Consensus, the Demo Automation platform best known for letting buyers explore products on their own terms, is betting that gap can’t be closed by software alone. The company announced a strategic partnership with NewEdge Growth, a RevOps and go-to-market consulting firm that works with B2B and private equity–backed companies to design and scale modern revenue engines.

The partnership aims to make demo automation a structural part of RevOps, not just a sales enablement add-on—connecting buyer intent signals directly to GTM workflows across CRM, sales engagement, and analytics systems.

Why demos are still a bottleneck in modern B2B sales

Despite years of investment in automation, the product demo remains one of the most resource-intensive steps in the B2B funnel. Presales teams are stretched thin, sales cycles stall waiting for availability, and buyers increasingly want to self-educate before talking to a rep.

Consensus has built its business around that tension. Its platform automates product demos so buyers can explore asynchronously, while sales teams gain visibility into who engaged, what they viewed, and how intent is forming across stakeholders.

What’s been missing, however, is tight integration into RevOps strategy—how those signals are operationalized across forecasting, prioritization, and pipeline management.

That’s where NewEdge Growth comes in.

Bridging RevOps design and execution

NewEdge Growth specializes in architecting and integrating RevOps systems across complex B2B tech stacks, including CRM, sales engagement platforms, and analytics tools. Through the partnership, joint customers get a more unified approach: RevOps frameworks designed with demo automation baked in from the start.

Rather than treating demos as a one-off sales activity, the combined offering positions them as a data-generating asset inside the revenue engine.

Consensus’s Demolytics plays a central role here. The engagement data—who watched, for how long, and which features mattered—can be fed into RevOps workflows to help teams:

• Identify real buying groups earlier

• Prioritize deals based on demonstrated intent

• Reduce time spent on low-probability opportunities

• Scale presales without adding headcount

In a market where efficiency matters more than raw growth, that signal-driven approach is becoming essential.

Making demo automation operational, not optional

“We’re seeing too many teams invest in great technology without the strategy to fully leverage it,” said Adam Freeman, SVP of Global Partnerships & Strategic Alliances at Consensus. “This partnership is about making demo automation a core part of the revenue engine—not a disconnected tool.”

That distinction is subtle but important. Many B2B organizations already use demo automation in pockets, often driven by sales or marketing teams independently. The result is fragmented adoption and underutilized data.

By embedding Consensus into NewEdge Growth’s Tech Stack Services and RevOps as a Service offerings, demo automation becomes part of the system design—not an afterthought.

Why this matters for PE-backed and high-growth companies

The partnership also reflects broader pressure coming from private equity and boards. PE-backed companies are increasingly focused on revenue efficiency, predictability, and scalability, especially as hiring slows and CAC remains elevated.

Presales-heavy models don’t scale well under those constraints. Demo automation, when properly integrated, offers a way to support more pipeline without linearly increasing cost.

For Consensus, the partnership creates a strategic channel into organizations already investing in RevOps transformation. For NewEdge Growth, it adds a proven automation layer to help clients modernize sales execution faster.

Buyer control meets revenue signal

At a philosophical level, the partnership aligns with how B2B buying has changed.

“Modern buyers want control. Revenue teams need signal,” said Blake Brock, Founder & COO of NewEdge Growth.

Asynchronous demos give buyers autonomy, while Demolytics provides sellers with behavioral insight that’s often more reliable than form fills or surface-level engagement metrics.

When those insights are tied directly into RevOps workflows—routing, scoring, forecasting—the “next best action” becomes clearer, and deals move with less friction.

Competitive context: RevOps is consolidating

The RevOps ecosystem is consolidating around platforms that do more than collect data—they need to orchestrate action. CRM alone isn’t enough. Neither is a standalone enablement tool.

Consensus’s move mirrors a broader trend where point solutions are being pulled deeper into the revenue stack, either through partnerships or platform expansion. Vendors that can prove they influence cycle time, win rates, and pipeline quality—not just activity—are the ones gaining traction.

By aligning with a RevOps consultancy rather than another software vendor, Consensus is signaling that adoption and execution matter as much as features.

The takeaway

The Consensus–NewEdge Growth partnership isn’t about adding another integration. It’s about redefining where demo automation belongs in the B2B revenue model.

As buying becomes more self-directed and revenue teams are asked to do more with less, demos can no longer sit on the edge of the funnel. When automated demos are designed into RevOps from day one, they become a source of signal, scale, and speed.

For B2B organizations struggling to align strategy with execution, that shift may be exactly what the revenue engine needs.

Get in touch with our MarTech Experts.

The Data+AI security company announced a new AI-powered classification taxonomy designed to unify how enterprises identify, categorize, and protect sensitive data across modern data stacks and AI-driven environments. Alongside it, Symmetry introduced expanded “Bring Your Own AI” (BYOAI) capabilities, giving large organizations more control over how and where AI-powered classification runs.

Together, the announcements mark a strategic shift away from proprietary, vendor-specific classification models toward something the industry has largely lacked: a shared, extensible standard for data and AI security.

A single taxonomy for a fragmented security landscape

At the heart of Symmetry’s announcement is a comprehensive classification framework that serves as the backbone of its DataGuard platform. The taxonomy supports:

• 400+ sensitive data identifiers, spanning PII, PHI, PCI, financial data, credentials, and intellectual property

• 500+ semantic data types, including contracts, board documents, healthcare records, financial filings, and legal documents

• Regulatory mappings across GDPR, CCPA, HIPAA, PCI DSS, SOC 2, and emerging AI governance frameworks

• Privacy data elements, unified into a single model

The goal is straightforward but ambitious: replace the patchwork of incompatible taxonomies that force enterprises to translate policies across multiple tools, clouds, and vendors.

For security and privacy teams, that translation work has become a hidden tax—consuming time and increasing risk as data sprawls across SaaS apps, data lakes, warehouses, and AI pipelines.

Moving toward open standards, not closed ecosystems

Symmetry isn’t just introducing a new taxonomy—it plans to open source it, along with supporting datasets, to encourage industry-wide standardization and benchmarking.

In a notable step toward collaboration, the company has already integrated the Fides privacy-by-code taxonomy into its broader model. The combined taxonomy and corpus of test data will be released as an open-source project, with governance and benchmarking details expected in the coming weeks.

That approach directly challenges the status quo, where most data security vendors maintain proprietary classification schemes that don’t interoperate.

“Vendor-specific taxonomies force organizations to maintain multiple overlapping frameworks and create unnecessary friction,” said Sameer Sait, Senior Director of Information Security at Stanley 1913. “An open, standards-based taxonomy addresses a fundamental problem the entire industry faces.”

Why classification matters more in the AI era

Classification has always been foundational to data security—but AI has raised the stakes.

Large language models, analytics pipelines, and agent-based systems consume vast amounts of enterprise data. Without consistent classification, organizations struggle to answer basic questions: What data is sensitive? Where does it live? Who—or what—can access it?

Symmetry CEO Dr. Mohit Tiwari framed the issue bluntly.

“The data security industry has a taxonomy problem. Organizations waste resources translating between incompatible approaches instead of securing data.”

His comparison is telling. Tiwari likens Symmetry’s vision to a “PyTorch moment for data security”—a compact specification layer that abstracts complexity while enabling portability.

Just as PyTorch allows AI practitioners to define models once and deploy them across GPUs or TPUs, an open data security taxonomy would let privacy and security teams define policies once and enforce them everywhere—from Databricks Unity Catalog and Snowflake to AWS IAM, Kubernetes OPA rules, and DLP systems.

From policy statements to technical enforcement

One of the most compelling implications of the taxonomy is its role in bridging human policy and machine enforcement.

Today, high-level directives—such as “vendors cannot access customer data”—require manual translation into dozens of disconnected systems. That process is slow, error-prone, and difficult to audit.

Symmetry’s approach aims to turn those directives into policy-as-code, automatically generating permissions, access controls, network rules, and audit configurations across the stack.

This isn’t just about compliance speed. It’s about making governance scalable in environments where data and AI systems change faster than humans can document them.

Benchmarking data security like AI models

By releasing evaluation datasets alongside the taxonomy, Symmetry is also pushing for something rare in security: reproducible benchmarking.

In AI, shared benchmarks drove rapid improvement by making performance measurable and comparable. Data security classification, by contrast, has largely operated without standardized testing.

“Data security needs the same approach,” said Tiwari. “Open benchmarks allow the community to test, compare, and continuously improve classification accuracy.”

If adopted broadly, that could pressure vendors to compete on measurable outcomes rather than opaque claims.

“Bring Your Own AI” to avoid lock-in

Complementing the taxonomy is Symmetry’s expanded BYOAI support, which allows customers to run classification using their own AI infrastructure—whether that’s Azure OpenAI, AWS Bedrock, Google Vertex AI, or private GPU environments.

This matters for two reasons: data sovereignty and control.

Many enterprises are reluctant to send sensitive data through third-party cloud pipelines. Symmetry’s architecture brings AI-powered classification to where the data already lives, rather than forcing data to move.

That stands in contrast to cloud-dependent Data Security Posture Management tools that rely on centralized vendor infrastructure—an approach that can introduce compliance and trust concerns.

A differentiated position in a crowded market

The data security market is crowded with DSPM, DLP, and AI governance tools, many of which promise visibility but stop short of standardization.

Symmetry is carving out a distinct position: comprehensive classification, infrastructure flexibility, and an open standard designed to outlive any single vendor.

Whether the industry rallies around this taxonomy remains to be seen. But the problem it addresses—fragmented classification in a world of exploding data and AI usage—is real and growing.

The takeaway

Symmetry Systems isn’t just shipping a feature. It’s challenging a deeply entrenched model of proprietary data classification at a moment when AI is forcing enterprises to rethink governance from the ground up.

If its open taxonomy gains traction, it could do for data security what shared frameworks did for AI development: turn fragmented experimentation into a more measurable, interoperable discipline.

For enterprises grappling with AI-driven data sprawl, that shift can’t come soon enough.

Get in touch with our MarTech Experts.

As B2B buying journeys become longer, messier, and more group-driven, Forrester is sending a clear message to vendors: revenue marketing platforms must evolve—or risk irrelevance.

In its newly released The Forrester Wave™: Revenue Marketing Platforms for B2B, Q1 2026, Forrester Research named 6sense® a Leader, recognizing the company’s agent-powered Revenue Intelligence platform for its depth, data sophistication, and ability to operationalize how modern buying decisions actually happen.

The recognition reinforces 6sense’s positioning as a go-to platform for large B2B enterprises navigating a reality where buyers form preferences early, engage anonymously, and expect highly personalized interactions long before they ever talk to sales.

Why this Wave matters now

Revenue marketing platforms have moved beyond lead scoring and campaign tracking. According to Forrester, today’s systems must reflect how B2B buyers really behave: researching independently, acting as buying groups rather than individuals, and shifting signals constantly across channels.

The Wave evaluated vendors that offer unified revenue marketing platforms, generate meaningful market revenue, and are frequently cited by Forrester’s enterprise clients. In other words, this wasn’t a checklist exercise—it was a test of who can actually support enterprise go-to-market teams under real-world conditions.

Forrester’s conclusion: 6sense stands out for organizations looking to unify marketing and sales engagement around predictive, AI-driven workflows.

What Forrester highlighted about 6sense

In the report, Forrester states that 6sense “best fits B2B enterprises seeking a data-rich, AI-powered platform to unify marketing and sales engagement and operationalize predictive, orchestrated revenue workflows.”

That’s not faint praise. It reflects a shift away from siloed marketing automation and CRM add-ons toward platforms that act as a connective layer across the entire revenue engine.

Forrester also described 6sense’s offering as “among the most complete” in the evaluation, calling out its Intelligent Workflows as a key differentiator. These workflows unify data, intent signals, and orchestration inside what Forrester describes as an adaptive, AI-driven canvas.

In practical terms, that means teams can move from insight to action without jumping between systems—or relying on static rules that quickly go stale.

Perfect scores where it counts

6sense received the highest possible score in 14 evaluation criteria, including:

• Data capabilities

• Anonymous audience segmentation

• Adaptive workflow and journey orchestration

Those areas are increasingly critical as buying activity shifts earlier and becomes harder to observe. Anonymous research, once treated as a blind spot, is now one of the most valuable signal sources for revenue teams—if they can act on it.

By scoring highly across data and orchestration, 6sense is positioning itself not just as an analytics layer, but as an execution engine for modern GTM teams.

Customer feedback reinforces the narrative

Beyond product capabilities, 6sense also received above-average customer feedback in the evaluation. Customers described the platform as “strategic, responsive, and deeply integrated,” with particular praise for data accuracy, predictive modeling, and measurable pipeline impact.

That emphasis on outcomes matters. As CFOs scrutinize MarTech spend more closely, platforms that can directly tie activity to pipeline quality and revenue velocity have a clear advantage.

According to 6sense, customers use the platform to uncover in-market accounts earlier, engage buying groups more effectively, and improve conversion rates—ultimately winning larger deals and closing them faster.

The broader competitive landscape

The revenue marketing platform category has become increasingly crowded, with vendors racing to layer AI onto legacy ABM, MAP, and CRM stacks. Many promise intelligence; fewer deliver orchestration that scales across enterprise complexity.

What sets 6sense apart, according to Forrester’s analysis, is the tight coupling of data, prediction, and action. Rather than treating AI as an add-on, the platform uses it to continuously adapt workflows based on changing buyer behavior.

That approach aligns with where the market is heading. Static journeys and rigid funnels are giving way to systems that respond in real time—because buyers do.

A signal for enterprise GTM teams

For enterprise B2B organizations reevaluating their revenue tech stacks in 2026, this Wave offers a clear signal. Platforms built for yesterday’s linear buying models are struggling to keep up with today’s reality of distributed decision-making and early-stage intent.

“B2B buying has fundamentally changed, and go-to-market teams need systems built for how decisions are made today,” said Chris Ball, CEO of 6sense.

The Forrester recognition suggests that 6sense is resonating with that need—particularly among enterprises looking to unify marketing and sales around shared, actionable intelligence.

The takeaway

Being named a Leader in Forrester’s Revenue Marketing Platforms Wave isn’t just about feature depth. It reflects alignment with how B2B growth actually happens in 2026: anonymously, collaboratively, and long before a demo request.

For 6sense, the recognition reinforces its strategy of building an agent-powered platform that doesn’t just surface insights, but helps teams act on them—faster and with greater confidence.

For the market, it’s another sign that revenue marketing is no longer about managing campaigns. It’s about orchestrating decisions.

Get in touch with our MarTech Experts.

Attentive is doubling down on a simple but increasingly critical idea: in modern marketing, identity starts on mobile—even when shopping doesn’t.

The omnichannel marketing platform announced a major expansion of its patented two-tap™ technology, extending it from mobile-only flows to desktop shopping experiences for mobile subscribers. At the same time, Attentive rolled out a slate of new tools designed to help brands navigate tightening platform rules, shifting inbox behavior, and rising expectations for personalization across SMS, email, and beyond.

Together, the updates signal a clear strategic bet: as inbox filtering, privacy controls, and AI-driven experiences reshape digital marketing, brands that own durable, mobile-first customer relationships will have a structural advantage.

Why two-tap on desktop matters now

Two-tap™ has long been one of Attentive’s signature differentiators. The patented technology lets consumers subscribe to SMS marketing with minimal friction—typically two quick taps on their phone—dramatically increasing opt-in rates compared to traditional forms.

Until now, that experience lived primarily on mobile. But consumer behavior has changed. Shoppers increasingly browse on desktop at work or at home, then complete purchases—or engage with brands—on mobile.

By extending two-tap™ to desktop via a QR-based opt-in flow, Attentive is targeting a common blind spot in ecommerce: high-intent web traffic that never converts into a lasting, owned relationship.

Instead of asking desktop shoppers to fill out forms or remember to opt in later, brands can now prompt them to scan a QR code and instantly subscribe on their phone. The result is a cleaner handoff between devices—and a higher-quality subscriber entering the brand’s mobile ecosystem.

“Expanding two-tap™ to desktop increases the surface area for list growth and strengthens the long-term value of brands’ owned audiences,” said Nakul Narayan, Attentive’s Chief Product Officer.

Mobile identity over channel-first marketing

The two-tap expansion reflects a broader shift in how Attentive sees the market. Rather than treating SMS, email, push, and ads as separate channels, the company is positioning mobile identity—the phone number and its associated signals—as the connective tissue across the customer journey.

“Platform changes and shifting consumer habits are forcing marketing into a new era that favors mobile-first identity,” said Eric Miao, Attentive’s Chief Strategy Officer.

That framing is notable. As cookies fade, inbox algorithms tighten, and paid acquisition costs rise, first-party data has become the most valuable asset a brand can own. Attentive’s pitch is that mobile—specifically SMS—offers the most direct, resilient path to capturing and activating that data.

iOS inbox filtering raises the stakes

The timing of these updates is no accident. Apple’s continued evolution of iOS inbox behavior has made message visibility less predictable, particularly for promotional content.

According to Attentive’s internal data, messages routed into filtered inbox experiences can suffer 30–40% lower clickthrough and conversion rates. Combine that with the reality that 81% of consumers ignore irrelevant messages, and the margin for error gets thin fast.

To address this, Attentive introduced new inbox visibility tools that help brands identify messages at risk of filtering and apply proactive mitigations before performance drops. While the company hasn’t disclosed the exact mechanics, the focus is on preserving deliverability without resorting to volume-driven tactics that erode trust.

This aligns with a broader industry trend: inbox providers are increasingly rewarding relevance, consistency, and compliance over raw send frequency.

Compliance without the operational headache

Alongside visibility, compliance is becoming more complex—especially for brands operating across regions with different quiet-hour rules and consent requirements.

Attentive’s new capabilities aim to reduce that burden through automation rather than manual configuration. Key additions include:

• Automated state-level quiet hours, reducing the risk of sending messages at non-compliant times

• Improved location detection, minimizing operational lift for distributed audiences

• Audience size controls, helping marketers balance reach, budget, and performance

These features reflect a reality many teams face: compliance failures are rarely strategic—they’re operational. Automating guardrails allows marketers to move faster without increasing risk.

AI shifts from novelty to workflow engine

AI also plays a larger role in Attentive’s latest updates, but with a practical tilt. Rather than positioning AI as a creative replacement, the platform is using it to compress time-to-value.

New AI-driven features include:

• AI email template generation for faster, on-brand creation

• AI-powered campaign and journey enhancements to test, learn, and optimize with less manual effort

• Workflow intelligence that adapts messaging across SMS, email, push, ads, and loyalty integrations like Yotpo

The emphasis here is efficiency. As marketing teams are asked to do more with fewer resources, AI that reduces setup and iteration time is becoming table stakes.

Bridging online and offline experiences

Attentive also introduced barcode generation for email, allowing brands to connect digital campaigns to in-store experiences without custom HTML. While not flashy, it addresses a persistent challenge for omnichannel retailers: tying online engagement to physical-world behavior.

In an era where attribution is increasingly probabilistic, even small improvements in online-to-offline linkage can unlock more confident decision-making.

Proof points from the field

For brands already using two-tap™, the expansion to desktop builds on proven results. TeePublic and Redbubble report that Attentive’s approach has driven roughly 2x higher opt-in rates, a meaningful lift as inbox filtering and sender trust become stricter.

That kind of performance matters less for vanity metrics and more for durability. High-intent subscribers are more likely to engage, convert, and stick around—exactly the signals platforms reward.

The bigger MarTech picture

Attentive’s announcement highlights a broader recalibration underway in MarTech. Growth is no longer about adding more channels; it’s about owning fewer, stronger relationships and activating them intelligently.

As platform rules harden and consumers become more selective, frictionless consent, inbox visibility, and relevance aren’t optimizations—they’re prerequisites.

By extending two-tap™ beyond mobile screens and reinforcing its platform with compliance and AI-driven workflows, Attentive is betting that the future of personalization isn’t louder marketing. It’s smarter, more respectful, and rooted in identity brands truly own.

For marketers navigating mobile’s next era, that distinction may define who keeps their reach—and who slowly loses it.

Get in touch with our MarTech Experts.

Rocket Doctor AI Inc., a physician-built digital health company operating at the intersection of artificial intelligence and virtual care, is stepping up its market visibility.

The company (CSE: AIDR; OTC: AIRDF; Frankfurt: 939) announced it has engaged Vancouver-based Danayi Capital Corp. to provide digital marketing services over a two-month period starting February 9, 2026. The agreement comes with an upfront payment of USD $125,000 and is focused on online investor outreach and digital advertising via WallStreetLogic.com.

While short in duration, the move signals a broader push by Rocket Doctor AI to sharpen its narrative with investors and the market—particularly as competition intensifies across AI-enabled healthcare platforms.

A targeted marketing push, not a speculative play

According to the company, Danayi will operate strictly as a third-party service provider. The firm holds no direct or indirect ownership in Rocket Doctor AI or its securities, and all parties are described as operating at arm’s length.

That distinction matters. In today’s small-cap and emerging-tech markets, marketing engagements often attract scrutiny from regulators and investors alike. Rocket Doctor’s disclosure—covering Danayi’s compensation, scope of work, and lack of equity interest—reads like a preemptive move to reinforce transparency.

The marketing effort will focus on digital campaigns and online advertising, an increasingly common tactic among health-tech firms looking to stand out in crowded capital markets without resorting to splashy product announcements.

Equity incentives signal continued reliance on external expertise

Alongside the marketing engagement, Rocket Doctor AI also disclosed new equity compensation grants to consultants.

The company issued:

• 33,353 stock options, exercisable at $0.77 per share, with a three-year term

• 205,065 restricted share units (RSUs), also valid for three years

Both the options and RSUs vest over one year and were granted under the company’s existing share compensation plans.

While modest in size, the grants point to Rocket Doctor’s ongoing reliance on external consultants—a common approach among growth-stage AI and healthcare firms balancing speed, specialization, and cost control. Rather than expanding headcount aggressively, many companies in this space are opting for flexible, incentive-aligned expertise.

Why this matters in the AI health market

Digital health is no longer just about virtual visits. The sector is shifting toward AI-powered decision support, automation, and scalable care delivery—areas where Rocket Doctor AI is positioning itself aggressively.

At the center of the company’s technology stack is its Global Library of Medicine (GLM), a clinically validated AI decision-support system developed with input from hundreds of physicians worldwide. Unlike consumer-facing symptom checkers, GLM is positioned as a professional-grade tool designed to support clinical judgment rather than replace it.

That physician-first framing is increasingly important. As regulators and healthcare systems scrutinize AI tools for safety and bias, platforms built with direct clinician involvement are gaining credibility over black-box alternatives.

Beyond AI: a full-stack virtual care platform

Rocket Doctor AI’s ambitions extend beyond algorithms. Through Rocket Doctor Inc., the company operates an AI-powered digital health platform and marketplace designed to help physicians launch and manage independent virtual or hybrid practices.

To date, the platform has supported:

• 300+ licensed physicians

• 700,000+ patient visits

The value proposition is straightforward but timely: reduce administrative burden, restore physician autonomy, and expand patient access—particularly in underserved regions.

In Canada, that means rural and remote communities with limited access to family doctors. In the U.S., it includes patients covered by Medicaid and Medicare, where provider shortages and reimbursement complexity often limit care options.

Marketing meets mission

The decision to invest in digital marketing comes as healthcare AI companies face a dual challenge: proving clinical value while also communicating that value clearly to investors, partners, and regulators.

Rocket Doctor AI’s technology story—AI decision support, large language models, connected medical devices—sits squarely within some of the most hyped (and scrutinized) areas of modern healthcare. Cutting through the noise requires not just innovation, but disciplined messaging.

By engaging Danayi Capital for a defined, short-term campaign, Rocket Doctor appears to be testing how targeted digital outreach can amplify its story without overcommitting resources.

A crowded field with rising stakes

Rocket Doctor AI is far from alone in this race. Teladoc, Amwell, and a wave of AI-native startups are all vying to define the next generation of virtual care. Meanwhile, Big Tech continues to circle healthcare with AI-powered tools, raising the bar for differentiation.

In that context, visibility matters. Not just with patients or providers, but with capital markets increasingly selective about which AI narratives they believe.

The company’s recent disclosures suggest a strategy focused on incremental execution rather than headline-grabbing moves—tight marketing windows, measured equity incentives, and a steady emphasis on physician-led design.

The takeaway

Rocket Doctor AI’s engagement of Danayi Capital may not be transformative on its own, but it reflects a broader reality of the AI healthcare market in 2026: innovation alone isn’t enough. Companies must also prove credibility, transparency, and momentum.

As AI continues to reshape healthcare delivery, the winners are likely to be those that balance technical ambition with disciplined growth—and know how to tell that story clearly.

Get in touch with our MarTech Experts.

As AI-powered search engines increasingly reshape how information is discovered, marketers are running into a new problem: they can’t see what’s actually influencing AI-generated answers. Today, Stacker believes it has a fix.

The earned media distribution platform announced a strategic partnership with Scrunch that brings AI search visibility and citation reporting directly into the Stacker platform. The integration aims to help brands understand how third-party placements—news articles, syndicated content, and other earned mentions—affect their visibility and authority inside AI search tools.

That’s a growing concern as tools like ChatGPT, Google’s AI Overviews, and Perplexity pull from a mix of owned, earned, and third-party sources to generate responses. While marketers have plenty of data on how their own websites perform, what happens off-site has largely remained a black box.

Why earned media matters more in AI search

Traditional SEO rewards well-structured owned content. AI search, however, plays by different rules. Large language models tend to favor signals of credibility—citations, brand mentions, and authoritative third-party sources—often outside a brand’s direct control.

Most AI visibility platforms today focus on owned content discovery: whether a brand’s site is cited, summarized, or referenced in AI-generated responses. That approach misses a key piece of the puzzle: earned media.

Stacker’s core value proposition has always been earned reach—distributing brand stories across trusted publishers to generate third-party credibility. Scrunch, meanwhile, tracks how brands appear in AI search prompts, responses, and citations. Together, the companies are attempting to connect those dots.

The result is a unified view of how distributed stories and earned placements influence AI-driven discovery.

What the Stacker–Scrunch integration actually does

Once integrated, Scrunch’s AI search analytics will be embedded inside the Stacker platform. Customers will be able to track:

• AI prompt responses where their brand appears

• Brand mentions and citations in AI-generated answers

• The role third-party URLs play in AI visibility

• How earned placements compare to owned channels in shaping AI authority

Unlike standalone AI search tools, the Stacker integration adds context—linking AI visibility data directly to specific earned media placements and distribution campaigns.

For marketers trying to justify earned media spend, that connection is critical.

“AI search rewards credibility, and credibility is increasingly built outside your owned channels,” said Noah Greenberg, CEO of Stacker. “We’ve seen anecdotally how distributing owned content across third-party publications can directly impact AI search visibility, but nothing provided a comprehensive reporting solution for isolating the impact of earned media.”

In other words, this turns gut instinct into measurable insight.

Closing a major measurement gap

The partnership highlights a broader industry shift. As AI-generated answers replace traditional blue-link search results, marketers are being forced to rethink what “visibility” even means.

Clicks are declining. Attribution is fuzzier. And influence increasingly comes from being cited, summarized, or referenced—sometimes without a user ever visiting a brand’s site.

Scrunch CEO Chris Andrew framed the problem bluntly.

“If you are not showing up in AI search, it’s because there’s a gap between knowing which sources impact visibility and the ability to grow your brand presence in said sources at scale,” he said.

By pairing Scrunch’s AI monitoring with Stacker’s earned distribution engine, the companies aim to close that loop—showing not just where brands appear, but why they appear there.

How this compares to other AI visibility tools

The AI search analytics space is crowded but fragmented. Tools like Profound, BrandRank, and other emerging platforms track brand mentions in AI outputs, but most stop short of tying that visibility back to specific marketing activities.

Stacker’s approach stands out because it starts with distribution. Rather than treating AI visibility as an abstract metric, it ties performance to concrete earned placements—news articles, data-driven stories, and publisher syndication.

That could give communications and content teams a clearer path from action to outcome, especially as budgets tighten and leadership demands proof of impact.

Implications for PR, content, and SEO teams

This integration also blurs the traditional lines between PR, content marketing, and SEO.

AI search doesn’t care which team produced a piece of content—it cares about authority, context, and credibility. Earned media, long treated as a brand awareness play, is becoming a direct input into search visibility.

For PR teams, that elevates the strategic value of third-party placements. For SEO teams, it signals that optimizing owned pages alone is no longer enough. And for content teams, it reinforces the importance of stories designed to travel beyond a brand’s website.

Rollout timeline and availability

The Scrunch-powered AI Search Insights integration is scheduled to begin rolling out to Stacker customers in March 2026. While pricing and packaging details haven’t been disclosed, the feature will be embedded within the existing Stacker platform rather than offered as a standalone add-on.

That positioning suggests Stacker sees AI visibility not as a bolt-on metric, but as a core part of earned media strategy going forward.

The bigger picture

As AI search becomes the default interface for information discovery, marketers face an uncomfortable truth: influence is increasingly earned elsewhere.

The Stacker–Scrunch partnership reflects a broader recalibration happening across MarTech. Measurement frameworks built for websites and clicks are giving way to systems that track authority, citations, and presence across the wider information ecosystem.

For brands navigating that transition, visibility into earned media’s role in AI search may soon be less of a nice-to-have—and more of a survival skill.

Get in touch with our MarTech Experts.

Campaign Monitor is making a clear bet on practical AI—not flashy automation for its own sake, but tools designed to help small and mid-sized businesses actually make better email marketing decisions.

The company has announced three new AI-powered features—Marketing Monitor, Segment Mapper, and AI Email Booster—aimed at giving marketers always-on guidance directly inside the platform. The goal: reduce guesswork, shorten optimization cycles, and help lean teams improve results without changing how they work.

Marketing Monitor is already live for customers, while Segment Mapper and AI Email Booster roll out on January 28.

AI as an In-App Marketing Partner, Not a Black Box

Campaign Monitor’s new features are positioned less as autonomous AI and more as a built-in marketing advisor—surfacing insights, recommendations, and next steps without forcing users to hand over control.

That distinction matters. Many SMB marketers are surrounded by dashboards and metrics but lack clarity on what to act on next. Campaign Monitor’s approach focuses on turning data into direction.

Together, the three features cover the core email marketing loop: performance analysis, audience targeting, and campaign optimization.

Marketing Monitor: Performance With Context

Marketing Monitor tackles one of email marketing’s biggest blind spots: knowing whether your results are actually good.

The feature benchmarks campaign performance against relevant industry standards, adding context to metrics like opens, clicks, and engagement. Instead of just showing numbers, it highlights where marketers should focus next—helping teams prioritize fixes that will have the most impact.

For SMBs without analysts or dedicated optimization teams, this kind of guidance can dramatically speed up decision-making.

Segment Mapper: Audience Building Without the Technical Headaches

Segment Mapper lowers the barrier to advanced targeting by letting marketers describe their audience goals in plain language. The AI then translates that intent into usable audience segments inside Campaign Monitor.

This removes a common friction point for non-technical users who know who they want to reach but struggle with filters, logic rules, and segmentation syntax. It’s a notable step toward making personalization more accessible—especially as inbox competition continues to intensify.

AI Email Booster: Optimization Inside the Builder

AI Email Booster works directly inside the email builder, analyzing content as it’s created and surfacing clear, actionable recommendations. Marketers can apply suggestions with a single click, rather than switching tools or interpreting abstract scores.

The focus here is speed and clarity. Instead of overwhelming users with AI-generated rewrites or opaque predictions, Campaign Monitor is aiming for small, confident improvements that compound over time.

Why This Matters for SMB Marketers

Email remains one of the highest-ROI channels, but expectations for personalization and relevance keep rising. At the same time, most SMBs don’t have the time, staff, or budget to experiment endlessly or interpret complex analytics.

Campaign Monitor’s AI strategy is designed to close that gap by:

• Reducing time-to-value for campaign improvements

• Making advanced tactics accessible to non-experts

• Preserving human control over creative and strategy

“AI should make email marketing easier, not more complicated,” said Elizabeth Smalley, Chief Product Officer at Campaign Monitor. “We built these new AI features to provide always-on guidance directly inside the platform, helping marketers better see what’s working to optimize faster, without losing control of their strategy.”

That emphasis on collaboration—AI assisting rather than replacing human judgment—sets Campaign Monitor apart from more aggressive automation-first approaches in the email marketing space.

Launch Events and Availability

To showcase the new capabilities, Campaign Monitor will host two live launch webinars:

• Tuesday, February 3 at 1:00 PM EST

• Wednesday, February 4 at 10:30 AM AEST

The sessions will walk marketers through real-world use cases and demonstrate how the tools can simplify decision-making and boost performance.

The Bigger Picture

Campaign Monitor’s update reflects a broader MarTech shift: AI is moving from experimental features to embedded decision support. Rather than asking marketers to trust AI blindly, platforms are increasingly focused on delivering contextual guidance that fits naturally into existing workflows.

For SMBs, that balance—between intelligence and control—may be exactly what’s needed to stay competitive as inboxes grow more crowded and customer expectations continue to rise.

Get in touch with our MarTech Experts.

MiningLamp Technology has added a major credential to its fast-growing reputation in enterprise AI. The Hong Kong–listed company (2718.HK) took home the Grand Prize at the national finals of the 3rd China’s Innovation Challenge on Artificial Intelligence Application Scene (CICAS)—one of the country’s most competitive and influential AI events—cementing its status as a serious force in Agentic AI and multimodal large models.

The winning project, developed in collaboration with Peking University, is called “Intelligent Platform for Brand Globalization Creative Generation and Emotional Connection Based on Multimodal Large Models.” Beyond the long name, the idea is straightforward and timely: help companies expand globally by using AI to localize creative content, predict emotional response, and generate marketing assets faster—without losing cultural nuance.

The project was also named a “2025 National Artificial Intelligence Application Scenario Exemplary Case,” a designation reserved for AI systems with strong real-world commercial and societal impact.

A Fierce Competition With National Significance

CICAS is not a typical startup pitch contest. Jointly organized by the Chinese Association for Artificial Intelligence, the Suzhou Municipal People’s Government, and Soochow University, the competition is designed to surface AI technologies that can scale across industries.

This year’s challenge drew more than 3,250 registered teams, with 113 elite teams advancing to the national finals. Over 350 participants—from China and abroad—competed in Suzhou, Jiangsu Province, placing MiningLamp’s win firmly in “best-of-the-best” territory.

For MiningLamp, this marks a symbolic moment. While the company has been active in enterprise AI since 2006, the CICAS Grand Prize represents its first major national AI competition win since its Hong Kong Stock Exchange listing in November 2025.

Why Brand Globalization Is an AI Problem Now

Global expansion has become harder, not easier, for brands. Cultural missteps go viral instantly, consumer sentiment shifts faster than traditional research can track, and content localization remains expensive and slow.

MiningLamp’s platform is built around a clear thesis: global brand marketing is no longer a creative-only problem—it’s a data, emotion, and automation problem.

According to Wu Minghui, Founder, CEO, and CTO of MiningLamp, brands going global face three persistent barriers:

• Cultural and emotional differences across markets

• High costs and long timelines for content localization

• Limited data-driven insight into how creative will actually land

The platform addresses these challenges by combining multimodal AI, Agentic workflows, and proprietary data intelligence into a single system designed for marketing teams—not just data scientists.

Four Technologies Powering the Platform

At the core of MiningLamp’s winning solution are four tightly integrated capabilities. Together, they form an end-to-end workflow that spans insight generation, emotional evaluation, and content creation.

1. A Global Multimodal Content Asset Library

The platform includes a multimodal content library covering major global markets, incorporating video, image, and text assets. Rather than starting from scratch for every campaign, brands can draw from culturally relevant creative materials aligned with regional norms and preferences.

The practical upside is speed and cost efficiency. Localization cycles that once took weeks can now be compressed into days—or even hours—while maintaining cultural relevance.

In a market where brands are expected to “think global but act local,” this library becomes a strategic advantage rather than a simple repository.

2. Trustworthy Data Collection via the “Dexterous Hand” Mano Model

MiningLamp’s Mano model—described internally as an AI “dexterous hand”—is one of the platform’s most distinctive features.

Mano can operate across browser environments, visually identifying interface elements and interacting with them much like a human would. Users simply provide a URL and a description of their data needs; Mano handles the rest, collecting multi-source web data with minimal manual intervention.

This capability matters because global market analysis often fails due to fragmented, unreliable data. Mano’s human-like perception allows it to gather cleaner, more contextual datasets—critical for downstream decision-making.

Technical benchmarks underscore Mano’s maturity:

• Ranked first in the specialized model category of the OSWorld benchmark

• Ranked second overall, just behind Anthropic’s Claude-Sonnet-4.5

• Achieved SOTA performance on the Mind2Web benchmark

• A 7B-parameter version supports private deployment for enterprise security needs

For enterprises wary of black-box AI, this emphasis on transparency and controllability is notable.

3. Subjective Emotional Perception Analysis With HMLLM

Perhaps the most ambitious element of the platform is its Hypergraph Multimodal Large Language Model (HMLLM), designed to simulate how different audiences feel about content—not just how they engage with it.

Unlike traditional sentiment analysis, HMLLM models subjective emotional response across dimensions like attention, emotion, and cognition. It can estimate how viewers from different cultures, age groups, and genders are likely to react to advertising content before it goes live.

The model is trained on uniquely rich datasets:

• Video-SME and SPA-ADV, built from EEG and eye-tracking data

• Data collected from over 10,000 real human subjects

• Emotional response modeling with R² consistency exceeding 89%

The research behind HMLLM earned a Best Paper Nomination at ACM MM 2024, lending academic credibility to what is often treated as a fuzzy marketing problem.

For global brands, the implication is clear: fewer cultural misfires, less guesswork, and more confidence in creative decisions.

4. AI-Driven Video Generation at Scale

Once insights and emotional assessments are complete, the platform can automatically generate and optimize video content. This closes the loop—from market understanding to creative output—inside a single AI-driven workflow.

MiningLamp claims the system can compress traditional video production timelines from weeks to hours, a meaningful advantage as short-form video and rapid campaign iteration become standard across platforms like TikTok, YouTube, and connected TV.

This positions the platform not just as an analytics tool, but as a full-stack AI marketing engine.

Enterprise-Grade AI, Not Just a Demo

MiningLamp’s broader technical credentials reinforce the seriousness of the platform. The company has published 20+ papers in top-tier international journals and conferences, including:

• ACM MM 2024 (CCF-A): Best Paper Nomination for HMLLM

• TPAMI (SCI Q1): Few-shot video instance segmentation

• IJCV (SCI Q1): Image generation methods

• AAAI 2026 (CCF-A): Mano model compression, accepted as an oral presentation

These aren’t marketing whitepapers—they’re peer-reviewed contributions that help explain why MiningLamp is increasingly described as China’s first “Agentic AI” public company.

Strategic Implications for MarTech and Global Brands

MiningLamp’s win highlights a broader industry trend: AI is moving from content optimization to content decision-making.

While many Western MarTech platforms focus on performance metrics after launch, MiningLamp is betting on AI that evaluates cultural fit and emotional resonance before content reaches consumers. That shift could reshape how global campaigns are planned, especially in regulated or reputation-sensitive industries.

The platform also aligns with the growing enterprise demand for trustworthy AI—systems that are explainable, auditable, and deployable in private environments.

What Comes Next

At the CICAS closing ceremony, MiningLamp signed a cooperation intent with Gusu District, signaling plans to expand AI R&D and real-world deployment scenarios locally.

The company says it will continue applying its “data-driven trustworthy productivity” philosophy beyond brand globalization, targeting additional vertical industries where Agentic AI can deliver measurable impact.

As global competition intensifies and AI-driven differentiation becomes table stakes, MiningLamp’s platform could emerge as a critical infrastructure layer for companies trying to scale internationally without losing cultural intelligence along the way.

Get in touch with our MarTech Experts.