cohorts, certification programs, and hands-on projects designed to help developers master what Cloudinary calls “the visual web.”

The move reflects a growing industry reality: in modern digital products, media performance is no longer a design afterthought—it’s infrastructure.

A Developer Community Focused on the Visual Layer

The Cloudinary Creators Community is built around practical education rather than brand evangelism. Developers gain access to:

• Free structured courses

• Hands-on projects

• Certification pathways

• A dedicated Discord community

• Live webinars and expert-led sessions

• Open-source collaboration opportunities

The inaugural course, “Cloud to Crowd: Media IQ for Developers with Next.js,” teaches developers how to build and optimize visual media workflows at scale using modern frameworks. The focus is performance, automation, and intelligent delivery—skills that are increasingly essential in ecommerce, media, SaaS, and mobile-first applications.

Jen Looper, Director of Developer Relations at Cloudinary, describes the initiative as a “practical learning environment, not a marketing initiative.” The emphasis is on equipping developers to handle complex, high-volume image and video management challenges.

In a world where product teams obsess over milliseconds and Core Web Vitals, that’s not trivial.

Why Media Optimization Is Now a Core Skill

As digital experiences become more visual—think immersive ecommerce galleries, short-form video, interactive storytelling—media payloads grow heavier. Poorly optimized images and videos can crush performance, damage SEO rankings, and degrade user experience.

Frameworks like Next.js have pushed performance optimization closer to the development workflow, but media handling often remains fragmented across teams.

Cloudinary’s strategy positions media APIs and automation as first-class developer skills. Rather than manually compressing assets or juggling CDNs, developers can automate transformation, delivery, and optimization directly in the build pipeline.

This aligns with broader trends in developer tooling: APIs and infrastructure abstract complexity, but understanding how they work—and how to scale them—remains valuable.

Scaling Through Nonprofit Partnerships

To expand reach beyond traditional developer circles, Cloudinary has partnered with five tech-focused nonprofits across multiple geographies:

• Developers in Vogue (Ghana)

• GirlScript Foundation (India)

• Hack Your Future (Denmark)

• Tampa Devs (US)

• Vets Who Code (US)

These organizations will deliver training through structured cohorts and bootcamps, helping developers from diverse and often underrepresented backgrounds gain exposure to media optimization technologies.

For example, GirlScript Foundation in India, which serves a community of over 500,000 learners, sees the partnership as a way to introduce “niche, high-impact technologies” rarely covered in formal curricula.

The nonprofit model allows Cloudinary to scale the program globally while supporting developers who may not have access to advanced infrastructure training.

Beyond Tutorials: Certification and Community

Unlike casual online courses, the Creators Community incorporates certification and portfolio-building opportunities. Developers who complete the Cloud to Crowd course can earn a certificate and apply to join the broader community.

Inside the Discord environment, members can:

• Participate in live use-case breakdowns

• Attend expert-led webinars

• Join mini-hack events

• Contribute to open-source projects

• Receive mentorship

That blend of structured education and peer collaboration mirrors successful developer ecosystems built by major cloud and API providers.

The difference here is the narrow focus: mastering the media layer of modern applications.

Competitive Context: Developer Ecosystems as Growth Engines

Developer communities have become strategic growth channels for infrastructure platforms. Companies like Stripe, Twilio, and major cloud providers have long invested in developer education as a way to drive adoption.

Cloudinary’s move follows that playbook—but with a specialized focus on image and video infrastructure.

As AI-generated visuals, personalized content, and real-time transformations become common, the complexity of managing media pipelines increases. Platforms that provide both tooling and education stand to benefit from early adoption among developers.

The Creators Community may also serve as a certification signal for hiring managers looking for developers who understand performance optimization and scalable media workflows.

What It Means for the Visual Economy

Digital products are increasingly judged on visual richness and performance simultaneously. That creates a tension: richer media experiences often mean heavier assets.

Cloudinary’s bet is that educating developers in media automation and optimization will become as fundamental as teaching API integration or database design.

If that’s true, the Cloudinary Creators Community could become more than a training program. It could evolve into a talent pipeline for companies building media-intensive applications.

Developers can join through one of the nonprofit partners or independently by completing the Cloud to Crowd course and applying after certification.

For developers who’ve mastered front-end frameworks but never formally studied media performance, this may be a timely addition to their toolkit.

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Political ad tech just got a 2026 upgrade.

OpenX Technologies, Inc. has introduced prioritized access to curated, political-approved inventory built for the 2026 US midterm elections—alongside what it calls a first-to-market partnership with Givsly to power values-based voter targeting.

The pitch: faster activation, brand-safe CTV at scale, and audience construction based on shared values—not just traditional voter files.

In a political cycle expected to bring surging CPMs, inventory shortages, and unpredictable voter behavior, OpenX is positioning itself as a control layer for campaigns that can’t afford to miss prime inventory windows.

Political CTV, Without the Chaos

Election seasons are notoriously volatile for digital media buyers. Political dollars flood into connected TV (CTV), mobile, and web inventory, driving up prices and constraining supply. Publishers often tighten controls on automated political demand, and DSP competition intensifies.

OpenX’s answer centers on curated, prioritized supply. Campaigns get guaranteed access to pre-vetted publishers, including Newsweek, Plex, The E.W. Scripps Company, and Xumo.

According to OpenX, the model reduces path duplication and helps stabilize CPMs during demand spikes. It also claims auction-efficient pricing structured to consistently win supply—even when political demand peaks.

Campaigns can activate across screens in under 24 hours, with ZIP-code-level targeting and localized measurement reporting by county, DMA, and ZIP code.

In practical terms, that’s designed to eliminate the scramble many campaigns face when trying to secure premium CTV inventory at the last minute.

A Shift Toward Values-Based Targeting

The more novel component is OpenX’s partnership with Givsly.

Rather than relying solely on traditional political datasets—often built around party affiliation or historical voter behavior—the integration taps first-party, privacy-conscious signals from Givsly’s network of more than 500 nonprofits.

These aggregated signals, including volunteering and donation activity, allow campaigns to construct voter audiences based on shared values such as environmental protection or women’s empowerment.

The logic is straightforward: voter traits are becoming less predictable, especially outside traditional red-versus-blue divides. Values alignment may provide a more durable signal for message resonance.

Givsly CEO Chad Hickey argues that campaigns need to activate on voter values, not just voter files. OpenX provides the scaled CTV and omnichannel supply; Givsly adds the intent layer.

The combination aims to help campaigns identify contested ZIP codes where values alignment is strongest—a potential edge in tight races.

Built-In Compliance and Governance

Political advertising comes with heightened scrutiny. To address that, OpenX’s offering includes creative compliance tools such as automated ad scanning, political transparency controls, and governance guardrails.

That emphasis matters to premium publishers.

Newsweek’s Chief Revenue Officer Danielle Varvaro noted that the partnership provides the transparency and control required to uphold editorial standards during election cycles. Similarly, Xumo’s programmatic leadership emphasized maintaining brand-safe CTV environments while managing political demand at scale.

In short: premium publishers want political dollars—but on their terms.

Turnkey Packages for DSP Buyers

OpenX is also introducing election-specific inventory bundles pre-optimized for political windows. These turnkey packages are designed to plug directly into major demand-side platforms such as Basis Technologies, IQM, and StackAdapt.

For political buyers under time pressure, pre-packaged inventory with compliance baked in could streamline execution during critical campaign moments.

Competitive Context: CTV as Political Battleground

CTV has rapidly become a must-buy channel in political media strategies. As linear TV audiences fragment, campaigns are shifting budgets toward streaming platforms that offer both scale and data-driven targeting.

But CTV inventory during election surges is finite. Premium publishers increasingly limit open-market access, preferring curated or direct relationships to maintain control over messaging and brand alignment.

OpenX’s claim to be the only platform offering all-direct publisher supply across formats—including CTV—positions it against other SSPs and exchanges competing for political budgets.

The values-based targeting angle also reflects broader industry shifts. As privacy regulations and platform changes constrain third-party data, first-party and contextual signals are becoming more central to campaign strategy.

If traditional voter files grow less predictive, values-driven signals may gain traction—especially in swing districts where micro-messaging can tip outcomes.

What It Means for 2026

With the 2026 midterms approaching, political campaigns are already planning media strategies in a landscape defined by:

• High competition for CTV supply

• Volatile CPMs

• Tight compliance requirements

• Evolving voter segmentation models

OpenX’s combined approach—curated inventory plus values-based targeting—aims to reduce execution risk while improving audience precision.

Whether it becomes a defining feature of the 2026 cycle will depend on performance and adoption. But one thing is clear: political CTV is no longer just about reach. It’s about access, alignment, and speed.

Get in touch with our MarTech Experts.

Maritime risk isn’t getting simpler. Sanctions enforcement, supply chain volatility, and geopolitical tension have turned global shipping lanes into high-stakes data environments.

That’s the backdrop for Windward’s latest leadership move. The maritime intelligence firm announced the appointment of Stuart Strachan as Chairman of the Board, elevating a longtime board member as it sharpens its focus on enterprise expansion and advanced AI capabilities.

For a company positioning itself as a leader in “mission-grade Maritime AI,” the hire looks less ceremonial and more strategic.

A Data Veteran From S&P and IHS Markit

Strachan brings more than two decades of experience across maritime, trade, and data analytics markets. He previously held senior leadership roles at S&P Global and IHS Markit, where he led maritime, trade, and supply chain intelligence businesses serving governments, traders, financial institutions, and shipping operators.

Those platforms are widely used to manage risk, compliance, and operational complexity across global trade flows—precisely the domains where Windward competes and differentiates.

Earlier in his career, Strachan led strategic marketing at Jane's, a well-known source of open-source intelligence (OSINT) for militaries and defense agencies. That background gives him direct exposure to national security use cases—an area increasingly intertwined with maritime analytics.

In short: this is not a generic board appointment. It’s a signal that Windward intends to deepen its position in high-stakes intelligence markets.

Scaling From Alerts to Decision-Ready Intelligence

Windward has built its reputation on applying AI to maritime data—tracking vessel behavior, identifying risk patterns, and supporting sanctions compliance and investigations. Its platform is used by governments, financial institutions, energy companies, and shipping stakeholders.

CEO and Co-Founder Ami Daniel framed Strachan’s appointment as timely. As maritime risk grows more complex and intelligence expectations rise, Windward is shifting from reactive alerts toward predictive, outcome-driven intelligence.

That shift mirrors a broader industry trend. Across sectors, AI platforms are moving beyond dashboards and notifications toward systems that deliver evidence-based recommendations. The language Windward uses—“decision-ready insights”—reflects that ambition.

Strachan, who has served on Windward’s board prior to becoming Chairman, echoed that direction. He described the company as moving from raw data and alerts toward structured, evidence-backed intelligence designed for operational use.

The emphasis on “mission-grade” capabilities is also telling. In maritime AI, accuracy and explainability aren’t marketing features—they’re requirements for compliance, enforcement, and national security applications.

Maritime AI in an Era of Sanctions and Supply Chain Stress

The timing of this leadership change matters.

Global trade flows are increasingly shaped by sanctions regimes, export controls, and geopolitical fragmentation. Dark fleet activity, ship-to-ship transfers, and sanctions evasion tactics have pushed regulators and commercial operators to rely more heavily on advanced analytics.

At the same time, financial institutions and insurers face mounting regulatory scrutiny tied to maritime exposure. That raises demand for platforms capable of delivering audit-ready insights rather than generic risk scores.

Windward’s growth strategy reflects these pressures. The company says its next phase will focus on:

• Expanding enterprise adoption

• Deepening government partnerships

• Advancing agentic Maritime AI capabilities for investigations, compliance, and operational decision-making

The mention of “agentic” AI suggests Windward is exploring systems that go beyond detection—potentially automating investigative workflows or compliance actions.

Competitive Context

The maritime intelligence market includes established information services providers and newer AI-native entrants. Legacy players often bring scale and historical data depth, while AI-first companies emphasize behavioral modeling and predictive capabilities.

Strachan’s experience at S&P Global and IHS Markit bridges both worlds: large-scale information services infrastructure and specialized maritime intelligence operations.

For Windward, that hybrid perspective could help navigate the transition from high-growth tech company to scaled global platform—especially as enterprise buyers demand stability, governance, and long-term roadmap clarity.

What This Means for Windward

Board leadership changes often fly under the radar. In this case, the appointment underscores three priorities:

1. Enterprise credibility – Bringing in a chairman with deep global information services experience reinforces Windward’s positioning with governments and financial institutions.

2. Operational maturity – As the company scales, governance and strategic discipline become as important as innovation.

3. AI evolution – Moving from alerts to predictive, evidence-based intelligence aligns Windward with broader shifts toward outcome-driven AI systems.

If maritime risk continues to intensify—and few expect it to ease—platforms capable of delivering actionable intelligence rather than raw data will likely command growing attention.

Strachan’s appointment suggests Windward intends to be one of them.

Get in touch with our MarTech Experts.

Private equity is back—at least on the surface.

After three sluggish years, global buyout deal value and exits surged in 2025 to their second-highest levels on record, signaling what could be the start of a sustained rebound. But beneath the headline recovery, the math of private equity has changed dramatically.

That’s the central takeaway from the 17th annual Global Private Equity Report by Bain & Company. The firm’s message is cautiously optimistic: momentum is returning, but the industry has hit a structural inflection point. Growth is harder, liquidity is constrained, and investor scrutiny is sharper than ever.

Or as Bain puts it: “12 is the new 5.”

A Record Rebound—With an Asterisk

Global buyout deal value jumped 44% year over year in 2025 to $904 billion (excluding add-ons). Exit value climbed 47% to $717 billion. Both figures rank as the second-highest ever, trailing only private equity’s 2021 peak.

The year was punctuated by headline-grabbing megadeals. A $56.6 billion public-to-private acquisition of Electronic Arts set a new buyout record. Macquarie’s $40 billion sale of Aligned Data Centers to BlackRock and tech consortium partners underscored investor appetite for AI infrastructure assets. Other standout deals included Air Lease ($27.5 billion) and Walgreens Boots Alliance ($23.7 billion).

But here’s the catch: the recovery was narrow.

Just 13 megadeals worth more than $10 billion accounted for $274 billion—roughly 30% of global deal value. Eleven of those took place in the US. Deal count actually fell 6% year over year to 3,018 transactions, even as average disclosed deal size hit a record $1.2 billion.

In other words, 2025 was a year of giants.

Liquidity Logjam Still Haunts the Industry

For all the celebration around deal value, cash returns to investors remain stubbornly weak.

Distributions to limited partners (LPs) as a percentage of net asset value have now stayed below 15% for four consecutive years—a record low stretch for the industry. In 2025, the figure hovered around 14%, a level not seen since the 2008–09 financial crisis.

Meanwhile, the industry is sitting on roughly 32,000 unsold portfolio companies worth an estimated $3.8 trillion. Average holding periods have stretched to around seven years, up from five to six years during the 2010–2021 window.

That backlog is more than an accounting issue. It directly affects fundraising.

With less cash flowing back from older funds, LPs face allocation constraints. Buyout fundraising fell 16% in 2025 to $395 billion, and the number of funds closed dropped 23%, marking a fourth straight year of decline. Investors are becoming choosier, concentrating commitments among large, established managers with consistent top-quartile performance.

The golden decade is clearly over.

The New Math: “12 Is the New 5”

During the 2010s, private equity benefited from a rare alignment of tailwinds: near-zero interest rates, expanding valuation multiples, abundant leverage, and eager investors. In that environment, a typical deal needed just 5% annual EBITDA growth to deliver a 2.5x multiple on invested capital over five years—translating into roughly a 20% internal rate of return.

Today, that math doesn’t work.

Borrowing costs sit in the 8% to 9% range. Leverage ratios are lower, typically 30% to 40%. Purchase multiples remain high, but the era of automatic multiple expansion is largely gone.

To achieve the same 2.5x return benchmark, Bain calculates that deals now require 10% to 12% annual EBITDA growth over five years. Hence the shorthand: “12 is the new 5.”

This shift fundamentally changes the skill set required to win in private equity. Financial engineering alone won’t cut it. Operational improvement, revenue acceleration, technology enablement, and disciplined execution become central—not optional.

Rebecca Burack, Bain’s global head of private equity, puts it plainly: attractive returns now demand sustained double-digit growth. Firms that treat alpha generation as a system, not a slogan, will separate themselves.

Megadeals Mask Structural Pressures

The rebound in 2025 was driven by pent-up deal appetite and $1.3 trillion in global buyout dry powder, much of it aging. Falling interest rates and revived credit markets provided the spark.

Yet much of the equity in megadeals came from outside traditional PE funds—sovereign wealth funds and corporate buyers eager to deploy capital, particularly in AI-linked sectors. That influx of non-buyout capital intensifies competition and dilutes private equity’s share of transactions.

Below the $10 billion threshold, growth was more modest. Deal value excluding megadeals rose 16%. The $1 billion to $5 billion segment grew 29%, while the $5 billion to $10 billion range increased just 6%.

North America drove roughly 80% of overall deal value growth. Europe’s contribution looked comparable only after removing megadeals from the equation.

In short, scale players thrived. The rest of the market remains uneven.

Exits Improve—But Not Enough

Exit value rebounded sharply in 2025, helped by improved macro conditions and strategic demand fueled partly by AI-driven infrastructure needs.

Sponsor-to-strategic exits (sales to corporate buyers) rose 66% globally, with especially strong growth in North America and Europe. Sponsor-to-sponsor deals grew 21% worldwide, though heavily influenced by a few outsized transactions.

IPOs rose 36% from a very low base but remain a minor exit channel due to market volatility and execution risk.

Secondaries and continuation vehicles (CVs) continued expanding as alternative liquidity mechanisms. GP-led continuation vehicles grew 62% year over year and have expanded at a 37% annual rate since 2022. While still under 10% of total exit value, CVs are increasingly used to generate partial liquidity without fully exiting assets.

Despite these improvements, overall net cash flow for private equity only modestly exceeded breakeven in 2025. The liquidity challenge is easing—but far from solved.

Fundraising: Survival of the Fittest

Even as total private capital fundraising reached $1.3 trillion in 2025—boosted by infrastructure funds—buyout fundraising weakened.

LPs still value private equity’s diversification benefits and long-term outperformance versus public markets. But they’re demanding clearer strategies, consistent distributions, and demonstrable alpha.

Competition for capital is intensifying as costs rise. Leading firms are investing heavily in sector expertise, AI capabilities, professionalized investor relations, and technology platforms. At the same time, management fees are under pressure and LPs are pushing for more co-investment opportunities.

The bar is rising on both performance and communication.

2026: Cautious Optimism

Bain sees 2026 shaping up positively. Interest rates are trending downward, pipelines are stocked, stock markets remain elevated, and credit markets have stabilized—assuming no unexpected macro shock.

But this is not a simple cyclical rebound. It’s a structural reset.

The private equity model that thrived in the 2010s—leveraged, multiple-expansion-driven, and capital-rich—has given way to a more competitive, operationally intensive era.

Firms that can identify targets years in advance, conduct “full potential due diligence,” and systematically unlock revenue and operational improvements will likely outperform.

The rest may find that second-highest-on-record deal values don’t guarantee first-rate returns.

Get in touch with our MarTech Experts.

Retail marketers don’t need more dashboards. They need answers.

That’s the pitch from Bluecore, which today introduced Marketing Agent, a retail-focused agentic AI system designed to move teams from performance analysis to execution in seconds. Built directly into BluecoreAI, the new tool promises to collapse hours of reporting, dashboard hopping, and campaign troubleshooting into a conversational workflow that actually tells marketers what’s happening—and what to do next.

In a market crowded with AI copilots and generative assistants, Bluecore is betting that context, not cleverness, will win.

From Insight to Action—Without the Spreadsheet

Retail marketing teams have no shortage of data. What they lack is time.

Weekly business reviews, audience troubleshooting, campaign diagnostics—these processes often require pulling reports from multiple systems, interpreting shifting metrics, and aligning teams on what actions to take. According to Bluecore, Marketing Agent automates much of that work by delivering structured performance snapshots, root-cause explanations, and prioritized recommendations in one unified interface.

Instead of asking teams to interpret dashboards, the system provides conversational diagnostics that explain:

• What changed

• Why it changed

• What to do next

It’s designed to function as both analyst and operator—an AI layer that not only identifies performance shifts but connects them directly to activation workflows.

CEO Fayez Mohamood framed it bluntly: retail marketers didn’t ask for “another AI widget.” They asked for clarity. Marketing Agent, he argues, delivers practical, trustworthy AI grounded in unified retail data rather than surface-level campaign metrics.

Why Bluecore Thinks It’s Different

The AI assistant category is getting crowded. Platforms from CRM giants to standalone martech vendors are racing to layer generative interfaces on top of reporting dashboards. But many of those tools rely on partial datasets or generalized industry models.

Bluecore’s differentiator, at least on paper, is its retail-native data foundation.

Marketing Agent operates on a unified dataset that includes identity resolution, shopper behavior, lifecycle stages, transaction history, and catalog data. That broader context allows the system to go beyond performance summaries and into diagnostic intelligence—identifying root causes across audiences, campaigns, and merchandising variables.

Because diagnosis and activation share the same data backbone, recommended actions are grounded in the same definitions and metrics that produced the analysis. Bluecore also emphasizes built-in guardrails to maintain metric consistency and reduce AI hallucinations—a growing concern as generative systems become embedded in operational workflows.

Under the hood, the system uses a coordinated set of specialized agents. One analyzes performance trends. Another diagnoses root causes. A third recommends next steps. Together, they aim to create a continuous loop from insight to execution.

In practical terms, that means fewer meetings debating what went wrong—and more immediate action.

A Single Surface for Retail Performance

Marketing Agent consolidates three core capabilities:

1. Exploratory Diagnostic Analysis – Structured, logic-based analysis across campaigns, audiences, and channels.

2. Conversational Context Retention – Follow-up questions preserve context, avoiding the “reset” problem common with generic AI tools.

3. Direct Path to Activation – Insights connect directly to operational workflows, shortening the distance between decision and execution.

The goal isn’t just faster reporting. It’s operational leverage.

Andrew Rickert, VP of Digital Marketing at QVC Group, says the system has already changed internal workflows. Instead of spending hours pulling reports and interpreting dashboards, his team receives instant diagnostics explaining performance shifts and recommended actions.

That kind of automation could prove particularly valuable during high-volume retail periods—holiday sales, promotional events, product launches—when speed matters more than slide decks.

Built From Retailer Feedback, Not Lab Experiments

Bluecore says Marketing Agent was developed in response to direct retailer input, including insights gathered from a recent JAM Sesh event with more than 50 retail leaders.

Across those conversations, marketers consistently asked for help answering three recurring questions:

• What happened?

• Why did it happen?

• What should we do about it?

These aren’t theoretical problems. Weekly business reviews alone can consume entire mornings across marketing teams. Multiply that across audience analysis, channel optimization, and campaign troubleshooting, and the time drain becomes significant.

Marketing Agent attempts to remove that bottleneck entirely.

The Bigger Picture: Agentic AI in MarTech

Bluecore’s launch lands amid a broader shift toward agentic AI systems—tools that don’t just generate content or summarize reports, but autonomously analyze, recommend, and act.

The industry is moving beyond “copilot” interfaces toward AI systems embedded directly into workflows. Major platforms across CRM, commerce, and advertising are introducing agents capable of executing tasks rather than merely suggesting them.

But retail marketing poses unique challenges: fragmented data, omnichannel complexity, and fast-moving consumer behavior. A generic AI assistant trained on broad industry data often lacks the context needed to deliver precise, actionable diagnostics.

Bluecore’s bet is that vertical depth beats horizontal breadth.

If Marketing Agent performs as advertised, it could reduce reliance on manual analytics workflows and shift marketing teams toward a more continuous optimization model—one where diagnostics and execution are tightly linked.

Availability and Market Impact

Marketing Agent is available now to Bluecore clients.

For retailers already using Bluecore’s identity and customer movement platform, the addition effectively adds an AI operating layer on top of existing data infrastructure. For competitors, it raises the bar: dashboards and campaign summaries may no longer be enough.

As AI adoption accelerates in retail marketing, differentiation will likely hinge on three factors:

• Data depth

• Diagnostic reliability

• Operational integration

Bluecore is positioning Marketing Agent squarely at the intersection of all three.

If it delivers, retail marketers may finally spend less time explaining performance—and more time improving it.

Get in touch with our MarTech Experts.

AI-driven reputation defense firm Cyabra Strategy Ltd. is stepping deeper into the fight against synthetic media manipulation with the formation of its new Brand & Entertainment Council, a high-profile advisory group aimed at combating AI-generated disinformation targeting celebrities and global brands.

The council brings together leaders from communications, analytics, and entertainment, including:

• Jonny Bentwood, President of Data & Analytics at Golin

• Mike G, Partner and Talent Agent at United Talent Agency

• Arthur Stark, former President of Bed Bath & Beyond

Why Now?

The council launches amid a surge in AI-powered impersonation, deepfakes, and coordinated fake-account amplification campaigns. Recent high-profile incidents involving celebrities such as Tom Hanks and Scarlett Johansson highlight how convincingly AI can replicate public figures’ likenesses without consent. Brands haven’t been spared either—Starbucks has reportedly faced fake executive announcements and orchestrated boycott campaigns that created stock volatility and reputational damage.

As AI-generated content becomes cheaper and more scalable, a single manipulated post can trigger real-world consequences—lost revenue, legal exposure, and long-term brand erosion.

What the Council Will Do

According to CEO and co-founder Dan Brahmy, the advisory group will:

• Provide strategic oversight on emerging digital threats

• Help shape ethical AI and authenticity standards

• Guide development of next-generation detection tools

• Promote industry-wide awareness around synthetic manipulation

Cyabra’s core platform specializes in real-time detection of coordinated inauthentic behavior, fake accounts, and AI-generated content. By pairing its analytics engine with frontline entertainment and brand expertise, the company aims to stay ahead of increasingly sophisticated campaigns.

Bigger Picture

The formation of the council signals a broader industry shift: disinformation defense is no longer just a political or cybersecurity concern—it’s a brand, talent, and shareholder issue. As entertainment and retail become prime targets for algorithm-driven influence operations, proactive monitoring is quickly moving from optional to essential.

Cyabra has also entered into a business combination agreement with Trailblazer Merger Corporation I (NASDAQ: TBMC), positioning the company for its next growth phase.

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As AI assistants increasingly replace traditional search results, brands are discovering a harsh reality: measuring AI visibility is easy. Influencing it is not.

Mersel AI, Inc. this week launched its Generative Engine Optimization (GEO) execution platform, aimed squarely at helping companies improve how they appear inside AI-generated answers and recommendations across major assistants.

That includes platforms like ChatGPT, Perplexity AI, Gemini, and Claude—tools that are rapidly becoming the first stop for product research, vendor comparisons, and category discovery.

The pitch is straightforward: visibility dashboards don’t fix invisibility. Execution does.

From AI Visibility Metrics to AI Eligibility

Over the past year, a wave of AI visibility tools has emerged, promising to track brand mentions, prompt-level position, and share of voice inside generative AI answers. For marketing and growth teams, that data can be illuminating—and occasionally alarming.

But as Mersel AI points out, simply knowing you’re absent from AI responses doesn’t mean you know how to change it.

Large language models cite and summarize sources based on structured clarity, semantic consistency, and credibility signals. If your product data is ambiguous, inconsistently presented, or thinly supported off-site, measurement alone won’t move the needle.

Mersel AI’s solution is an “agent-as-a-service” model designed to operationalize GEO. Instead of licensing a tool and assigning another dashboard to an already overloaded team, the company positions itself as an execution layer that ships changes continuously.

Founder Joseph Wu frames the issue bluntly: many teams can measure where they’re missing in AI answers, but they lack the infrastructure to implement the fixes at scale.

A Four-Layer Approach to AI Citation

The GEO execution platform focuses on four operational pillars that influence how AI systems interpret and recommend brands.

1. A Machine-Readable Layer for Websites

Rather than requiring a full website rebuild, Mersel AI adds a structured, machine-readable layer over existing sites. This includes schema markup, structured data, and semantic signals designed to clarify product attributes, pricing context, policies, and positioning.

The goal is to reduce ambiguity. AI systems favor content that is easier to parse and less prone to misinterpretation. If a product’s specifications or policies are inconsistently formatted across pages, models may hesitate to summarize or cite them confidently.

2. Content Structured for AI Summarization

Traditional SEO content often prioritizes keyword density and long-form coverage. GEO content, by contrast, must be extractable.

Mersel AI supports recurring publication of prompt-aligned content built around real AI query patterns—comparisons, category overviews, use cases, and decision-stage questions. The structure is engineered for summarization, enabling language models to lift key points with minimal friction.

In practice, that means clear fact blocks, consistent terminology, and tightly scoped explanations that map cleanly to how AI assistants generate responses.

3. Off-Site Trust Signals

AI systems don’t rely solely on on-page content. They cross-reference review sites, social platforms, and editorial sources to validate claims and establish credibility.

Mersel AI says it strengthens third-party presence through internal agentic tools that reinforce brand signals across relevant external platforms. In crowded categories where messaging converges, these signals may influence whether a brand is cited as a recommendation or omitted altogether.

4. Measurement Tied Directly to Iteration

Unlike standalone monitoring tools, Mersel AI connects cross-platform AI visibility tracking to shipped updates. It measures brand-mention rates, prompt-level positioning, and competitive share of voice—then uses those insights to guide subsequent changes.

This creates a feedback loop: measure, implement, reassess, repeat.

Why GEO Is Gaining Momentum

Generative Engine Optimization is emerging as a parallel discipline to traditional SEO and Answer Engine Optimization (AEO). While SEO targets ranking positions in search results, GEO targets presence within AI-generated narratives.

The stakes are rising quickly. As conversational interfaces become default research tools, fewer users may scroll through multiple links. Instead, they rely on summarized answers and curated recommendations.

For brands, that means the battle for visibility is shifting from page rankings to citation eligibility.

The challenge is that AI ecosystems evolve constantly. Model updates, prompt trends, and citation behaviors can change without notice. For many companies, building an internal GEO team to track and respond to these shifts may be impractical.

Mersel AI is betting that outsourcing execution—rather than just analytics—will resonate with organizations that need continuous adaptation without expanding headcount.

The Bigger Shift: Tools vs. Outcomes

The broader marketing technology landscape is moving from software licensing to outcome-based services. AI tooling has lowered the barrier to insight, but not necessarily to impact.

Mersel AI’s agent-as-a-service positioning reflects that shift. Instead of adding another interface to the stack, it aims to deliver iterative implementation tied directly to AI platform behavior.

If AI assistants continue to displace traditional search journeys, GEO may become less of a niche experiment and more of a baseline requirement.

For now, Mersel AI is staking its claim early in what could become a highly competitive segment: helping brands not just be visible to AI—but be chosen by it.

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