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.

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

AI is now the starting point of the buyer journey

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

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

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

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

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

What is Answer Engine Optimization (AEO)?

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

These platforms go beyond traditional SEO by enabling organizations to:

• Optimize content for AI-generated answers and conversational interfaces

• Track brand mentions and citations within LLM responses

• Identify ranking and recommendation factors used by AI systems

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

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

First G2 Grid® Report for AEO released

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

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

• A total of 150+ reviews across the category

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

Leader

• Profound

High Performers

• Otterly.AI

• Scrunch AI

Contenders

• Semrush

• BrightEdge

• Conductor

Niche

• Quattr

• GetCito

• GenRank.io

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

Vendors race to control AI-driven brand perception

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

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

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

How G2 evaluates AEO software

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

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

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

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

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

The bigger picture

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

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

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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.

Get in touch with our MarTech Experts.

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

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

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

From conversational AI to execution engines

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

According to FlashLabs, SuperAgent can:

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

• Execute browser-level actions across the web

• Identify and qualify customers by scanning multiple data signals

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

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

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

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

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

Messaging becomes the command line

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

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

• Telegram

• iMessage

• SMS

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

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

Built for enterprises, not experiments

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

SuperAgent is fully hosted and production-ready, requiring:

• No hardware deployment

• No infrastructure management

• No exposed credentials

• No complex authentication flows

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

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

AI as a revenue workforce

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

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

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

FlashLabs is attempting to push past that boundary.

Why this matters for MarTech and RevOps

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

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

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

The competitive landscape

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

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

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

The bigger picture

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

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

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

Get in touch with our MarTech Experts.

Hightouch’s elevation to Leader in the 2025 Gartner Magic Quadrant for Customer Data Platforms (CDPs) is more than a badge of honor for a fast-growing data startup. It’s a marker of where the CDP market is heading—and how quickly the old rules are being rewritten.

This is Hightouch’s first-ever appearance in the Magic Quadrant, and it didn’t just sneak in. Gartner positioned the company squarely among Leaders, citing its Completeness of Vision and Ability to Execute. For a category long dominated by monolithic, all-in-one platforms, the recognition validates a different approach: warehouse-native, composable customer data activation.

In plain terms, Gartner is signaling that CDPs no longer need to sit on top of a data stack, duplicating information and slowing teams down. Instead, they can live inside the modern data warehouse—and that architectural choice is becoming a competitive advantage.

Why This Matters: The CDP Market Is Being Rewritten

For most of the last decade, CDPs followed a familiar pattern. Vendors promised a single system of record for customer data, ingesting information from dozens of sources, transforming it internally, and then pushing it out to marketing and advertising tools. That model worked—until it didn’t.

As cloud data warehouses like Snowflake, Databricks, and BigQuery became the real source of truth for enterprises, cracks started to show:

• Data duplication drove up costs

• Sync delays made “real-time” personalization aspirational at best

• Engineering teams became bottlenecks for marketing and growth teams

• Governance and security became harder, not easier

Hightouch emerged with a contrarian idea: don’t move the data at all.

Instead of copying customer data into yet another platform, Hightouch activates it directly from the warehouse, using the same governed, analytics-ready data that already powers BI and machine learning. Marketing, sales, and customer success teams get fresh, reliable data—without asking engineering to rebuild pipelines or manage new silos.

Gartner’s recognition suggests that this model is no longer fringe. It’s becoming mainstream.

What Sets Hightouch Apart

Hightouch describes itself as a warehouse-native customer data platform, but the distinction goes beyond buzzwords.

At its core, the platform connects cloud data warehouses directly to downstream tools—think ad networks, marketing automation platforms, CRMs, customer support systems, and even connected TV and retail media networks.

Key differentiators include:

No Data Replication
Hightouch doesn’t require companies to copy customer data into a proprietary store. That reduces infrastructure costs and eliminates sync lag—two pain points that have plagued traditional CDPs.

Built for Modern Data Stacks
Rather than replacing tools like Snowflake or Databricks, Hightouch assumes they’re already central. It layers activation, orchestration, and governance on top of existing investments.

Cross-Team Usability
Marketing, growth, and lifecycle teams can build audiences and launch campaigns without SQL-heavy workflows, while data teams retain control over schemas, permissions, and data quality.

AI-Powered Activation
Hightouch is leaning into AI to help teams optimize performance across channels—automating decisions around targeting, timing, and personalization based on warehouse data.

This focus aligns neatly with what Gartner and other analysts have been tracking: a move away from monolithic CDPs toward composable architectures that integrate cleanly into enterprise data ecosystems.

A First-Time Leader—And That’s the Story

What makes this placement particularly notable is that 2025 marks Hightouch’s first inclusion in the Magic Quadrant. Vendors often spend years moving from Niche Player to Visionary before earning a Leader spot.

That jump reflects both execution speed and market timing.

According to Hightouch, Gartner evaluated vendors on their ability to deliver against current CDP needs while articulating a credible vision for where the market is going. In a category undergoing architectural change, vision matters as much as feature checklists.

Hightouch’s leadership team sees the recognition as confirmation that the CDP market is aligning with ideas the company has pushed since its early days.

“Organizations want to power personalized marketing with their complete data, move faster without data replication, and use AI to optimize performance across channels continuously,” said Tejas Manohar, co-founder and co-CEO of Hightouch. “That combination has been core to Hightouch from the beginning.”

Translation: the market finally caught up.

How Hightouch Compares to Traditional CDPs

To understand the impact of Gartner’s positioning, it helps to look at what Hightouch is not.

Traditional CDPs often bundle identity resolution, storage, analytics, and activation into a single system. While convenient on paper, this approach can clash with modern enterprise realities, where data teams already rely on best-of-breed tools.

Hightouch flips that model:

This difference is especially relevant as enterprises scale. When billions of rows of customer data are involved, duplication isn’t just inefficient—it’s expensive and risky.

Industry Implications: CDPs Meet the Composable Era

Hightouch’s Leader placement also reflects a broader trend across the martech landscape: composability.

Just as headless CMSs reshaped content management and modular data stacks redefined analytics, CDPs are being unbundled. Enterprises increasingly prefer tools that do one thing well and integrate cleanly, rather than platforms that try to do everything.

Gartner’s Magic Quadrant plays a powerful role here. For enterprise buyers, it’s often a filtering mechanism long before demos or RFPs begin. Seeing a warehouse-native vendor among Leaders sends a clear message: this architecture is no longer experimental.

Expect ripple effects:

• Increased scrutiny of data duplication practices

• More CDP vendors adopting warehouse-first roadmaps

• Greater alignment between marketing and data teams

• AI-driven activation becoming table stakes, not optional

The Growing Role of AI in Activation

One subtle but important aspect of Hightouch’s positioning is its emphasis on AI-powered activation.

While many CDPs talk about AI in abstract terms—predictions, scores, recommendations—Hightouch is focused on applying AI directly to campaign execution. That includes optimizing audience definitions, channel selection, and performance over time.

This matters because AI models are only as good as the data feeding them. By working directly on warehouse data, Hightouch reduces the risk of stale or incomplete inputs—a common issue when data is copied across systems.

As advertising, retail media, and connected TV ecosystems become more fragmented, AI-assisted orchestration is shifting from “nice to have” to essential.

What Gartner’s Magic Quadrant Signals to Buyers

For enterprise technology buyers, the Magic Quadrant remains a shorthand for market maturity and vendor credibility. Gartner combines analyst research with validated customer feedback, offering a view that goes beyond marketing claims.

Hightouch’s placement suggests that:

• Warehouse-native CDPs are viable for large enterprises

• The market rewards execution speed and architectural clarity

• Buyers should question whether they still need standalone CDP storage

It doesn’t mean traditional CDPs are obsolete overnight—but it does suggest their dominance is no longer guaranteed.

The Competitive Landscape Is Heating Up

Hightouch isn’t alone in pushing the warehouse-native narrative, but its Leader status gives it a visibility boost at a critical moment.

As legacy CDP vendors modernize their stacks and new entrants emerge with composable-first designs, differentiation will come down to usability, governance, and performance at scale. Gartner’s evaluation implies that Hightouch is executing well on all three—at least for now.

The real test will be how quickly competitors adapt, and whether enterprises are willing to rethink long-held assumptions about where customer data “should” live.

Bottom Line

Hightouch’s debut as a Leader in the 2025 Gartner Magic Quadrant for Customer Data Platforms is a milestone not just for the company, but for the CDP category itself.

It reinforces a growing consensus: the future of customer data activation lives in the warehouse, not in yet another silo. For marketing, growth, and data leaders navigating increasingly complex stacks, that shift could simplify operations—and unlock faster, more reliable personalization at scale.

Whether this marks the beginning of the end for traditional CDPs or simply a new phase of competition, one thing is clear: the center of gravity in customer data is moving, and Gartner just confirmed it.

Get in touch with our MarTech Experts.

Artificial intelligence may be transforming customer service dashboards and call routing systems, but when things go wrong in the real world—no heat, a flooded basement, a medical concern—most consumers still want to talk to a person. And they want that option immediately.

That’s the central takeaway from ServiceForge’s newly released research report, “Keep Service Human,” which digs into how consumers actually feel about AI-driven customer service. The answer, based on original survey data, is blunt: speed alone doesn’t equal satisfaction, and automation can cost businesses real revenue when it replaces human interaction too aggressively.

For home services, skilled trades, and other high-stakes service industries, the findings land as both a warning and a strategic opportunity.

Consumers Are Clear: Don’t Replace Humans When It Matters

According to the report, 85% of consumers prefer speaking with a real human when contacting a local service business. Even more striking, one in three respondents said they would hang up immediately if they reached an AI bot.

That’s not a mild preference—it’s active resistance.

For service-driven businesses that rely on inbound calls to book jobs, that behavior translates directly into missed appointments, lost revenue, and damaged brand perception. An unanswered call is one thing; a call that ends in frustration is worse.

ServiceForge frames this as a growing disconnect between how companies deploy AI and how customers experience it.

Speed Isn’t the Priority—Resolution Is

Much of the hype around AI in customer service focuses on faster response times, lower costs, and always-on availability. But the data suggests consumers are optimizing for something else entirely: getting the problem solved.

Key findings from the Keep Service Human report include:

• 73% say resolution matters more than how fast the call is answered

• 54% describe AI-powered customer service as frustrating

• 83% have actively requested to speak with a human instead of AI

These numbers challenge a common assumption in CX strategy—that faster equals better. For essential services, customers appear willing to wait a bit longer if it means empathy, clarity, and confidence that someone understands the situation.

Why This Hits Harder for Local and Essential Services

ServiceForge focuses on software for skilled trades and home service businesses, and that context is crucial. When a customer calls a plumber, HVAC technician, or electrician, the situation is often urgent, emotional, or disruptive to daily life.

In those moments, AI’s strengths—efficiency, consistency, scale—don’t fully match the customer’s needs.

“When a customer is calling because their heat is out or their basement is flooding, they want things AI can’t deliver: empathy, understanding and reassurance,” said Jane Blanchard, head of brand and marketing at ServiceForge.

That insight aligns with broader CX research showing that emotional intelligence and trust play an outsized role in service satisfaction, particularly in crisis or high-cost scenarios.

This Isn’t Just a Home Services Problem

While the report centers on skilled trades, the implications stretch much further.

Respondents expressed similar discomfort with AI-led customer service in healthcare, real estate, legal services, and other relationship-driven industries. In other words, this isn’t about pipes and furnaces—it’s about contexts where decisions feel personal, urgent, or risky.

That distinction matters as AI adoption accelerates across industries. The data suggests that blanket automation strategies may work for transactional interactions, but fall apart when customers need guidance, reassurance, or nuanced explanations.

The AI Paradox: Efficiency vs. Experience

None of this means AI has no place in customer service. ServiceForge is careful to draw that line clearly.

The report acknowledges that AI can be highly effective in back-office automation, scheduling, data entry, and internal efficiency. Used correctly, it can free up human agents to focus on the conversations that actually require judgment and empathy.

The problem arises when AI becomes the front door instead of the support system.

For businesses chasing cost savings, it’s tempting to push customers toward bots by default. But the data suggests that approach may erode trust—and ultimately revenue—especially in competitive local markets where reputation and reviews carry outsized weight.

Human Support as a Competitive Advantage

One of the more strategic insights from the report is how human-led service correlates with brand outcomes.

ServiceForge found that customers are significantly more likely to:

• Leave positive online reviews

• Express trust in the company

• Recommend the business to others

In crowded local markets, those signals can be decisive. Star ratings, word-of-mouth, and perceived responsiveness often matter more than price alone.

“For home service businesses, the human touch isn’t just nice to have; it can be a major competitive advantage,” Blanchard noted.

That framing positions human customer service not as a cost center, but as a differentiator—something AI-first strategies risk undervaluing.

What This Means for CX and MarTech Leaders

The Keep Service Human report lands at a moment when AI is being rapidly deployed across customer engagement stacks, often under pressure to “do more with less.” For MarTech and CX leaders, the findings suggest a need for more nuanced design choices.

Key implications include:

• Hybrid models outperform AI-only approaches in high-stakes interactions

• Customer intent should dictate automation levels, not cost targets alone

• Human availability needs to be visible and accessible, not hidden behind bots

• Trust and empathy remain core CX metrics, even in AI-powered environments

In other words, AI works best when it amplifies humans—not when it replaces them outright.

A Reality Check for AI-First Customer Service

As generative AI and conversational bots continue to improve, the temptation will be to assume customer resistance is temporary. ServiceForge’s data suggests otherwise.

Consumers aren’t rejecting AI because it’s new—they’re rejecting it because, in certain moments, it feels insufficient. And no amount of speed can compensate for the absence of understanding when something important is on the line.

The message from the research is straightforward: keep service human, especially when it counts.

Get in touch with our MarTech Experts.

Enterprises are pouring money into AI, but many are still building on shaky foundations. New research cited by Datalinx AI suggests 63% of enterprises admit they lack the data management practices required to support AI at scale. The result is a familiar pattern: ambitious AI roadmaps, expensive consulting engagements, and fragile data pipelines that break just when they’re needed most.

Datalinx AI believes that’s the real bottleneck—and investors are buying in.

The company, which positions itself as an AI data refinery, has raised $4.2 million in oversubscribed Seed funding to help enterprise marketing and data teams transform raw, fragmented data into AI- and application-ready assets. The round was led by High Alpha, with participation from Databricks Ventures and Aperiam, alongside a notable group of strategic angels with deep roots in enterprise software, advertising, and data infrastructure.

For a market obsessed with models, copilots, and generative interfaces, Datalinx is betting that data readiness—not model sophistication—is the real differentiator.

The Real Cost of “Broken” Enterprise Data

Most large organizations already run on modern cloud warehouses and analytics stacks. Yet AI initiatives still stall. According to Datalinx, the problem isn’t access to tools—it’s the complexity and brittleness of the data pipelines feeding them.

Enterprises often spend millions on systems integrators or divert highly paid engineers into what Datalinx bluntly describes as janitorial work: discovering datasets, cleaning them, validating schemas, resolving inconsistencies, and rebuilding pipelines when they inevitably fail.

Even then, the output is often opaque, hard to trust, and poorly documented. That fragility makes it nearly impossible to build predictive, production-grade AI systems—especially in marketing, advertising, and commercial analytics, where data is messy, fast-moving, and deeply contextual.

Datalinx is targeting that pain point head-on.

What Datalinx Actually Does

At its core, Datalinx aims to automate the most failure-prone parts of enterprise data work—from discovery to activation—using a combination of AI agents, domain-specific knowledge, and modular architecture.

The company describes its platform as the first “agentic data utility”, designed to:

• Discover relevant datasets across complex enterprise environments

• Clean and validate data automatically

• Apply commercial and marketing-specific ontologies

• Produce high-fidelity, outcome-ready data products

• Maintain transparency and predictability throughout the process

Rather than focusing on dashboards or surface-level analytics, Datalinx concentrates on data products—assets designed explicitly to drive downstream outcomes in AI models, marketing activation, and data science workflows.

The pitch is simple but ambitious: 10x faster time-to-value using a fraction of the resources typically required.

Built for AI, Not Just Analytics

One of the more subtle distinctions in Datalinx’s positioning is its emphasis on AI readiness, not just data cleanliness.

Traditional data engineering workflows often stop at “good enough” for reporting. AI systems, especially those driving personalization, prediction, or automated decision-making, demand far more consistency, context, and semantic clarity.

Enterprise teams frequently struggle with questions like:

• Which version of this data should the model use?

• How should fields be structured for predictive performance?

• What hidden assumptions exist in the data?

• How do we ensure changes don’t silently break downstream systems?

Datalinx addresses these challenges by embedding domain expertise and context graphing directly into the data refinement process. Instead of treating all data as interchangeable, it applies specialized knowledge—particularly around commercial, marketing, and advertising data—to guide how assets are shaped and activated.

This focus aligns with a growing realization in the market: AI systems fail less often because of bad models than because of misunderstood data.

A Team with Enterprise Scars

Datalinx is led by Joe Luchs, CEO and co-founder, a multi-time founder and former executive at Amazon and Oracle. That background shows in the company’s framing of the problem.

Rather than pitching AI as a silver bullet, Luchs emphasizes the operational realities enterprises face.

“You can’t reap the benefits of AI innovation on a foundation of broken data,” Luchs said. “We’re providing the first agentic data utility, designed to bring enterprises clean, actionable, and performant data products with minimal work and full transparency.”

The emphasis on transparency is notable. One of the persistent complaints about automated data tooling is that it replaces manual work with black boxes. Datalinx argues that enterprises need automation and visibility—especially when data underpins revenue-generating systems.

Early Traction and Enterprise Validation

While Datalinx is still early, it’s already working with large organizations and platform partners.

The company was one of just five startups selected for the inaugural Databricks AI Accelerator Cohort in 2025, a signal that its approach resonates with major data infrastructure players.

That partnership extends beyond branding. Datalinx integrates deeply with Databricks, aligning its data refinement capabilities with modern lakehouse architectures and AI workflows.

Andrew Ferguson, VP at Databricks Ventures, framed the value proposition clearly:

“The most successful AI strategies are built on a foundation of clean, high-quality data. By combining our infrastructure and AI tools with marketing and advertising data models, Datalinx creates seamless connections between CMOs and their data teams.”

That last point—bridging CMOs and data teams—is strategically important. Many AI initiatives stall not because of technology gaps, but because business and technical stakeholders lack a shared data language.

A Real-World Use Case: Sallie Mae

Datalinx has also landed early enterprise collaborators. Sallie Mae, for example, selected Datalinx as a co-development partner to accelerate data product development across its data and media initiatives.

According to Li Lin, VP of Engineering at Sallie Mae, the appeal was automation combined with accessibility.

By automating time-consuming pipeline work, enabling natural-language data exploration, and embedding domain expertise into data product design, Datalinx is already showing early promise in speeding up go-to-market execution.

That blend—technical depth paired with usability—is increasingly critical as enterprises try to scale AI beyond experimental teams.

Why Investors Are Paying Attention

The investor list behind Datalinx reads like a who’s who of enterprise software and ad tech experience.

Alongside High Alpha, Databricks Ventures, and Aperiam, the round includes:

• Frederic Kerrest, co-founder of Okta and 515 Ventures

• Ari Paparo, founder and CEO of Beeswax and Marketecture

• Arup Banerjee, founder and CEO of Windfall Data

These aren’t passive investors chasing AI hype cycles. Many have lived through multiple infrastructure shifts and understand how long-standing data problems resurface with each new wave of technology.

High Alpha partner Mike Langellier summed up the opportunity succinctly: Datalinx could become the essential utility layer for enterprises using data in AI, advertising, and marketing.

That framing positions Datalinx less as a point solution and more as foundational infrastructure—an ambitious but potentially defensible role if the company executes well.

The Bigger Trend: Data Readiness as the New Bottleneck

Datalinx’s timing is hard to ignore. As generative AI moves from experimentation to production, enterprises are discovering that data readiness is now the rate-limiting step.

Models can be swapped. APIs can be integrated. But messy, undocumented, fragmented data slows everything.

This has created a new category of tooling focused on:

• Semantic layers and ontologies

• Data observability and trust

• Automated data product generation

• Agentic workflows that reduce manual engineering

Datalinx sits squarely in that emerging space, with a specific focus on commercial and marketing data—areas where AI-driven personalization and automation promise outsized returns, but only if the data holds up.

What Comes Next

With $4.2 million in fresh capital, Datalinx plans to scale operations and meet growing demand from enterprise teams under pressure to deliver AI results faster.

The challenge ahead will be execution: proving that agentic automation can handle the nuance and edge cases that have historically required human judgment. If Datalinx can maintain trust while reducing effort, it could carve out a durable position in the enterprise AI stack.

For now, the message is clear: AI innovation doesn’t fail because of a lack of ambition—it fails because the data isn’t ready. Datalinx is betting that fixing that problem is one of the biggest opportunities of the AI era.

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With new vehicle sales growth expected to remain constrained in 2026, automotive dealers are being forced to rethink where growth really comes from. The answer, increasingly, lies not just on the lot—but in the conversations happening every day across sales, service, and BDC operations.

That’s the context behind Marchex’s appearance at the 2026 National Automobile Dealers Association (NADA) Show, where the company will exhibit at booth #7337N. Marchex plans to spotlight how its AI-powered conversation intelligence platform turns customer calls and interactions into measurable business outcomes at a time when every missed opportunity carries more weight.

As consumers hold onto vehicles longer and lean more heavily on service departments, dealers face rising call volumes, higher operational complexity, and increased pressure to convert conversations into revenue. Marchex is positioning its platform as a way to bring clarity—and accountability—to those interactions.

Why Conversations Matter More in 2026

The automotive market has shifted dramatically over the past few years. Inventory volatility, margin pressure, and changing consumer behavior have pushed dealers to rely less on pure vehicle sales volume and more on service, retention, and experience-driven differentiation.

Service departments, in particular, are emerging as growth engines. Longer vehicle ownership cycles mean more maintenance, more repairs, and more high-value RO opportunities—but only if dealerships can capture intent, respond effectively, and avoid breakdowns in communication.

That’s where Marchex believes conversation intelligence can make the biggest impact.

“By analyzing customer conversations, Marchex equips automotive retailers to maximize vehicle sales, generate higher-value repair orders, increase scheduled appointments, improve RO close rates, and improve agent performance across the customer journey,” said Troy Hartless, President and CRO of Marchex.

The emphasis isn’t just on listening—it’s on prescriptive insights that tell dealers exactly what to do next.

From Call Tracking to Operational Intelligence

Marchex has been embedded in the automotive industry for nearly two decades, supporting more than 5,000 U.S. dealerships and maintaining deep relationships with OEMs. Over that time, the role of call analytics has evolved significantly.

Basic call tracking answered one question: Did the call happen?

Conversation intelligence answers much harder ones:

• Was the customer intent identified correctly?

• Did the agent ask the right questions?

• Were service or sales opportunities missed?

• Did the conversation lead to a booked appointment or RO?

• Where did the process break down?

Marchex’s AI platform analyzes unstructured conversation data—calls, transcripts, and outcomes—and transforms it into actionable operational insights across sales, service, and marketing.

For dealerships, that means visibility into the moments that directly affect revenue, customer satisfaction, and long-term loyalty.

Engage for Sales: Finding and Acting on Buyer Intent

At NADA 2026, Marchex will highlight its Engage for Sales solution, designed to help dealerships capture and convert high-intent buyers more consistently.

Rather than treating every inbound lead equally, Engage for Sales uses AI-driven analysis to:

• Identify conversations that signal strong purchase intent

• Flag missed opportunities in real time

• Prioritize follow-up based on likelihood to convert

• Alert teams when leads require immediate attention

In an environment where lead volumes may soften but competition remains fierce, this kind of prioritization becomes critical. Dealers can focus resources where they matter most, rather than relying on gut instinct or incomplete CRM data.

The result is fewer dropped leads, faster response times, and better close rates—without adding headcount.

Engage for Service: Unlocking RO Growth Hiding in Plain Sight

If sales conversations are about intent, service conversations are about need—and those needs often go unmet.

Marchex’s Engage for Service solution is built to surface what customers are actually asking for, even when they don’t use precise terminology. By analyzing calls, the platform can detect:

• Unmet service needs

• Indicators of major repair opportunities

• Gaps between customer requests and agent responses

• Missed chances to upsell or schedule additional work

For service departments under pressure to increase RO values and throughput, these insights provide a roadmap for action. Managers can identify which calls deserve follow-up, which agents need coaching, and where processes are failing customers.

In a service-driven growth model, that intelligence can make the difference between flat performance and sustained profitability.

Previewing the Agent Performance Suite

One of the most forward-looking elements of Marchex’s NADA presence will be a preview of its upcoming Agent Performance Suite, designed to address a growing challenge in dealership operations: how humans and AI work together on the front lines.

As dealerships experiment with AI-powered agents, chatbots, and automated workflows, performance gaps can emerge. Some interactions improve. Others quietly degrade the customer experience.

Marchex’s new suite aims to make those dynamics visible.

The platform provides insights into:

• Where agents succeed or struggle in conversations

• How handoffs between AI and human agents perform

• Which behaviors correlate with bookings, ROs, and sales

• Where automation helps—or hurts—customer engagement

What sets the suite apart is its focus on coaching and improvement, not just reporting. Conversations are translated into personalized, skill-specific action plans, giving each agent clear guidance on how to perform better.

As labor challenges persist and agent effectiveness becomes a defining growth lever, this kind of performance intelligence is likely to gain traction.

Enterprise Visibility for Dealer Groups

For large dealer groups operating across multiple locations and brands, consistency is often as challenging as growth.

Marchex addresses that need with a unified enterprise view of marketing, sales, and service performance. Leadership teams can:

• Identify trends across rooftops

• Attribute campaigns to real outcomes

• Scale best practices across locations

• Maintain consistent standards and customer experience

That enterprise-wide perspective becomes increasingly valuable as groups look to optimize operations without sacrificing local nuance.

A Timely Bet on Conversation Intelligence

Marchex’s message at NADA 2026 aligns with broader trends in automotive retail and martech alike. As margins tighten and acquisition costs rise, maximizing existing customer interactions becomes one of the most efficient paths to growth.

Conversation intelligence sits at the intersection of CX, AI, and revenue operations—turning what was once dark, unstructured data into a strategic asset.

For dealers facing a year of modest sales growth but rising service demand, the implications are clear: the conversations you already have may be your biggest untapped opportunity.

What Dealers Can Expect at NADA 2026

Dealers attending NADA 2026 are invited to visit Marchex at booth #7337N, where they can:

• See live product demonstrations

• Receive real-time account reviews

• Explore how AI-driven insights apply to their operations

• Learn how conversation intelligence can drive growth in 2026 and beyond

As the automotive industry adapts to new realities, Marchex is betting that better conversations—and better insight into them—will separate high-performing dealerships from the rest.