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.

Get in touch with our MarTech Experts.

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.

Get in touch with our MarTech Experts.

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.

Get in touch with our MarTech Experts.

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.

Get in touch with our MarTech Experts.

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.

Get in touch with our MarTech Experts.

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.

Get in touch with our MarTech Experts.

The legal world isn’t known for moving fast. But in the Asia-Pacific region, one technology shift is accelerating faster than precedent-heavy industries typically allow: AI-driven document management.

NetDocuments, widely regarded as one of the most secure and trusted intelligent DMS platforms in the legal field, has announced 72% year-over-year growth in APAC. That’s a staggering number for a sector where digital transformation often resembles cautious tiptoeing more than rapid adoption.

More than 20,000 legal professionals across Australia, New Zealand, Singapore, and Japan now rely on NetDocuments—not just for storing documents, but for using generative AI, workflow automation, and embedded legal intelligence to get work done faster and more securely.

NetDocuments’ rapid expansion points to a broader trend: law firms in APAC aren’t just warming up to AI—they’re actively operationalizing it.

Why APAC Law Firms Are Choosing NetDocuments Over “Upgrade-Only” DMS Projects

For many firms, the original plan was modest: update the document system, modernize storage, check a box, move on.

But once firms start evaluating NetDocuments, the scope typically expands.

That’s what happened at Norman Waterhouse. IT Manager Frederik Schwim explains it plainly:

“We were planning a DMS upgrade as an isolated project—then we realized NetDocuments came with far more functionality. We achieved several tech goals with a single implementation.”

Instead of separate projects for document automation, AI insights, contract review tools, or judicial trend analysis, NetDocuments bundles these into an integrated platform.

This isn’t just a DMS anymore—it’s an intelligent legal operations layer.

AI Where Lawyers Actually Work: Inside ndMAX

The star of NetDocuments’ APAC growth story is ndMAX, the company’s AI and workflow automation suite. Where many legal AI tools sit outside standard workflows—requiring uploads to third-party systems or new interfaces—NetDocuments brings AI directly to the heart of everyday legal work.

ndMAX Legal AI Assistant

Lawyers can ask questions across the entire document corpus and get instant, context-aware answers. Unlike general-purpose AI tools, responses stay fully contained within the firm’s DMS—critical for maintaining privilege and confidentiality.

Agentic Editing in Microsoft Word

This is where things get interesting. Instead of switching apps or relying on an assistant tool, lawyers can request edits from inside Word:

• Ask for restructuring

• Generate suggested revisions

• Apply edits automatically

• Turn insights into actionable changes instantly

For legal professionals drowning in version control and document markups, this is a productivity lifeline.

ndMAX Studio

A collection of pre-built AI apps for common legal workflows, including:

• Document classification and profiling

• Contract review against playbooks

• Judicial decision trend analysis

• Automated document assessments

These aren’t “experimental features”—firms are already using them to build consistent, repeatable workflows across practice groups.

AI App Builder

NetDocuments gives firms the flexibility to customize the out-of-the-box apps or build their own. For APAC firms seeking tailored automation—rather than one-size-fits-all AI—this is a key differentiator.

Secure AI: The New Battleground for Legal Tech

While AI enthusiasm is high, legal skepticism is higher—especially around confidentiality, data exposure, and compliance.

APAC firms routinely cite security and control as the biggest hurdles to adopting generative AI. That’s where NetDocuments stakes its strongest claim: AI is embedded in the platform itself, never requiring lawyers to copy, paste, or upload sensitive content into external systems.

Ron Dutta, Director of IT at McCullough Robertson, summed it up:

“For all the excitement around AI, it brings concerns around integration and confidentiality. NetDocuments’ integrated approach eliminates the risks of external AI tools.”

In other words: AI that stays inside the firm’s security perimeter is AI lawyers feel comfortable using daily.

A Region Ready for AI—But Cautious About How It’s Delivered

APAC law firms aren’t simply adopting NetDocuments because it’s an AI-enabled DMS. They’re choosing it because it aligns with their practical, long-term technology strategies.

Take McInnes Wilson, whose CIO Robyna May emphasizes the importance of both capability and responsibility:

“Our focus is making AI a natural, accessible part of everyday legal work. NetDocuments’ strategy aligns with ours—and lets us deploy AI safely and responsibly.”

This theme repeats across the region:

• Legal teams want more automation.

• They need AI that’s embedded, not bolted on.

• They prioritize secure, compliant systems that maintain client trust.

• They want tools that reduce friction, not add steps.

NetDocuments checks all these boxes—explaining its accelerating adoption curve.

Why APAC Is Becoming a Hotbed for Legal Tech Innovation

APAC firms are moving faster than many expect. While U.S. and U.K. firms often dominate legal tech headlines, APAC’s transformation is uniquely compelling.

Three forces are driving it:

1. Rapid modernization cycles

Many firms are shifting from legacy, on-prem systems straight to cloud-native, AI-ready platforms—skipping the half-measures common in older markets.

2. A strong focus on operational efficiency

APAC firms face competitive pressure to deliver high-quality work at scale, especially in fast-growing markets like Singapore and Australia.

3. A cultural emphasis on secure innovation

APAC firms want cutting-edge tools—but not at the expense of confidentiality or client obligations. NetDocuments’ embedded-AI approach fits that mindset.

The Bigger Picture: The Legal DMS Is Becoming the AI Hub

Historically, a DMS was just… a repository. A necessary but unexciting piece of infrastructure.

But with generative AI’s rise, the DMS becomes something entirely different:

• a training ground for firm-specific insights

• an engine for workflow automation

• a secure environment for AI content generation

• a central hub for drafting, reviewing, analyzing, and updating legal documents

NetDocuments’ APAC growth reflects a broader shift: law firms no longer see the DMS as storage. They see it as the core of their AI strategy.

And in a profession built on written work, that shift is monumental.

NetDocuments’ Path Forward in APAC

For Head of APAC Jennifer Cathcart, the momentum is clear:

“Legal technology innovation in APAC is thriving as firms embrace AI. Our Intelligent DMS vision is helping legal teams focus on serving clients—not wrestling with workflows.”

With demand rising for integrated AI systems—and concerns growing about fragmented third-party tools—NetDocuments is well positioned to continue its APAC expansion.

If the current trajectory holds, 72% YoY growth may soon look conservative.

Get in touch with our MarTech Experts.

Elastic—best known as The Search AI Company—has landed in the Leader category of the IDC MarketScape: Worldwide Observability Platforms 2025 Vendor Assessment, a signal that the company’s OpenTelemetry-first, AI-powered approach is resonating with enterprises wrestling with swelling data volumes, hybrid architectures, and accelerating performance demands.

IDC’s endorsement highlights what observability buyers are increasingly prioritizing: open standards, correlation across signals, intelligent automation, and cost-governance levers built directly into the pipeline.

Elastic checks all those boxes—and then some.

Why IDC Says Elastic Stands Out

The MarketScape report points to Elastic’s “open standards–first architecture”, which reduces tooling fragmentation by natively ingesting OpenTelemetry data and tracking signals across logs, metrics, traces, and real user monitoring (RUM). That means teams can move from detection to decision without ripping out instrumentation or duplicating data pipelines across complex, hybrid, and multicloud estates.

In plain English: Elastic simplifies observability without forcing teams to rewire everything.

IDC adds that Elastic is a fit “when an open standards observability platform with Prometheus and OpenTelemetry alignment, RUM/APM correlation, and petabyte-scale retention controls is needed.” Not many vendors can comfortably support observability at petabyte-level scale while keeping ingestion pathways flexible and manageable.

Elastic’s Observability Pitch: AI-Native, OTel-Native, Operationally Pragmatic

Elastic Observability positions itself as the platform that unifies operational and business data, enabling SRE teams to detect and resolve issues faster by connecting performance signals to actual customer experience.

That connection—tying telemetry to business outcomes—is increasingly where enterprises want to go. If the homepage latency spike correlates with a revenue drop or a checkout failure, teams need that insight in real time, and ideally without stitching together multiple tools.

Elastic’s approach includes:

• Full OpenTelemetry-native ingestion (no adapters or conversions required)

• Zero-code auto-instrumentation across major languages

• Correlated logs, metrics, traces, and RUM/APM views

• Broad connector coverage for hybrid and multi-cloud sources

• Enterprise-grade support and governance controls

Shannon Kalvar, research director at IDC, sums it up:

“Elastic links technical performance to customer experience and business context out of the box… The platform’s extensibility and role-appropriate views support shared context across DevOps while maintaining cost governance levers at scale.”

That last point—cost governance—is becoming a major battleground across observability vendors. Elastic’s retention controls and scalable ingestion pipeline appear to have stood out to IDC evaluators.

Streams and EDOT: Elastic’s Latest Innovations

Elastic isn't just leaning on past credibility—it’s pushing new capabilities at a steady clip.

EDOT for OpenTelemetry Support

Elastic recently expanded its enterprise support through its EDOT (Elastic Distribution of OpenTelemetry) initiative, providing deeper coverage for organizations adopting OTel at scale.

Streams: AI-Powered Log Intelligence

Perhaps the most forward-looking addition is Streams, an agentic, AI-driven experience that reinvents how teams work with logs. Instead of manually filtering, wrangling, and enriching log data, Streams helps SREs jump straight into the “investigator” role.

Traditional log search patterns are often slow and mentally taxing. Streams is Elastic’s attempt to move log analysis closer to natural-language investigations, abstracting away the noise and complexity that bog down incident response.

As Elastic’s senior vice president of Software Engineering Santosh Krishnan puts it:

“Our mission is to help teams move from reactive troubleshooting to proactive, intelligent operations that make digital experiences fast, reliable and resilient… Streams and Agent Builder accelerate how teams derive value from signals and build AI agentic workflows.”

This framing aligns with a broader industry trend: observability is transitioning from a detection toolset into an intelligent automation and decisioning layer.

The Bigger Market Context: Observability Is Evolving—Fast

Elastic’s Leader position also reflects a shifting competitive landscape. The observability market once revolved around dashboards and alerting. Now, enterprises want:

• OpenTelemetry-native ingestion (without proprietary lock-in)

• AI-assisted correlation and summarization

• Cost controls baked into the pipeline

• Unified data storage instead of tool sprawl

• Support for hybrid, legacy, and cloud-native workloads

• Faster path from signal to root cause

Elastic’s pitch—an open, scalable, search-driven approach—resonates strongly as companies rethink their observability architectures to manage soaring data volumes and escalating cloud costs.

Being named a Leader in the IDC MarketScape validates Elastic’s position among vendors racing to deliver AI-powered, OTel-aligned observability platforms at enterprise scale.

Is Elastic’s Open Standards Strategy Paying Off?

IDC’s language suggests a clear “yes.” OpenTelemetry is quickly becoming the backbone of modern observability stacks, and Elastic’s full embrace of OTel—not as a bolt-on but as a core ingestion path—gives it strategic advantage.

For organizations struggling to unify distributed telemetry from microservices, mobile sessions, edge workloads, and legacy systems, Elastic offers:

• a single ingestion strategy

• a unified data model

• AI-powered correlation

• and petabyte-scale retention without ballooning costs

It’s a compelling formula at a time when platform consolidation is accelerating.

The Bottom Line

Elastic’s recognition as a Leader in the IDC MarketScape for Observability Platforms underscores the company’s momentum as enterprises shift future observability investments toward AI-driven, OTel-native architectures.

With innovations like EDOT support and Streams, combined with scalable ingestion pathways and strong cost governance, Elastic is positioning itself as one of the few platforms capable of handling modern observability complexity at truly massive scale.

Whether SREs are looking to accelerate triage, unify telemetry, adopt OpenTelemetry more cleanly, or reduce tool fragmentation, Elastic’s offering is increasingly viewed as a safe—and strategic—bet.

Get in touch with our MarTech Experts.

">