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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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Filed under Best Practices, Business Productivity Search, Desktop Search, Enterprise AI, Enterprise eDiscovery, Enterprise Search, ESI, Google Workspace, Information Access, Information Management, m365, MS Teams, X1 Search 11

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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Filed under Cloud Data, Corporations, Cybersecurity, eDiscovery, eDiscovery & Compliance, Enterprise AI, Enterprise eDiscovery, Information Governance, m365