ON24 just added another weapon to its engagement stack. The company secured U.S. Patent No. 12,445,698 B2 for its AI engine that finds and extracts “Key Moments” from long-form video content. The system works across webinars, virtual events, digital conferences, and recorded demos.

The feature solves a long-standing marketing headache. Teams often run events that generate hours of footage but struggle to reuse that content. ON24’s patented technology tackles that gap by automating the discovery of highlight moments. The platform then packages them into short clips that can slip into nurture streams, partner enablement programs, landing pages, and social feeds.

Turning Engagement Behavior Into Short-Form Assets

The AI engine does more than trim videos. It examines engagement signals from the event itself. That includes viewing behavior, interactions, and content consumption patterns. The model then ranks moments that matter and extracts them into short, high-value videos.

This helps B2B marketers extend the shelf life of every event. Instead of a single live session, teams can quickly launch follow-ups, repurposed assets, and targeted campaigns—without manual editing or guesswork.

According to ON24 CEO Sharat Sharan, the core of the company’s strategy is AI-driven engagement. He says the new patent strengthens ON24’s position as an AI-enabled platform that understands audience behavior in real time. It also helps the company scale personalized content across channels.

Built by Engineers and Data Scientists for Demand Teams

The “Key Moments” engine is the product of ON24 developers and machine learning experts. The goal was simple: pull out the most engaging sections from any event. That includes product walkthroughs, customer stories, expert analysis, or Q&A sessions.

ON24 says the tech benefits a wide range of teams—marketing, customer success, sales, partner management, and training groups. Each can reuse “Key Moments” to reinforce messaging or support education workflows.

CTO Jayesh Sahasi says the patent advances ON24’s vision of connected, AI-led engagement. The system ties together behavioral data, automation, and content analysis. It lets teams transform a single event into continuous, data-backed campaigns.

Why This Matters for the B2B Engagement Market

Short-form content is the new currency in B2B marketing. Marketers want rapid content delivery, personalized outreach, and scalable video workflows. ON24’s patent supports that shift by automating one of the slowest steps in the process—manual video review.

Competitors offer clip-generation tools, but ON24’s differentiator is its use of engagement data tied to event behavior. It’s not guessing what content works; it’s identifying what audiences respond to.

As AI becomes central to B2B engagement platforms, this patent strengthens ON24’s position in a crowded market. It also signals the company’s larger push toward AI-powered orchestration across the full customer journey.

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Alembic Technologies just pulled off one of the biggest jumps in enterprise AI valuation this year. The Causal AI startup secured $145 million in Series B and growth funding, marking a 15.7x valuation increase since its last round. The investment was led by Prysm Capital and Accenture, signaling strong institutional confidence in a company aiming to redefine how enterprises understand cause and effect.

The move comes at a moment when every B2B vendor claims AI leadership. Yet Alembic’s pitch is sharper: while competitors rely on generic models, it focuses on proprietary data, causal inference, and a compute layer powerful enough to keep up with Fortune 500 demand. That combination has drawn interest from companies such as Delta Air Lines, Mars, NVIDIA, and others seeking clarity in a messy measurement landscape.

A Data Flywheel With Real Competitive Weight

At the core of Alembic’s strategy is a simple idea with major implications: better insights create better decisions, which then generate better data. This closed loop forms what CEO Tomás Puig calls a compounding data flywheel, and it’s difficult for rivals to replicate.

Many enterprises are drowning in dashboards and correlation-based reporting. However, Alembic claims its Causal AI identifies what actually drives outcomes. That advantage positions the company as a key intelligence layer rather than another analytics feature.

Accenture’s involvement reinforces that view. The consulting firm will integrate Alembic’s Causal engine into its enterprise transformation work, giving clients tools that move beyond pattern recognition toward evidence-based decisions. As Accenture CEO Julie Sweet put it, large companies need “verifiable, cause-and-effect insights” to act quickly and safely.

Big Customers, Bigger Impact

Enterprise customers are already using the platform to quantify decisions that were previously guesswork. Delta Air Lines linked Olympic sponsorship activations to ticket sales within days. Mars measured the exact dollar impact of viral brand moments. North Sails optimized digital spend for record returns. One Fortune 500 company grew its sales pipeline by 37% with precise attribution.

These examples illustrate why Alembic is drawing attention. Most organizations are not short on data; they are short on certainty. Causal AI promises to close that gap.

A Supercomputer Strategy Built for the Long Game

Alembic is pairing its funding with serious infrastructure investment. The company will deploy a new NVIDIA NVL72 superPOD cluster at Equinix’s SV11 data center, running NVIDIA AI Enterprise across its stack. This system is engineered for spiking neural networks, high-speed graph processing, and continuous-learning workloads.

This will be the company’s second dedicated supercomputing cluster, creating bi-coastal redundancy and ensuring the compute headroom needed for real-time causal analysis. Alembic positions this as a strategic moat. Instead of relying on shared cloud resources, it will operate a private AI fleet optimized for its unique workloads.

The deeper tie to NVIDIA also reflects a broader trend: enterprise AI platforms increasingly require custom compute infrastructure to maintain speed, security, and differentiation.

Defining the Next Layer of Enterprise Intelligence

Investors see Alembic as more than a vertical analytics solution. Many describe it as an emerging foundational model for enterprise decision-making. Instead of generating text or images, it generates causal truth, and brands are paying attention.

Prysm Capital’s team sees the company as a “mission-critical intelligence layer,” while WndrCo partners highlight the platform’s ability to deliver what marketers have sought for decades: clear, quantifiable attribution that informs where every dollar should go.

The company is also building a reputation for marrying deep research with commercial relevance. Its approach combines spiking neural networks, advanced graph modeling, real-time simulation, and high-performance compute into a system that updates continuously as new data enters the ecosystem.

The Market Impact

With this round, Alembic is positioned to influence several fast-shifting categories:

• Marketing measurement, where correlation-based models are losing credibility

• AI-driven budgeting, as brands face pressure for provable ROI

• Enterprise intelligence platforms, which increasingly compete on proprietary data

• Custom AI infrastructure, especially among companies needing guaranteed compute

Causal AI sits at the intersection of all four. That gives Alembic a strategic lane with few direct rivals and strong tailwinds as enterprises rebuild their data strategies around reliability rather than volume.

The Bottom Line

Alembic now has the capital, compute, and customer base to accelerate its push into enterprise AI. The company’s focus on Causal intelligence sets it apart in a market dominated by generative hype. With a new superPOD, deeper ties to Accenture, and accelerating Fortune 500 adoption, it is shaping a category that could define the next era of decision intelligence.

If Alembic delivers on its promise, the industry may soon shift from asking what happened to understanding why—and acting with far more confidence.

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Marketing measurement has never been easy, but it’s about to get a serious upgrade. Accenture has invested in Alembic, an AI-powered causal intelligence platform built to show which marketing efforts actually generate revenue. The investment comes through Accenture Ventures and includes a strategic partnership designed to push Causal AI deeper into the enterprise stack.

The timing is ideal. According to recent Gartner research, two-thirds of marketing leaders struggle to prove campaign impact. Traditional attribution tools often rely on siloed datasets, lagging models, or incomplete signals. Alembic says it can fix that by grounding measurement in cause-and-effect logic instead of correlation.

A Causal Engine Built for Real-Time Answers

Alembic’s platform ingests data from broadcast channels, social media, site traffic, and direct-to-consumer communications. It then merges those signals with sales data and runs causal analysis to determine what actions drive outcomes. The system assigns an impact score to each channel or marketing event, giving executives a clear view of what moved revenue and why.

The appeal is clear. Marketers want real attribution. Finance teams want accountability. Executives want decisions backed by evidence rather than dashboards that contradict each other.

Accenture CEO Julie Sweet framed the partnership as essential for enterprise transformation. Companies are no longer deploying AI in isolation. They need trusted intelligence at the core of their operations, and Causal AI offers a more reliable foundation than traditional measurement.

Breaking the Limits of Traditional Attribution

Most measurement platforms struggle with data fragmentation. Many cannot handle channels like brand campaigns, event sponsorships, or quick-moving organic social content. Alembic claims its software can analyze those unstructured signals and map the downstream impact even as customer data expands rapidly.

The platform can also model external factors—such as policy changes or unexpected market events—to show how they influence performance. This helps brands adjust spend in real time and stay ahead of shifting conditions.

Alembic CEO Tomás Puig attributes this capability to the company’s NVIDIA SuperPOD compute backbone. The infrastructure gives the platform enough power to run continuous causal calculations and surface insights with minimal delay. “Most companies aren’t short on data,” Puig said. “They’re short on answers.”

A New Measurement Paradigm for the Enterprise

Accenture Song sees the partnership as a turning point for performance measurement. According to Arun Kumar, global customer AI and data lead, Alembic complements methods such as marketing mix modeling but adds the ability to analyze far more variables. Instead of viewing measurement as a post-campaign autopsy, Causal AI turns analytics into a live operational tool.

The partnership also joins a growing ecosystem of AI tools within Accenture Song. Aaru supports strategic planning; Writer enhances content creation; AI Refinery accelerates campaign execution. Alembic slots into the final stage—proving what worked, how it worked, and how to scale it.

Accenture is already piloting Alembic’s technology internally to assess its own marketing initiatives. This early integration signals confidence in the platform and sets the stage for wider client adoption.

Part of a Larger Funding Wave

This investment follows Alembic’s recent Series B round, which was led by Prysm Capital and Accenture. Other participants included Silver Lake Waterman, Liquid 2 Ventures, NextEquity, Friends & Family Capital, and WndrCo. The funding will help Alembic expand its Causal AI engine, enhance its infrastructure footprint, and support a growing roster of enterprise customers.

With demand rising for reliable, real-time attribution, the partnership positions Alembic as a key player in the next phase of AI-driven marketing intelligence. As enterprises look for clarity in a noisy market, Causal AI may prove to be the missing link between massive datasets and actionable decisions.

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Deepgram has added another milestone to its rapid rise in Voice AI. The company’s enterprise-grade text-to-speech model, Aura-2, has been named a 2025 Customer Experience Innovation Award winner by TMC’s CUSTOMER magazine.

The award highlights companies pushing customer experience forward across every touchpoint—including social channels, automated workflows, and AI-powered agents. And this year, Aura-2 stood out for one reason: it sounds great, but more importantly, it works great.

Built for Work, Not Entertainment

Most TTS models chase entertainment-quality voices. Aura-2 targets the enterprise instead. It is engineered to sound human in the places that matter most—contact centers, regulated workflows, and real-time digital agents.

It provides:

• Domain-specific pronunciation for complex vocabulary
  (drug names, legal terms, identifiers, structured data)

• Sub-200ms TTFB latency, crucial for live voice agents

• Human-like clarity and accuracy

• Pricing that scales for production workloads

The model is powered by Deepgram Enterprise Runtime (DER), which supports deployments across cloud, VPC, and on-prem environments. DER also enables model hot-swapping and real-time optimization, both rare capabilities in the TTS market.

Industry Recognition

TMC CEO Rich Tehrani praised Deepgram for raising the bar on customer experience technology. He highlighted Aura-2 as a model that delivers performance across all customer engagement channels, not just synthetic voice demos.

Deepgram CMO Praveen Rangnath framed Aura-2 as a turning point in enterprise TTS. According to him, the model redefines what production-ready voice AI must deliver—speed, accuracy, consistency, and reliability.

Why Aura-2 Matters

Enterprises are adopting real-time AI agents at unprecedented speed, but most TTS tools still struggle with latency, scaling, and proper pronunciation under load.

Aura-2 directly targets those gaps. Its performance profile makes it suitable for industries where every millisecond and every mispronounced value matters, from customer support to healthcare, fintech, and logistics.

Try Aura-2

Developers can test Aura-2 through a self-serve API, complete with documentation and a real-time playground.

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Box and Amazon Web Services have entered a new multi-year strategic collaboration agreement (SCA) aimed at reshaping how enterprises use AI to extract value from content. The deal expands the long-standing partnership between the companies and focuses on developing new Box AI agents powered by AWS infrastructure and foundation models.

The announcement underscores a broader shift: AI agents are moving from prototype to production, and enterprise content is becoming the engine behind them.

Unlocking AI Value from Enterprise Content

According to Box CEO Aaron Levie, the power of AI depends on the context it can access—and that context sits inside documents, contracts, plans, and workflows that drive business operations. Box aims to centralize that intelligence through its Intelligent Content Management (ICM) platform while using AWS as the backbone for scale, security, and compliance.

AWS VP of Agentic AI Swami Sivasubramanian noted that AI agents are redefining how industries operate. The Box collaboration will help organizations securely use their structured and unstructured content as the foundation for agentic workflows.

New AI Agent Integrations with AWS

The partnership introduces a series of deep integrations that expand Box’s AI capabilities and streamline automation across content-heavy workflows. Key additions include:

AI Agents Powered by Amazon Bedrock

New Box AI agents can summarize long documents, generate multi-document FAQs, extract metadata, and trigger automated workflows. Customers can customize these agents using Amazon Bedrock models to fit unique industry or departmental needs.

Multimodal Analysis with Amazon Nova

Using Amazon Nova Multimodal Embeddings, Box AI can analyze text, images, video, and audio together. This unified view improves search accuracy, content intelligence, and automated decision-making across large content repositories.

Amazon Quick Suite Integration

Available today, the new Quick Suite integration lets customers extract insights, generate new files, and act on Box content directly within Quick Suite—boosting productivity for teams handling operational or analytical tasks.

Amazon Q Developer Customization

Developers can use Amazon Q Developer with the Box SDK and self-hosted MCP server to build intelligent apps and automate content workflows faster.

Compatibility with Strands, Kiro, and Bedrock AgentCore

These integrations ensure seamless orchestration between AI agents, connectors, and Box’s ICM platform, enabling secure automation at enterprise scale.

Box to Join AWS Marketplace

A key milestone of the SCA is Box’s upcoming availability in AWS Marketplace in early 2026. This will streamline procurement and deployment for large organizations seeking to centralize spending and accelerate adoption of secure content management and AI-driven workflows.

AWS Marketplace access also strengthens Box’s distribution model, making it easier for regulated industries to buy and deploy Box inside existing cloud environments.

Why This Collaboration Matters

The Box–AWS partnership goes beyond adding AI features. It positions content as a strategic asset and gives enterprises a secure path to deploy agentic workflows without compromising governance.

The combined stack—Box’s ICM platform and AWS’s agentic AI ecosystem—offers scalability, deep compliance, and flexible deployments for industries that handle sensitive data.

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B2B buyers hate waiting for demos. They’d rather poke around the product themselves, form an opinion quickly, and then decide whether a sales call is worth their time. It’s a trend reshaping nearly every software category, from PLG startups to enterprise giants.

Consensus—best known for pioneering AI-powered demo automation for sales teams—is now taking that philosophy upstream. Today, the company unveiled Consensus for Marketing, a new layer in its platform designed to give marketers the same interactive, self-service product experiences that sales teams have been leveraging for years. The goal: turn B2B websites, campaigns, and events into always-on, personalized demo engines.

If that sounds like a page pulled from the playbook of product-led growth, you’re not wrong. The difference here is that Consensus is pitching it to traditional marketing teams that want the conversion power of PLG without overhauling their entire go-to-market motion.

And based on early data—6–8x higher conversion rates for these Product Qualified Leads (PQLs) compared to classic MQLs—it seems the pitch might land.

Why Marketing Is Suddenly Obsessed With “Self-Serve Everything”

The shift toward self-service product research isn’t new. But over the past few years, B2B buyer behavior has essentially completed its metamorphosis. Prospects now operate like well-informed consumers:

• They search independently.

• They compare vendors before speaking to anyone.

• And they expect the product to prove itself before a calendar invite ever hits their inbox.

Traditional marketing assets—PDFs, landing pages, gated guides—haven’t just aged poorly; they’ve become almost irrelevant to buyers who want to experience value, not just read about it.

This is the demand gap Consensus is aiming to fill. Marketers need more than static content; they need interactive product touch points that qualify leads in real time.

As Betty Mok, SVP of Marketing at Consensus, puts it: “Interactive product demos are the number one resource buyers want when they land on a website. This is now table stakes.”

So What Exactly Is Consensus for Marketing?

Think of it as a marketing-grade layer built on top of the company’s established Demo Automation Platform. Sales already uses Consensus to automate and personalize pre-sales demos at scale. Marketing now gets its own toolkit—designed specifically for demand generation, inbound funnels, website experience, and event engagement.

Here’s what’s new under the hood.

Mobile-First, Browser-Native Product Walkthroughs

Buyers no longer sit at a desk browsing product pages. They’re researching across devices, especially mobile, and they expect experiences—not static screenshots.

Consensus for Marketing delivers responsive demos and tours that adapt cleanly across screen sizes. This is particularly relevant in crowded SaaS markets where design polish and user-friendliness aren’t just aesthetic perks; they can influence purchase decisions and brand perception.

If your competitors offer a “book a demo” button and you offer a full click-through experience on any device, prospects feel the difference.

Always-On Product Experiences

Here’s where Consensus diverges from traditional PLG tools. Most SaaS companies gate access to product trials or tours behind sign-up walls. Consensus flips the model by treating the product tour as part of inbound engagement—something that can appear:

• directly on the website,

• inside outbound nurture flows,

• embedded in ads,

• or even at in-person events.

The company positions it as a 24/7 interactive product layer, allowing marketers to “capture demand the moment interest sparks,” rather than waiting for prospects to request a meeting.

In practical terms, it means that every buyer gets their own personalized test drive, on demand, without the delays of scheduling.

Automated Lead Capture and Buyer Intelligence

This is arguably the feature that transforms these demos from “cool interactive content” into a revenue engine.

Consensus integrates with major MAP and CRM platforms (think Marketo, HubSpot, Salesforce, etc.) to capture behavior signals as prospects explore demos. It identifies:

• high-intent actions,

• areas of interest,

• time spent,

• friction points,

• and readiness to engage sales.

Instead of handing sales teams generic MQLs, Consensus produces PQLs—prospects who have already interacted deeply with the product and shown pre-sales level intent.

This isn’t a new category (PLG companies have done this for years), but bringing PQL logic to traditional, marketing-led funnels is a noteworthy advancement—and one that could help hybrid GTM teams modernize without ripping and replacing their strategy.

AI Content Studio: A Marketer’s New Best Friend

One challenge with interactive demos is the labor required to update them. Product screens change. Messaging evolves. A new feature drops two days before a major launch. Consensus tackles this friction with the AI Content Studio, which lets teams:

• generate new demo flows,

• update videos or clickable tours,

• personalize versions for campaigns,

• keep content aligned with product updates,

• and drastically reduce production overhead.

For marketing teams tired of version-control chaos, this is a welcome change. And considering how fast SaaS products shift, the ability to update demo content in real time could be a deciding factor for adoption.

Why This Matters in the Bigger B2B Landscape

The launch of Consensus for Marketing lands at a pivotal moment. A few major industry shifts are colliding:

1. Buyers increasingly distrust vendor claims.

They want to see value firsthand—not via hyper-polished marketing promises.

2. PLG set the standard for self-service evaluation.

Even non-PLG companies are expected to match that bar.

3. AI is enabling scalable personalization.

What once required designers, video editors, and product specialists can now be delivered automatically and updated instantly.

4. Marketing is being measured further down the funnel.

Pipeline generation—not just lead volume—is becoming the core KPI.

Against this backdrop, Consensus's pitch is straightforward:
If your product is the primary selling point, give buyers the product upfront—naturally, automatically, and with context.

Competitive Landscape: Where Consensus Fits

The market for interactive product tours has exploded. Tools like Navattic, Reprise, Storylane, and Walnut all focus on demo creation, often targeting both GTM and product teams.

Where Consensus differentiates is its origin story. It didn’t start as a marketing-focused tour builder; it started as a sales performance platform designed to automate demos and empower pre-sales teams.

This gives it three edges:

1. Enterprise-grade personalization logic already proven in sales workflows.

2. Deeper analytics tied to buyer intent, not just tour completion.

3. Stronger integration with CRM and MAP systems, aligning marketing and sales around one demo engine.

If competitors are demo builders, Consensus is positioning itself as an end-to-end demo lifecycle system spanning marketing, pre-sales, and sales—with AI as the connective tissue.

From MQL to PQL: The Funnel Evolution

Marketing teams have long relied on MQLs—leads who fill out a form or download a white paper—as early buying signals. But those signals are increasingly weak indicators of true intent.

Consensus argues that the future is the Product Qualified Lead, a signal generated not from surface-level content engagement but from genuine product interaction.

And the numbers back it up: early adopters report PQLs convert at 6–8x the rates of traditional MQLs.

This isn’t simply better lead scoring. It’s a fundamental recalibration of how marketers measure demand, prioritize budgets, and evaluate campaign performance.

What Analysts Will Likely Watch Next

Although Consensus’s marketing expansion is impressive, success will hinge on three areas:

1. Adoption among non-PLG enterprises

Can Consensus convince traditional enterprise marketers—many still reliant on heavy sales cycles—to embrace interactive product experiences?

2. Integration depth

Consensus must continue to play well with Salesforce, HubSpot, Marketo, Eloqua, and a growing suite of analytics platforms to maintain its advantage.

3. Cross-functional buy-in

Because the platform touches both marketing and pre-sales, organizations will need alignment to deploy it effectively.

If Consensus can execute on those fronts, it could carve out a unique space that bridges marketing automation and product-led selling.

The Bottom Line

Consensus for Marketing is less an incremental product update and more a statement about where B2B buying is headed. Buyers want to explore products independently. Marketers want higher-intent leads. Sales wants prospects who already understand the product before the first conversation.

This new offering attempts to satisfy all three.

As Mok puts it, “Buyers want hands-on experiences, not just headlines.” And increasingly, they want those experiences on their own timeline.

If Consensus can help marketers deliver on that expectation—while feeding sales teams a more qualified pipeline—the company might just redefine what “top of funnel” means in the modern B2B stack.

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The rise of AI search has changed the rules of online visibility, and PR is suddenly back in the spotlight. Press Ranger—a platform known for using earned media to boost brand rankings inside AI models—has inked a new strategic partnership with OtterlyAI, one of the leading platforms for AI search monitoring and optimization. Together, the two companies want to make it easier for brands to understand, track, and dominate when AI engines decide which sources to cite.

This isn’t a feel-good handshake agreement. Press Ranger will become OtterlyAI’s preferred PR partner for boosting discoverability across AI-driven results. In return, OtterlyAI becomes the recommended AI search analytics tool for Press Ranger’s customers and community.

For brands competing in the new era of Generative Engine Optimization (GEO)—where AI recommendation engines increasingly shape visibility—the partnership signals a broader shift: SEO alone won’t cut it anymore, and PR is becoming a critical lever for influencing what AI systems choose to surface.

Why AI Search Is Breaking Traditional SEO

Search as we’ve known it—ten blue links, keyword competition, and endless on-page optimization—is fading. AI chatbots like ChatGPT, Google Gemini, and Perplexity are rewriting how people find answers. Instead of scanning search results, users now get consolidated, AI-synthesized responses sourced from material those engines deem credible.

The result?
Brands must ensure they appear in datasets and citations—not just search results.

That’s where GEO comes in. GEO shifts the focus from ranking on Google to ranking inside AI responses, which prioritize well-structured informational content, editorial coverage, and authority sources.

Press Ranger has positioned itself squarely in this transition, helping hundreds of brands expand their digital footprint via news articles and PR placements—content categories AI engines regularly draw from. Meanwhile, OtterlyAI monitors how these engines reference brands, flagging where visibility is rising or fading.

Their partnership essentially bridges PR creation and AI search analytics, giving marketers a closed-loop strategy: publish → track citations → optimize → republish.

A Webinar for the AI Search Era

To kick off the collaboration, the companies are hosting a joint webinar on November 18 titled:
“How to Rank on ChatGPT: The Overlooked Power of PR.”

The session promises a rare look under the hood, featuring insights from over 1 million AI citations tracked across leading generative engines. Key topics will include:

• Why AI platforms favor news, expert commentary, and third-party editorial content

• Which content categories AI systems cite most frequently—and why

• How strategically placed press coverage influences responses inside ChatGPT, Gemini, and Google AI Overviews

• Why PR—not keyword-stuffed SEO pages—is increasingly the deciding factor in how AI models choose sources

If the data bears out what early GEO experiments are showing, this may become the new norm: PR isn’t just for reputation; it’s for algorithmic visibility.

PR + AI Search Analytics: A Unified Stack

The real significance of the Press Ranger–OtterlyAI partnership lies in how the tools complement each other.

Press Ranger: Expanding the Content Footprint

Press Ranger helps brands secure placements in publications that AI engines already view as authoritative. Unlike social posts or brand blogs—sources generative engines tend to downrank—earned media carries weight.

As CEO Steve Beyatte puts it, “Press Ranger has a proven track record of securing placements in top publications that AI engines frequently cite.”

That track record is increasingly valuable as AI engines continue to prioritize trusted editorial sources.

OtterlyAI: Monitoring the AI Landscape in Real Time

OtterlyAI tracks how brands appear—or don’t—across a growing range of AI search platforms. For marketers struggling to understand how AI is summarizing their brand, where competitors are gaining ground, or how citations are changing as models update, OtterlyAI offers:

• automated AI search visibility tracking

• competitor benchmarking

• historical change detection

• platform-specific ranking insights

Thousands of marketers already rely on it, and its free “visibility audit” feature gives brands a low-friction way to see exactly where they stand inside major AI engines.

GEO Becomes Mainstream

A partnership like this would have sounded niche two years ago. Today, GEO is rapidly becoming part of the standard digital marketing mix.

Here’s why:

1. AI search is siphoning traffic from traditional search engines.

Generative engines reduce the need to click through to external sites, concentrating visibility among a small subset of highly cited sources.

2. PR is becoming algorithmic.

Brands can no longer rely on SEO pages alone; they need third-party validation to influence AI summaries.

3. Data-driven PR is now essential.

Marketers are demanding quantitative visibility tracking—not guesswork—when measuring the impact of earned media.

4. Authority is becoming the new currency.

AI responses heavily weight credible publications, making PR a differentiator for brands that want to break out of obscurity.

Press Ranger and OtterlyAI are clearly leaning into this trend, creating a stack that blends PR placement, authority building, and AI search analytics into one motion. It’s a sign that GEO strategy is evolving from experimental to operational.

The Industry Implications

This partnership hints at where PR and SEO are heading:

• PR teams will start caring about AI citations the same way they once obsessed over backlinks.

• Content strategies will favor formats that AI engines interpret as trustworthy, such as news articles, expert commentary, and authoritative guides.

• Monitoring tools for AI platforms will become as common as Google Analytics, especially as search behaviors shift away from traditional engines.

• GEO may rise as a standalone discipline, parallel to SEO and PR, requiring its own strategies, metrics, and tools.

For marketers struggling to understand why their brand isn’t appearing in AI-generated responses—or why competitors are—the Press Ranger–OtterlyAI combination provides both the why and the how to fix it.

What Comes Next

As AI engines continue to evolve, so will the tactics needed to influence them. Press Ranger’s distribution network and OtterlyAI’s search monitoring capabilities represent a logical pairing, but the real test will be adoption across marketing teams.

If brands begin treating AI citations as a core KPI, as they once did backlinks, GEO could quickly move from a niche discipline to a mainstream requirement. And partnerships like this could form the backbone of that shift.

For now, the message is clear: in the age of generative AI, visibility isn’t just about ranking—it’s about being referenced. And PR may be the most powerful lever marketers have to shape those references.

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UFC broadcasts are about to get a lot smarter—and a lot faster. IBM and UFC have unveiled In-Fight Insights, a new AI-driven real-time alert system designed to surface meaningful fight stats, streaks, and records the moment they happen. The feature debuts this Saturday at UFC 322: Della Maddalena vs. Makhachev inside Madison Square Garden.

It’s the latest evolution of the UFC Insights Engine, which is built on IBM’s enterprise AI platform, watsonx, and fueled by more than 13.2 million data points spanning two decades of UFC history. Until now, the system powered only pre-fight and post-fight analytics. This marks its first live in-octagon deployment.

Bringing AI Cage-Side

The challenge with combat sports is speed. A fighter can land 10 strikes before a commentator finishes a sentence. Doing real-time analysis at that pace—without sacrificing accuracy—has been a sticking point for most AI systems.

UFC says the new engine solves that.
“Anyone who uses AI tools knows they are normally able to go deep or fast, but not both,” said Alon Cohen, EVP of Innovation for TKO. “In collaborating with IBM… we have optimized Insights Engine to accomplish both.”

The system will trigger immediate notifications when major milestones occur—such as a fighter breaking a personal best, hitting a significant strike count, or entering record-setting territory. Commentators will get instant context, giving fans richer storytelling without the usual lag.

Why It Matters

AI in sports broadcasting has been booming—Formula 1, the NFL, and the NBA all use some form of automated analytics—but combat sports have lagged behind due to unpredictable pacing and unstructured movement.

With In-Fight Insights, UFC becomes one of the first major global sports organizations to embed live, context-rich, AI-powered storytelling directly into the broadcast. For fans, this means more transparency into what's actually unfolding mid-fight—not just what the eye can capture.

For IBM, it’s another example of watsonx edging deeper into sports tech.
“AI is really changing the game for the live sports viewing experience,” said Jonathan Adashek, IBM SVP of Marketing & Communications. “This is about unlocking the storytelling potential and human element inside the cage.”

Beyond the Broadcast

UFC and IBM plan to scale the system across:

• Live event broadcasts

• Pre-event programming

• Social media content

• In-venue displays

Everything captured in real time will be added to UFC’s growing analytics archive, strengthening machine learning capabilities for future events.

The Bigger Picture

As MMA continues its global expansion—and as fans demand deeper insight without slowing the action—UFC’s move mirrors a broader trend: sports bodies turning to AI not for gimmicks, but for storytelling.

With In-Fight Insights, UFC is betting that real-time intelligence will become as essential to modern broadcasts as slow-motion replays once were.

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