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.

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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.

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.

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.

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.

DDoS attacks are no longer blunt-force disruptions. They’re adaptive, multi-vector, and increasingly designed to blend in with legitimate traffic—making them harder to detect and even harder to stop without collateral damage.

That’s the context behind NSFOCUS being named a “Stars Company” in the DDoS Protection and Mitigation Security market by MarketsandMarkets’ 360Quadrants platform, a recognition that places the company among vendors combining strong product maturity with growing market impact.

The designation reflects NSFOCUS’s focus on automated, AI-driven DDoS defense—a shift that’s becoming table stakes as attack volumes surge and manual mitigation simply can’t keep up.

Why DDoS protection is being re-architected

Distributed denial-of-service attacks have evolved beyond traffic floods. Modern campaigns increasingly mix volumetric, protocol, and application-layer attacks, often launched simultaneously and tuned in real time to evade static defenses.

For enterprises, telecom providers, and operators of critical infrastructure, downtime is no longer just an IT problem—it’s a business continuity and trust issue. That reality is driving demand for platforms that can detect and mitigate attacks automatically, at scale, without disrupting legitimate users.

NSFOCUS’s DDoS Protection portfolio is built around that premise.

What differentiates NSFOCUS’s DDoS approach

At the core of NSFOCUS’s offering is a combination of AI-driven traffic analytics, behavioral modeling, and global threat intelligence. Together, these capabilities enable real-time detection and mitigation across multiple attack types while minimizing false positives.

Key elements of the platform include:

• Automated detection and mitigation of volumetric, protocol, and application-layer DDoS attacks

• Hybrid deployment architecture, combining on-premises appliances with cloud-based scrubbing centers

• Adaptive rate limiting and protocol anomaly analysis to maintain service availability during high-traffic events

• Bot filtering designed to separate malicious automation from real users

This hybrid model is particularly relevant for organizations operating across multi-cloud and carrier-grade environments, where traffic patterns are complex and capacity requirements can spike without warning.

Centralized visibility in a fragmented environment

One of the consistent challenges in DDoS defense is visibility. Many organizations rely on a patchwork of tools that detect attacks in isolation, making coordinated response difficult.

NSFOCUS addresses this with centralized management and analytics, giving security teams a unified view of attack activity, mitigation actions, and policy effectiveness. Detailed reporting and granular controls allow teams to tune defenses without rewriting configurations every time traffic patterns change.

Backed by continuous research and threat intelligence updates, the platform is designed to adapt as attackers change tactics—an increasingly important capability as DDoS tools become commoditized and widely accessible.

Why the 360Quadrants recognition matters

MarketsandMarkets’ 360Quadrants platform evaluates vendors using a mix of technical capability, commercial performance, and market execution, drawing on input from industry experts, customers, vendors, and secondary research sources.

The methodology includes:

• Shortlisting more than 25 prominent vendors and startups

• Regional portfolio and revenue analysis

• Assessment of growth initiatives and strategic collaborations

• Evaluation of industry-specific and market-relevant parameters

Being recognized as a “Stars Company” signals that NSFOCUS is not only delivering mature technology but is also gaining momentum in a market where credibility and performance are critical.

The broader market implication

The DDoS protection market is shifting away from static, rule-based systems toward automation-first security architectures. As attack durations shrink and complexity increases, response time has become just as important as detection accuracy.

Vendors that can combine AI, real-time analytics, and flexible deployment models are increasingly favored—especially by telecom carriers and operators of critical infrastructure, where latency and uptime are non-negotiable.

NSFOCUS’s recognition underscores that trend and highlights growing demand for resilient, scalable DDoS defenses that operate as a core layer of modern cybersecurity strategies rather than a bolt-on solution.

The takeaway

DDoS attacks aren’t slowing down—and they aren’t getting simpler. As attackers adopt more sophisticated techniques, organizations need protection that’s fast, automated, and adaptable across environments.

NSFOCUS’s “Stars Company” recognition from 360Quadrants reflects its positioning in that new reality: delivering AI-driven, hybrid DDoS protection designed to keep services online even when traffic turns hostile.

For enterprises and infrastructure providers rethinking how they defend availability, that shift from reactive mitigation to intelligent automation may be the most important upgrade of all.

Get in touch with our MarTech Experts.

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

AI is now the starting point of the buyer journey

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

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

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

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

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

What is Answer Engine Optimization (AEO)?

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

These platforms go beyond traditional SEO by enabling organizations to:

• Optimize content for AI-generated answers and conversational interfaces

• Track brand mentions and citations within LLM responses

• Identify ranking and recommendation factors used by AI systems

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

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

First G2 Grid® Report for AEO released

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

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

• A total of 150+ reviews across the category

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

Leader

• Profound

High Performers

• Otterly.AI

• Scrunch AI

Contenders

• Semrush

• BrightEdge

• Conductor

Niche

• Quattr

• GetCito

• GenRank.io

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

Vendors race to control AI-driven brand perception

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

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

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

How G2 evaluates AEO software

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

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

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

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

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

The bigger picture

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

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

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

Vrtly, Inc. wants to end that era.

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

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

Fixing a 20-year blind spot in healthcare marketing

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

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

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

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

From waiting room to treatment room—and beyond

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

Key capabilities include:

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

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

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

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

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

Why this matters now

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

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

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

Early momentum and rapid deployment

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

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

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

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

A new channel for healthcare brands—and beyond

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

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

The bigger picture

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

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

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

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