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.
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.
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
- 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.
- 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.
- 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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