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Microsoft Purview eDiscovery Problems Are Structural—and Growing

By John Patzakis

For organizations that rely on Microsoft Purview for eDiscovery, compliance and investigations, the gap between what the tool delivers and what the job demands is widening. Stalled searches, timed-out exports and a 2 GB per hour indexing cap slow investigations, drive up costs and leave legal teams with results they can’t fully defend.

Over the past several months, we have been speaking with a number of eDiscovery service providers, channel partners, and enterprise customers about their experiences with Microsoft Purview eDiscovery. The feedback has been remarkably consistent. The same challenges we began writing about several years ago are still there, and by most accounts they have gotten worse. One service provider that handles a high volume of Microsoft 365 matters told us that Purview performance has deteriorated noticeably in recent months, describing searches and exports that stall or time out and processing delays that have put counsel in a difficult position with opposing parties and the court.

When X1 first raised these issues, the common reaction in the market was that Purview was a maturing product that Microsoft would eventually fix. With Microsoft’s engineering resources, many assumed that eDiscovery inside Microsoft 365 would steadily improve. That was a reasonable expectation at the time. It has not happened. In our view, the reason is that Purview’s limitations stem from the architecture of Microsoft 365 itself, which makes it very difficult to engineer away. And there is now an additional factor worth considering: the intense competition for computing resources driven by Microsoft’s AI strategy.

The Architecture Has Not Changed
As we explained in our post on Microsoft 365 eDiscovery throttling, Microsoft 365 is a massive multi-tenant SaaS environment. Every customer shares pooled infrastructure, and eDiscovery is exactly the kind of heavy, bursty workload that a multi-tenant system must restrain to protect everyone else on the platform. Throttling, timeouts, and hard caps are how Microsoft manages that “noisy neighbor” problem, and they are built into the design of the system. We made the same point more broadly last December in our analysis of why most SaaS architectures fall short for enterprise-grade AI: shared capacity introduces contention and performance variability that no configuration setting can resolve.

Microsoft’s own documentation bears this out. The current Microsoft Learn page on eDiscovery limits, last updated in June 2026, shows:

  • 2 GB per hour for advanced indexing in Purview eDiscovery Premium. That is the same cap we cited in 2024. At that rate, indexing a single terabyte requires roughly three weeks of continuous processing.
  • 2 GB per hour per mailbox limit for exports, with daily exports capped at the tenant level.
  • Automatic cancellation of any process or export still running after seven days.
  • No service level agreement for search terms. Microsoft also states plainly that it “doesn’t provide a service level agreement for search times.”

Microsoft even maintains a service advisory to notify administrators when their tenant has been “throttled due to number of Search and Export jobs exceeding the limit set by Microsoft.”

For organizations with significant information governance requirements, these constraints are even more limiting. As we discussed in our post on information governance challenges in Microsoft 365, a defensible governance program depends on automated enforcement across the full data estate, and a 2 GB per hour ceiling makes that impractical at enterprise scale.

Special Master Favro’s Findings Still Hold
In July 2023, Special Master Philip Favro issued a report in Deal Genius, LLC v. O2COOL, LLC, No. 21 C 2046 (N.D. Ill.), raising serious concerns about whether reliance on Microsoft 365’s native search tools satisfies the reasonable inquiry requirement of Federal Rule of Civil Procedure 26(g). As we reported at the time, the Special Master found that M365 has “technological limitations that may not allow a responding party to generate reliable search results,” that it “generally does not fully index the data” in an organization’s environment, and that it does not allow users to validate their search and production results.

More than three years later, nothing in Microsoft’s documentation indicates those problems have been resolved. The indexing ceiling is unchanged, items that exceed parsing limits are still marked as partially indexed, and there is still no service level commitment. Meanwhile, the courts have raised the bar. In Kim v. Cushman & Wakefield U.S., Inc., 2026 WL 1353455 (C.D. Cal. Apr. 24, 2026), the court held that “[k]eyword searches alone, without more advanced and thoughtful search techniques, will be inadequate for Teams data.” That is a difficult standard to meet with a native toolset built around basic keyword and condition searching.

Recent Developments Reinforce the Concerns
Several developments over the past year add to the picture.

A systemic Purview hold failure. In Oakley v. MSG Networks, Inc., 792 F. Supp. 3d 394 (S.D.N.Y. 2025), Madison Square Garden placed a legal hold through Purview that was intended to ensure emails were “retained no matter what the user does.” Emails from eleven custodians were lost anyway. MSG’s forensic expert concluded that a technical glitch in its Microsoft environment had occurred “beyond a shadow of a doubt,” and the court noted that “the software malfunction affected seventy-nine matters other than the Oakley case.” MSG avoided sanctions because the court found its use of Purview to be a reasonable preservation step. But the emails were gone, and MSG had to fund a forensic investigation and litigate the issue to establish it was not at fault. Reasonableness under Rule 37(e) turns on what a party knew or should have known, and as the record of Purview failures grows, I would not count on that defense being available indefinitely.

Sanctions for delay even when reliance was reasonable. In FTC v. Match Group, Inc., 2025 WL 46024 (N.D. Tex. Jan. 7, 2025), an archiving system used with Office 365 failed to capture certain custodians and time periods. Match produced nearly 500 additional documents after the discovery deadline, most of them after the FTC had moved for summary judgment. The court found Match’s reliance on the archive reasonable, yet still imposed sanctions under Rule 16(f) for the late production, allowing the FTC additional depositions at Match’s expense. The lesson for Purview users is clear: courts can and will sanction delays caused by collection tools, regardless of how reasonable the choice of tool may have been.

Reduced functionality. In 2025 Microsoft moved all customers to its new Purview eDiscovery experience, retiring classic Content Search and eDiscovery (Standard) in May and classic eDiscovery (Premium) in August. In the process, Microsoft retired its legacy legal hold communications tools for custodian notices and acknowledgments. And as Casepoint has noted, the new interface no longer shows that a custodian was ever on hold once that custodian is released, making it harder to document a preservation timeline. The visibility problem is significant enough that a new third-party reporting product launched this spring because, as LawNext reported, legal teams have “no direct visibility into what is happening inside Microsoft Purview eDiscovery.”

More data, more complexity, new fees. Copilot is generating entirely new categories of data that Purview must preserve, search, and produce. Microsoft recently had to add an option to convert Loop and Copilot Pages files to HTML because those formats cannot be opened by standard review tools. Microsoft now also bills pay-as-you-go storage for certain AI interaction data in eDiscovery cases and requires E3 customers to activate pay-as-you-go billing to use its eDiscovery export APIs.

Service disruptions. On January 22, 2026, an infrastructure failure in North America blocked administrator access to the Purview portal, along with Exchange, Teams, and SharePoint search, for several hours.

The AI Factor
This brings us to a question several of our partners have raised with us. Microsoft has been unusually candid with investors that it does not have enough computing capacity to meet demand, and that it is making deliberate choices about where that capacity goes.

On Microsoft’s January 28, 2026 earnings call, CFO Amy Hood described the order of priority directly: “the first thing we’re doing is solving for the increased usage in sales and the accelerating pace of M365 Copilot. Then we make sure we’re investing in the long-term nature of R&D and product innovation,” with “the remainder going towards serving the Azure capacity that continues to grow in terms of demand.” She added that if the GPUs that came online in the prior two quarters had been allocated entirely to Azure, Azure’s growth metric “would have been over 40” percent.

On the October 28, 2026 call, Ms. Hood reported that “[c]ustomer demand continues to exceed available supply.” CEO Satya Nadella said Microsoft will “dynamically allocate capacity to the highest-value opportunities across compute, data, Foundry, and Copilot,” explaining that “we have one pool, and we want to get the pool to be as efficient and fungible” as possible. Microsoft is now projecting capital spending on the order of $170 billion to try to catch up.

Microsoft has not said where Purview eDiscovery falls in that order of priority, and we have no inside view into how Microsoft allocates resources within its data centers. But consider what Microsoft has told us. It operates “one pool” of capacity. That pool is constrained. Capacity is allocated to the “highest-value opportunities,” and Copilot is first in line. eDiscovery is a compliance function that generates little incremental revenue, carries no service level commitment for search times, and has seen its documented throughput limits stay frozen while its feature set has been trimmed. At the same time, Copilot is adding new volumes and types of data that Purview is expected to handle.

When capacity is scarce and allocated by value, it is fair to ask how high a priority eDiscovery search and export jobs receive. The service providers and customers telling us that Purview has gotten worse may be seeing the practical result of that ordering. Organizations planning their discovery and governance obligations around Purview should at least account for that possibility.

A Different Architecture
X1 takes a fundamentally different approach. The X1 Enterprise Platform uses our patented distributed micro-indexing technology to index Microsoft 365 data in place, across Exchange, OneDrive, SharePoint, and Teams, along with endpoints, file shares, and other cloud sources.

For legal and compliance teams, that means:

  • Predictable timelines. X1 does not depend on Purview’s search index or Microsoft’s eDiscovery search APIs, so it is not subject to Purview’s indexing ceiling, export caps or the throttling that comes with sharing those resources with every other Microsoft tenant.
  • No competing with Copilot for capacity. The compute runs on infrastructure our customers control, which means eDiscovery and governance jobs are not competing with Copilot for priority.
  • The search methodology courts now expect. Full Boolean, proximity, wildcard, and stemming queries that execute consistently across email and Teams chat, with the ability to iteratively test, validate and document search methodology before anything is collected.
  • Lower cost and less risk. Only potentially responsive data is collected, and it can be uploaded directly into Relativity for review.
  • AI where your data lives. The same in-place architecture powers X1 AI In-Place, allowing organizations to apply AI classification directly where their data lives without the cost, delay, and exposure of moving it.

X1 works alongside your Microsoft 365 investment. Your data stays in M365, and X1 gives you the defensible, scalable discovery layer Purview wasn’t built to provide.

If your organization or your clients are running into these issues with Purview, we would like to hear from you. Contact X1 to schedule a live demonstration of X1 Enterprise in a Microsoft 365 environment, or to discuss a proof of concept on your own data.

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Enterprise AI Has a Token Cost Problem — But It’s Very Fixable. What most AI vendors aren’t telling you.

By Larry Gill

The promise of AI in the enterprise is everywhere right now. Every eDiscovery vendor, legal tech platform, and cloud provider is claiming to have AI capabilities. But there’s a fundamental architectural flaw in how virtually every one of them applies AI — and it’s a problem that has significant consequences for your costs, your security, and your risk posture.

With our new release of X1 Enterprise v6, we’ve built a genuinely different approach. Last week, our team hosted a live product tour to walk through what that looks like in practice. Here’s a summary of what we covered — and why I believe it changes everything.

The Problem: AI Is Being Applied Too Late
The eDiscovery and data governance workflow has been largely the same for over 20 years: Identify → Collect → Process → Host → Review. Every major vendor with AI capabilities today is applying AI at the very end of that process — at the Review stage — after data has already been moved or copied into their platform.

That’s too late. And it’s not just where they’re applying AI in the workflow — it’s how they’re applying it that’s the real problem.

Before AI ever touches your data in these platforms, you’ve already:
• Copied and transferred sensitive enterprise information to a vendor-controlled environment
• Paid for processing and hosting on the full data volume — including everything that turns out to be irrelevant
• Created security and compliance exposure from that mass data transfer to a third party
• Waited through long, throttled ingestion cycles before any analysis can begin

And now you’re being up-charged for ‘new’ AI capabilities on top of already expensive collection, hosting, and review fees. And the reason why you are being charged so much is that many of these vendors are merely brokering usage (and being charged for it) through large, centralized AI platforms.

If you’re considering pointing a cloud LLM — Claude, Copilot, ChatGPT, or even legal-focused platforms like Harvey — directly at your enterprise data to solve this problem, I want to be direct: they’re the wrong tool for the job. Cloud AI platforms cannot search data in-place. If you try to use them across your full enterprise data estate, you’ll be exfiltrating enormous volumes of data to their AI engines and consuming a massive number of tokens — exploding your costs in the process.

Infographic illustrating X1's approach to applying AI at the source before data moves, featuring steps: Identify, Collect, Process, Host, and Review.

X1’s Answer: AI In-Place, Before Anything Moves
X1 Enterprise v6 takes a fundamentally different architectural approach. We call it AI In-Place.

Rather than copying data into a centralized platform and then applying AI, X1 deploys distributed micro-indexes directly across your enterprise data sources — your M365 environment, endpoints, cloud repositories, and more. Your data stays exactly where it lives. We bring the AI to the data. Not the other way around.

That means AI decisioning happens before collection, before review-set creation, before any exporting, and before anything moves. We apply AI at the very beginning of the eDiscovery and data governance workflow — not at the end.

X1’s AI capabilities are about upstream AI enablement, not (yet another) prompt-wrapper that brokers expensive queries to Anthropic or OpenAI like too many other eDiscovery and Compliance Platforms. X1’s fundamental architectural shift means X1 neither charges nor incurs OEM AI costs, as the models are frozen and deployed in-place. This factor alone results in massive cost savings and efficiencies.

Infographic comparing two data architectures: 'Collect-First' process showing bulk copy and transfer methods, and 'Analyze-In-Place' by X1 featuring AI capabilities for data analysis in real-time.

One Platform, Across Every Critical Use Case
The AI In-Place architecture isn’t a point solution. It’s an enterprise platform that spans your most critical data workflows:

eDiscovery — X1 enables index-in-place early case assessment, data identification, and highly targeted collection. You get full data visibility and AI-powered responsiveness scoring before a single document is exported, resulting in dramatically smaller review volumes and lower costs — beginning before collection even starts.

Risk and Compliance — X1 identifies and remediates PCI, PII, and privacy-regulated data across your enterprise, continuously and without moving it into a compliance platform. It supports departed employee workflows, GDPR, FOIA, HIPAA compliance, and more — all analyzed and remediated in-place.

InfoSec and Investigations — When a breach occurs or an insider threat is suspected, time is critical. X1 gives investigation teams real-time capability at petabyte scale, across endpoint and cloud environments simultaneously — something no centralized architecture can match.

Information Governance — X1 handles large-scale data separation for M&A due diligence and divestitures, ROT analysis, records management policy enforcement, data mapping, and more — all in-place without migration or centralized data processing.

A Hidden Cost Nobody Is Talking About: Enterprise-Wide Token Explosion
There’s another dimension to this problem that rarely gets discussed openly, and it has major financial implications for any organization deploying AI at scale.

AI productivity tools like Claude or Copilot are genuinely valuable for administrative and day-to-day workflows — drafting emails, summarizing meetings, and generating content. But they are fundamentally the wrong tool for enterprise-wide data discovery.

Here’s why:

When you ask a cloud AI platform to find information across your enterprise data, it has no index to work from. It must retrieve and read the actual documents — potentially thousands or millions of them — just to locate what you’re looking for. Every document pulled into context consumes tokens. Every search, every query, every time someone asks a question about your data, the AI is ingesting enormous volumes of content to produce an answer. At enterprise scale, this doesn’t just add up — it explodes.

The costs compound quickly. Token pricing is consumption-based, and when your AI tool is reading entire document sets on every query rather than looking up a precise answer, you are essentially paying to re-read your entire data estate over and over again. For large organizations, this can translate into AI infrastructure costs that are orders of magnitude higher than they need to be.

X1’s local index-in-place technology solves this directly. Because X1 has already built a persistent, AI-enriched index across all your enterprise data sources — right where the data lives — your AI tools don’t need to go find and read the documents. Instead, the AI asks the question, X1 uses its index to identify the precise answer, and then delivers only the targeted files, documents, or data points the AI or end user actually needs. The documents themselves never have to be ingested into the AI platform at all.

The result is dramatically lower token consumption across your organization — because you’re sending the AI targeted answers, not raw document libraries. X1 becomes the intelligent retrieval layer that makes your existing AI investments far more efficient and far less expensive to operate at scale.

Where We’re Headed: X1 as the Governed Retrieval Layer for Enterprise AI
As your organization deploys more AI assistants and agents — through Copilot, Claude, or internal AI tools — they will all need a secure, governed way to retrieve knowledge from your distributed data. X1 is being built to serve as that infrastructure layer that connects your AI tools to your data.

Our vision is for X1 to become the MCP Server for your LLMs — the governed retrieval layer that sits between your centralized AI systems and your enterprise data. Your AI tools will ask the questions. X1 will find and provide the answers — safely, compliantly, at scale, with minimal cost, and without data ever leaving its source.

Three Things I Want You to Take Away

  1. AI In-Place gives you a real strategic advantage. Security, speed, and scalability — at a fraction of the cost — with your data never leaving your environment. There’s no need to collect, move, copy, re-index, or centralize before analysis can begin. The shortest path to insight is leaving the data where it already is.
  2. We will never monetize your data. Full stop. You can analyze your data in place and pay nothing extra for the AI capabilities we’ve built into v6. No data charges. No add-on fees. Ever. Your data is an asset — it shouldn’t be a revenue stream for your software vendor.
  3. Control belongs with you. This industry has been charging customers a premium for over-collection, over-processing, bloated hosting, inefficient review, and now AI add-on fees on top of it all. That model ends here. X1’s AI-native approach cuts through it entirely — dramatically lower costs, no unnecessary data sprawl, and control back where it belongs.

If you missed the webinar, you can watch it now here. And if you’d like to see what AI In-Place looks like in your specific environment — your M365 footprint, your eDiscovery program, your compliance posture — reach out to us at info@x1.com or visit x1.com to schedule a private demo.

“The right architecture for AI isn’t about moving your data to the AI. It’s about bringing the AI to your data.”
— Larry Gill, CEO, X1 Discovery

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Why X1’s AI In-Place Architecture Is a Genuine Departure from Legal AI’s Status Quo

By John Patzakis

X1 AI In-Place Architecture — AI hub connecting to distributed enterprise data sources including Microsoft 365, email, cloud, and endpoints

The legal technology market has a buzzword problem. Terms like “AI-powered,” “intelligent review,” and “automated analysis” have been applied so broadly—and so inconsistently—that they have largely lost their ability to signal anything meaningful about how a product actually works. Against that backdrop, X1’s announcement last week of AI In-Place for X1 Enterprise represents a genuinely different approach to applying AI within enterprise legal and compliance workflows. The reason for this basis is X1’s unique architecture.

To understand why, it helps to start with the dominant model that most legal AI tools share. The overwhelming majority of AI-enabled eDiscovery and governance platforms are built on a collect-first assumption: data must be moved out of its native environment—copied, ingested, centralized in a vendor-controlled repository—before any AI model can be applied to it. This is not an incidental design choice; it reflects the fundamental architecture of how most of these platforms were built, long before AI became part of the product story. The result is what practitioners have come to call the “prompt wrapper” problem: an AI interface sits in front of a conventional data pipeline, and the underlying mechanics—the cost, the risk, the latency—remain largely unchanged. A large language model with a “middleware” workflow does not solve the structural problem of what happens to sensitive data before the AI touches it.

X1’s AI In-Place architecture inverts that assumption. Rather than requiring data to travel to an AI system, X1’s patented distributed micro-indexing technology deploys AI models directly into lightweight micro-indexes at the data source itself—across Microsoft 365 environments, file shares, cloud repositories, and endpoints. The AI executes where the data lives, and the data does not move. The implications run across multiple dimensions: data never leaves the enterprise perimeter, security policies and endpoint controls remain intact throughout the process, and the computational overhead and massive AI token costs associated with large-scale data ingestion is avoided entirely. For matters involving a terabyte of data or more—where centralized collection is not merely expensive but operationally infeasible—this architectural distinction is not incremental. It changes what is actually possible.

The workflow mechanics reinforce the point. AI models are deployed into X1’s distributed micro-indexes behind the firewall, execute against enterprise data in place, and surface AI-enriched insights—tags, classifications, risk scores—into a central console without the underlying data ever being collected or copied. That means targeted collection decisions, early case assessment, and information governance actions can be driven by AI-informed analysis conducted across the full enterprise data landscape, not just against a subset of data that has already been moved. The distinction matters because the scope of analysis in the collect-first model is constrained by collection costs; in the in-place model, analysis scope is no longer tethered to collection volume. Investigations and governance programs can, in principle, cast a much wider net analytically while actually reducing the volume of data that requires review.

Mandi Ross, CEO of Insight Optix, offered a perspective that cuts to the core of what makes this architecture commercially significant: “Enabling AI directly where the clients’ data resides fundamentally changes the economics, speed, and risk profile of enterprise data discovery, investigations and compliance workflows. With X1 Enterprise AI In-Place, we can deploy AI models, pre-trained or customized for specific matters, data queries, or compliance requirements—securely within client environments, dramatically accelerating time to insight without sensitive information being collected, duplicated, or centralized outside their control.”

Ross identifies three dimensions the in-place approach changes: economics, speed, and risk. On economics, a significant lever is the reduction in review population size—AI-informed pre-collection filtering means fewer documents proceed to human review. Additionally, costs associated with collection and processing, including expensive AI token utilization, are all but eliminated. On speed, running analysis in situ, without waiting for collection and ingestion cycles, compresses time to first insight—critical in time-sensitive investigations and regulatory responses. On risk, data that does not move cannot be breached in transit, does not reside in vendor infrastructure outside the client’s control, and does not generate the compliance exposure of large-scale cross-boundary transfers. Her comment reflects what experienced practitioners understand but marketing language tends to obscure: the most consequential question about any legal AI tool is not what the AI does, but what happens to the data before and during its operation.

The enterprise deployment model reflects design discipline that distinguishes AI In-Place from retrofitted solutions. Organizations retain centralized governance over AI usage while processing remains local under existing security policies and endpoint controls. AI capabilities are fully optional and configurable at the data source level—important for organizations operating across multiple jurisdictions with differing regulatory requirements—and customer data is never used to train, fine-tune, or enrich underlying AI models, addressing a standard due diligence concern in enterprise AI procurement.

The practical use case implications are significant across several domains. In legal and eDiscovery contexts, in-place TAR and pre-collection analytics allow AI-informed decisions about what to collect before collection begins, directly reducing review volumes and costs. In information governance, AI-driven classification and policy enforcement can operate continuously across the full enterprise data estate rather than against periodic snapshots, enabling more responsive and defensible governance programs. In security and investigations, real-time insider risk detection at petabyte scale—across endpoint and cloud environments simultaneously—becomes feasible where centralized architectures make it impractical. In each case, analytical scope is no longer constrained by collection logistics.

Most legal AI products apply AI to data after it has already moved through the conventional collection pipeline. AI In-Place asks a more fundamental question: whether the pipeline itself should be reconceived. We will demonstrate it live on Wednesday, June 24—for those evaluating enterprise AI in legal, compliance, or governance contexts, it is worth seeing what a genuinely different architecture looks like in practice.

Register for the June 24 AI In-Place™ Product Tour →

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Bringing AI to the Data: How X1 Search v11 Redefines Secure Enterprise Search

By John Patzakis

At X1, we believe the future of enterprise AI depends on a simple but often overlooked principle: data should not have to move in order to become intelligent. With the launch of X1 Search v11, we are introducing a fundamentally different approach—one that embeds AI directly into our index-in-place architecture. Rather than forcing organizations to centralize and copy their data into external platforms, we enable AI to operate exactly where that data already lives. You can read the full press release here: https://www.x1.com/x1-introduces-ai-powered-x1-search-delivering-secure-ai-in-place-for-individual-and-enterprise-users/

This release represents an important milestone for us and for our customers. As Chas Meier noted, “X1 Search v11 marks an important milestone in how organizations can safely apply AI…without compromising the security controls enterprise environments demand.” That statement reflects our core design philosophy: AI must adapt to enterprise security, compliance, and governance requirements—not the other way around.

With X1 Search v11, we are delivering AI capabilities directly within our micro-index. That means organizations can apply advanced intelligence—classification, categorization, and contextual analysis—across emails, files, and collaboration data without ever relocating that information. Everything happens in place, within existing security boundaries, whether on endpoints or across enterprise systems.

For large enterprises, this architecture unlocks an even more powerful capability: the ability to deploy their own trained and curated large language models directly into the X1 index. Instead of relying solely on generic, hosted AI services, organizations can operationalize models tailored to their data that reflect their internal policies, regulatory requirements, and business workflows. These models run directly against their data, in place, delivering highly relevant and controlled outcomes.

This approach stands in sharp contrast to traditional hosted AI platforms. In those models, organizations must copy and transfer massive amounts of sensitive data into third-party hosted AI platforms before any meaningful analysis can occur. That process introduces serious risks. Moving data to outside providers complicates compliance, potentially compromises IP, and creates new attack surfaces that most enterprises simply cannot accept.

Beyond security concerns, the traditional model also breaks down operationally at scale. Enterprises are not dealing with small data sets; they are managing dozens of terabytes of distributed, unstructured data. Attempting to duplicate and transfer that volume is not just costly; it is infeasible. The result is delays, fragmentation, and incomplete analysis—undermining the very promise of AI.

We have taken a different path. By bringing AI to the data through our distributed micro-indexing technology, we eliminate the need for data movement entirely. Models can be deployed directly to where data resides, enabling real-time analysis while preserving security, reducing infrastructure overhead, and scaling seamlessly across the enterprise.

We see X1 Search v11 as more than a product release—it is a shift in how enterprise AI is deployed. Organizations no longer have to choose between innovation and control. With AI in place, they can achieve both.

To see this in action, we invite you to join our upcoming live product tour on Thursday, April 23, providing a guided walkthrough of the new AI-enriched capabilities and flexible model deployment features.

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X1 Brings “AI In-Place” to the Enterprise—A Major Breakthrough for Secure, Scalable AI Deployment

By John Patzakis

Our latest announcement represents a true inflection point in enterprise AI. With X1 Enterprise’s newly introduced capability for AI in-place, organizations and their service providers will, for the first time, be able to deploy and execute large language models (LLMs) directly where enterprise data lives—without moving or copying that data.

This is more than a product enhancement; it is a fundamental shift in how AI is applied across the enterprise.

The Foundation: Efficient Text Extraction Is Critical for AI
Large language models (LLMs) are the core engines that power today’s AI revolution. These models rely entirely on textual input to perform reasoning, summarization, search, and analysis. That is why text extraction is the critical first step. LLMs can only operate once another process extracts the text from emails, documents and chats. Traditionally, that meant copying or exporting data to external systems hosted by third party vendors, a process fraught with risk, cost, and compliance challenges.

Solving the “Data Movement Problem” for Enterprise AI
So, the key barrier to enterprise AI adoption has been the reluctance to move sensitive corporate data to external AI platforms. Whether for security, governance or cost reasons, most enterprises simply cannot send their data outside their environment.

X1’s innovation solves that problem head-on. Instead of shipping sensitive data out to an AI system, X1 brings the AI to the data. Enterprises can now deploy their own proprietary models or open-source LLMs within the secure perimeter of their existing infrastructure, whether on premises or in the cloud. X1’s index-in-place architecture performs the text extraction and indexing where the data resides. By extending that same principle to AI—forward-deploying LLMs directly to enterprise data sources—X1 now enables AI in-place. The result: organizations can apply the analytical power of LLMs across their data without ever moving it.

Once the LLMs are deployed into the X1 micro-indexes, X1 will then auto-apply AI-informed tags, which a user can query globally from a central console and act upon through targeted data collection or remediation. Imagine petabytes of data on file servers, laptops M365 and other sources all AI-classified and then queried and collected on a highly targeted basis.

This means enterprises can now unlock powerful new use cases no matter the scale—AI-assisted compliance, risk monitoring, GRC audits, eDiscovery, and more—while maintaining full control of their data and eliminating the need for costly, risky data transfers.

Enabling Collaboration Between Enterprises and Their Advisors
William Belt, Managing Director and Consulting Practice Leader at Complete Discovery Source, described the impact succinctly:

“Enabling AI in-place where our corporate client’s data lives is game-changing. We look forward to working with our clients to deploy AI models that are either pre-trained or customized for a specific matter or compliance requirement utilizing the X1 Enterprise platform.”

This capability creates a new bridge between corporations and their professional advisors—consulting firms, law firms, and service providers—who can now collaborate directly with their clients to develop, fine-tune, and deploy customized AI models for specific business or legal needs.

Rather than relying on generic cloud-based AI tools, organizations can now build targeted, matter-specific LLMs that are tuned to their unique data and compliance requirements, all executed securely in-place through the X1 Enterprise Platform.

A New Era for Enterprise AI
With this release, X1 is redefining the architecture of enterprise AI. Its ability to perform distributed micro-indexing and in-place AI analysis across global data sources enables secure, scalable, and cost-effective intelligence—without ever duplicating or relocating sensitive data.

For enterprises and their partners, this represents a new era of possibility: true AI at enterprise scale, in-place.

X1 will host a webinar on Wednesday, December 10, featuring a detailed overview of this new capability and a live demonstration. You can register here.

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