Category Archives: eDiscovery & Compliance

June 16, 2026 · 10:11 AM

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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May 19, 2026 · 8:33 AM

De-NISTing in eDiscovery: A Costly Provision That Shouldn’t Be in Model Orders in the First Place

By John Patzakis

A model eDiscovery order I recently came across from a federal district court issued by a respected judge included a provision requiring parties to de-NIST their files in the course of eDiscovery production. On its face, this may seem like a reasonable technical requirement to some practitioners. But this provision reflects a fundamental misunderstanding of how proportional, targeted eDiscovery collection should work — and it points to a broader problem in our industry that deserves some attention.

For those unfamiliar with the term, de-NISTing refers to the process of filtering out known, irrelevant system files from a forensic collection using the National Institute of Standards and Technology’s reference database of known file signatures. The NIST database catalogs hundreds of thousands of known operating system files, executables, DLL files, and other system-generated data that have no evidentiary value whatsoever. De-NISTing removes these files from a collection so that reviewers are not burdened with wading through mountains of irrelevant system data. The reason you need to de-NIST in the first place is because you collected a full-disk image — capturing everything on the drive, relevant or not.

And that is precisely the problem with requiring de-NISTing in a model eDiscovery order. As I have written extensively, including in our recent white paper on proportionality in eDiscovery, courts have consistently held that full-disk imaging is not the appropriate default for civil litigation collections. Going all the way back to Deipenhorst v. City of Battle Creek in 2006, courts have warned that imaging a hard drive results in the production of massive amounts of irrelevant — and potentially privileged — information. More recently, in Motorola Solutions v. Hytera Communications Corp., the court emphasized that forensic examination of a party’s computers “is no routine matter” and that courts must use caution to avoid unduly impinging on privacy interests. A model order that presupposes full-disk imaging by requiring de-NISTing is, at minimum, inconsistent with this well-established body of case law.

The 2015 amendments to Federal Rule of Civil Procedure 26(b)(1) established a clear six-pronged proportionality framework for eDiscovery, requiring parties and courts to weigh factors including the importance of the issues at stake, the amount in controversy, the parties’ resources, and whether the burden or expense of proposed discovery outweighs its likely benefits. Courts have taken these amendments seriously and have consistently limited overbroad discovery requests on proportionality grounds. A blanket model order requirement to de-NIST implicitly endorses a collect-everything methodology that runs counter to the proportionality principles embedded in Rule 26(b)(1) and the extensive case law that has developed around it.

So how does a provision like this end up in a model court order? The answer, I believe, lies in the undue influence that certain eDiscovery service providers have had on collection practices and, ultimately, on the drafting of court orders and guidelines. Some service providers have a clear financial incentive to collect as much data as possible, since their fees are calculated on a per-gigabyte basis — meaning the more data collected, processed, and hosted, the higher the bill. This volume-based business model has shaped industry “best practices” in ways that favor over-collection, and that mindset has quietly seeped into the thinking of some federal judges and the model orders they issue. What gets dressed up as technical diligence is, in many cases, simply an artifact of a business model that profits from excess.

If you are conducting a properly scoped, targeted eDiscovery collection that is consistent with the principles of proportionality — as the Federal Rules and overwhelming case law require — there is simply no reason to de-NIST. A targeted collection does not reach system files, executables, DLLs, or other non-user-generated data in the first place. You are collecting potentially relevant ESI from identified custodians, scoped by search terms, date ranges, file types, and data sources. You never touch the data that de-NISTing is designed to filter out, which means the entire de-NISTing step — and its associated cost and processing time — is unnecessary overhead born entirely of an overbroad collection methodology.

This is precisely the approach built into X1 Enterprise, which enables legal and IT teams to conduct targeted, remote collections across large numbers of custodians without ever capturing the system-level data that necessitates de-NISTing. X1 Enterprise collects only the user-generated, potentially relevant ESI within defined parameters, preserving full metadata integrity and maintaining a documented chain of custody — satisfying every requirement for forensic soundness without the bloat, expense, and proportionality concerns of full-disk imaging. In an era where courts are increasingly scrutinizing eDiscovery costs and demanding proportionality, practitioners and judges alike should be asking not how to manage the mess created by over-collection, but how to avoid creating that mess in the first place.

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May 6, 2026 · 9:23 AM

Why Most Tools Fall Short for Large-Scale Information Governance and What Actually Works

By John Patzakis

For more than a decade, enterprise organizations have struggled with a persistent and costly challenge: how to effectively search, collect, manage, and analyze large volumes of unstructured on-premise data for information governance, eDiscovery, and enterprise search use cases. We are talking about environments with many terabytes of data distributed across file servers, email archives, endpoints, and Microsoft 365 data that must be rapidly interrogated, precisely analyzed, and in many cases urgently remediated in response to a regulatory inquiry, a data breach, or an M&A transaction. Despite the proliferation of tools claiming to address this challenge, none has ever truly solved it at scale. The core reason is architectural. Most of these tools are built on a flawed foundation from the start.

The gravitational pull toward Elasticsearch as the search foundation for enterprise data tools is easy to understand. It is open source, it is widely documented, and it is written in Java a language familiar to a large pool of developers. For these reasons, a basic centralized search and analysis tool can be assembled relatively quickly, and hundreds of vendors and in-house development teams have taken exactly this path. The problem is not that Elasticsearch lacks capability for general-purpose search. The problem is that general-purpose search and large-scale enterprise information governance are fundamentally different problems, and what works for one fails badly at the other. What is rarely discussed openly but what practitioners learn the hard way is that Elasticsearch’s architectural limitations are not configuration issues that can be engineered around. They are structural constraints baked into the platform’s design, and they surface precisely at the scale and complexity that serious information governance work demands.

The result is a graveyard of failed or severely limited information governance deployments: tools that work impressively in demos on curated datasets of a few hundred gigabytes, but that buckle, stall, or simply break when asked to operate on the multi-terabyte, distributed, live data environments that characterize real enterprise compliance projects.

The Structural Limitations of Elasticsearch for Information Governance
The memory problem with Elasticsearch begins with Java itself, which requires a significant amount of compute power over other code bases when addressing large volumes of data. The Java Virtual Machine (JVM) requires a heap to manage object allocation, and as data volumes grow, the memory demands scale dramatically. Each Elasticsearch index must be loaded into memory to be searched, and in a multi-terabyte environment with complex query patterns — the kind that information governance work consistently requires — the JVM heap pressure becomes severe and unmanageable. Organizations that have attempted to deploy Elasticsearch-based platforms against over 10 terabytes of enterprise data consistently encounter the same outcome: massive hardware requirements, constant tuning, and performance that degrades as the dataset grows rather than holding steady. The compute overhead is not a solvable problem; it is an inherent consequence of building a memory-intensive centralized index on a Java runtime, and it places a practical ceiling on what Elasticsearch-based governance tools can realistically accomplish.

Beyond the memory constraints, the workflow required to use Elasticsearch for information governance introduces a second, equally serious problem: it requires a full copy of the data under governance to be made and migrated into the centralized index. For a 50-terabyte dataset, this means creating 50 additional terabytes of sensitive material — often including personally identifiable information, privileged communications, and confidential business records — and transferring it outside its original, controlled location. Requiring the wholesale copying and centralization of that same data in order to govern it is a fundamental contradiction, one that legal, security, and compliance stakeholders increasingly and rightly reject.

The timeline problem compounds the data duplication problem. Copying, transferring, and indexing 50 terabytes of enterprise data into a centralized Elasticsearch platform is not a weekend project. In real-world deployments, this process can take months, even under favorable conditions. And information governance use cases are rarely patient ones. Data breach impact assessments operate under regulatory notification deadlines measured in days. M&A-related data audits run on compressed timelines driven by transaction closing schedules. By the time the data has been staged and indexed into a centralized Elasticsearch platform, the underlying data has changed, and the copied index set is already stale.

Finally, even if an organization tolerates the data duplication, survives the timeline, and manages the memory overhead, there is a “last mile” problem that the centralized Elasticsearch architecture cannot solve: remediation. Information governance is not just about finding sensitive or problematic data — it is about acting on it — Deleting records past their retention period. Quarantining compromised PII. Tagging and separating data in support of a corporate divestiture. When the discovery and analysis workflow is built on a centralized copy of the data, the organization is operating on clones, not originals. The identified data still exists in its original locations distributed across file servers, Microsoft 365 environments, laptops, and cloud storage. Tracing back from a finding in a centralized index to the live source, and then executing a remediation action on that source, is a manual, error-prone, and operationally disruptive process.

How X1 Enterprise’s Micro-Indexing Architecture Solves What Elasticsearch Based Tools Cannot
X1 Enterprise is built on a fundamentally different architectural premise: rather than requiring data to be copied and centralized, X1’s patented micro-indexing technology indexes, searches, analyzes, and remediates data entirely in place where it lives, within the corporate environment, without ever moving it. This architectural difference is consequential at every stage of a large-scale governance project. The micro-indexing engine is written in C++, which delivers dramatically more efficient memory utilization than a Java-based runtime. Individual micro-indexes do not need to be loaded into memory simultaneously; the architecture is genuinely distributed and parallelized, enabling X1 Enterprise to operate effectively at multi-terabyte scale, including at hundreds of terabytes, without the memory walls and hardware escalation that make Elasticsearch-based platforms impractical for serious enterprise deployments.

Because X1 Enterprise operates in place, the data duplication problem is eliminated entirely. There is no second copy of your sensitive data to govern, secure, or explain to regulators. The indexed data remains in its original location, under the organization’s existing controls, throughout the entire governance workflow. This means that X1 Enterprise not only avoids compounding compliance risk, it actively reduces it, by ensuring that sensitive data never leaves its controlled environment. For organizations subject to GDPR, HIPAA, CCPA, or sector-specific data residency requirements, the ability to conduct large-scale information governance analysis entirely within the corporate firewall is not a luxury. It is a hard requirement. X1 Enterprise is the only platform in the market that can meet this requirement at multi-terabyte scale without architectural compromise.

Perhaps most powerfully, the in-place architecture closes the remediation loop that Elasticsearch-based tools leave permanently open. When X1 Enterprise identifies data that must be deleted, preserved, tagged, or acted upon, it can execute that remediation directly on the source data in Microsoft 365, on file servers, on endpoints, wherever the data resides. There is no manual tracing back from a centralized index to a distributed original. The finding and the action occur in the same environment, with full auditability and chain-of-custody documentation.

X1 Enterprise delivers the architecture that the industry has needed for years.

To learn more, schedule a briefing today at sales@x1.com or visit x1.com/solutions/x1-enterprise-platform.

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February 12, 2026 · 10:15 AM

Navigating Legal and Compliance Risks When Corporations Expose Sensitive Data to AI

By Kelly Twigger and John Patzakis

Implementing AI within a corporate environment is no longer a matter of “if” but “how.” We recently addressed these challenges in our webinar, “Navigating Legal and Compliance Risks in AI,” where our panel of experts discussed the strategic transition required to build a robust risk mitigation framework. While the efficiency gains of AI—such as automating workflows and surfacing deep insights—are compelling, introducing sensitive enterprise data into these models without a tactical plan can lead to unintended consequences. These risks range from the dilution of trade secrets to complex eDiscovery obligations and substantial regulatory exposure under the GDPR.

To leverage AI safely, counsel should focus on the following grounded strategies for risk management.

Protect Trade Secrets
Under federal law, trade secret status is contingent upon the owner taking “reasonable measures” to maintain secrecy. This is a rigorous standard; if proprietary information—such as source code or high-value technical data—is fed into an unsecured AI model without strict access controls, a company risks losing its legal protections entirely.

  • Review the Judicial Standard: In Snyder v. Beam Technologies, Inc., the 10th Circuit affirmed that failing to use confidentiality protections or allowing information to reside on unsecured devices can defeat trade secret status.
  • Maintain Active Safeguards: Courts emphasize that consistent and active safeguards are required to maintain secrecy. Lax internal controls during AI interactions can be cited as evidence that “reasonable measures” were not maintained.
  • Implement No-Prompt Zones: Establish “No-Prompt Zones” for your organization’s most sensitive intellectual property. By isolating core IP from third-party cloud models, you maintain a defensible record of “reasonable measures” that can withstand scrutiny in litigation.

Manage the eDiscovery Paper Trail
AI interactions—both the prompts submitted by employees and the responses generated by the tools—are considered discoverable Electronically Stored Information (ESI). These records are part of the corporate record and are subject to subpoena and legal holds.

  • Understand the Technical Reality: Microsoft has confirmed that Microsoft 365 Copilot interactions are logged through the Purview unified audit log, making them searchable, preservable, and producible via eDiscovery tools.
  • Assess Scope of Exposure: Because these chats are treated no differently than emails, they may inadvertently expose privileged or damaging material if not managed properly.
  • Map Information Logs: Update your legal hold workflows to specifically include AI conversation logs and audit trails. Mapping where these logs live before litigation arises ensures a more controlled and cost-effective discovery process.

Navigate GDPR and Data Privacy
Processing customer or employee data through AI models requires strict adherence to the GDPR principles of data minimization, purpose limitation, and lawfulness. Feeding sensitive data into AI models without a clearly articulated lawful basis—such as consent or legitimate interest—can result in significant administrative fines.

  • Meet Compliance Requirements: European authorities require organizations to demonstrate compliance by documenting purposes, limiting data inputs, and ensuring appropriate safeguards are in place.
  • Identify Special Categories: The GDPR is particularly restrictive regarding health information or data revealing racial or ethnic origin, requiring specific exemptions for processing.
  • Conduct Privacy Impact Assessments: Perform mandatory Privacy Impact Assessments (PIAs) for any AI tool that touches personal data. Documenting the purpose and necessity of the processing is critical for maintaining regulatory standing during an audit.

Leverage In-Place AI Functionality
A critical strategy for reducing risk is shifting where the AI processing occurs. Rather than routing data through external, third-party cloud-hosted AI services, organizations should consider prioritizing workflows where AI is applied in-place within the corporate network or controlled enterprise environment.

  • Secure the Data Perimeter: By keeping data and AI processing behind the organization’s own security firewall, you materially reduce the risk of trade secret leakage and data exfiltration.
  • Minimize Third-Party Footprint: Applying AI in-place narrows the scope of discoverable third-party records, as the interactions remain within your internal infrastructure rather than residing on a vendor’s servers.
  • Establish Full Governance Control: This model provides counsel with direct control over privacy, retention, and audit obligations—essentially giving you the “kill switch” for data that you simply do not have with external cloud vendors.

Tactical Governance and Ethical Oversight
Counsel must navigate the professional and technical nuances of AI deployment to ensure long-term stability.

  • Ensure Professional Competence: The ethical duty of technological competence requires attorneys to understand the limitations of the tools they use. AI should be treated as a “junior associate”—capable of great speed but requiring diligent human verification of all output.
  • Apply Risk-Based Tiering: Not all AI use cases carry the same weight. We recommend a tiered approach:
    o Tier 1 (Administrative): Low-risk tasks involving non-sensitive data.
    o Tier 2 (Internal/Marketing): Standard communications requiring routine oversight.
    o Tier 3 (High-Value/Restricted): High-stakes processing involving PII, health data, or proprietary IP, requiring senior legal sign-off and strict data handling protocols.
  • Execute Proactive Vendor Vetting: Move from consumer-grade tools to enterprise solutions that offer SOC 2 Type 2 attestations. Ensure contracts explicitly prohibit the vendor from using your data to train their global models.

In light of these risks, corporate counsel should take a proactive, structured approach to AI governance. This includes implementing data classification and usage controls to prevent sensitive trade secrets from being exposed to AI systems without safeguards; establishing clear policies governing AI prompts, outputs, retention, and eDiscovery treatment; and conducting privacy impact assessments to ensure personal data processing complies with GDPR and similar regulations. In addition, counsel should carefully evaluate AI deployment models and consider workflows in which AI models are deployed in-place within the corporate network or controlled enterprise environment, rather than routed through third-party cloud-hosted AI services. Keeping data and AI processing inside the organization’s security perimeter can materially reduce trade secret leakage risk, narrow the scope of discoverable third-party records, and provide greater control over privacy, retention, and audit obligations—while still allowing the enterprise to realize the benefits of advanced AI capabilities.

For a deeper dive into these strategies and more case studies, you can watch the full session here.

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December 16, 2025 · 9:13 AM

AI Without Data Movement: X1’s Webinar Reveals the Future of Secure Enterprise AI

By John Patzakis

X1’s recent webinar announcing the availability of true “AI in-place” for the enterprise was both highly attended and strongly validated by the audience response. The session did more than introduce a new feature; it articulated a fundamentally different architectural approach to enterprise AI—one designed explicitly for security, compliance, and scalability in complex, distributed environments. Our central message was simple: enterprise AI adoption has been constrained not by lack of interest, but by architectural and security requirements that existing platforms have failed to address.

That reality was most powerfully captured in a quote shared on the opening slide from a Fortune 100 Chief Information Security Officer, which set the tone for the entire discussion:

“Normally AI for infosec and compliance use cases is a non-starter for security reasons, but your workflow and architecture is completely different. This allows us – all behind our firewall — to develop our own models that are trained on our own data and customized to our specific security and compliance use cases and deployed in-place across our enterprise.”

This endorsement crystallized the webinar’s core insight: AI becomes viable for the most sensitive enterprise use cases only when it is deployed where the data already lives, rather than forcing data into external or centralized systems.

The technical foundation that makes this possible is X1’s micro-indexing architecture. Unlike traditional platforms built on centralized, resource-intensive indexing technologies, X1 deploys lightweight, distributed micro-indexes directly at the data source. This allows enterprises to index, search, and now apply AI analysis without mass data movement. As emphasized during the webinar, centralized indexing is not just expensive and slow—it is fundamentally misaligned with how modern enterprise data is distributed across file systems, endpoints, cloud platforms, and collaboration tools.

The session then highlighted how this architectural distinction resolves a long-standing problem in discovery, compliance, and security workflows. Legacy platforms require organizations to collect and centralize data before they can analyze it, introducing delays, high costs, and significant risk exposure. X1 reverses that workflow. By enabling visibility and AI-driven classification before collection, organizations can make informed, targeted decisions—collecting only what is necessary, remediating issues in-place, and dramatically reducing both risk and operational overhead.

The discussion also demystified large language models (LLMs), explaining that while model training is compute-intensive, models themselves are increasingly commoditized and portable. Critically, LLMs require extracted text and metadata— processed from native files—to function. This aligns perfectly with X1’s existing capability, as text and metadata extraction are already integral to our micro-indexing process. AI models can therefore be deployed alongside these indexes, operating in parallel across thousands of data sources with massive scalability.

The conversation then connected this architecture to concrete, high-value use cases. In eDiscovery, AI in-place enables faster early case assessment and proportionality by analyzing data where it resides. In incident response and breach investigations, security teams can immediately scope exposure across distributed systems without waiting months for data exports. For compliance and governance, AI models can continuously identify sensitive data, enforce retention policies, and surface risk conditions that were previously impractical to monitor at scale.

In addition to a live product demo showcasing this new capability, we concluded the webinar with several clarifying points and announcements. First, we emphasized that X1 does not access, monetize, or host customer data. Also, AI in-place is not an experimental add-on but an enhancement to a proven, production-grade platform. And notably, there is no additional licensing cost for the AI capability itself—customers simply deploy models within their own environment. With proof-of-concept testing beginning shortly and production deployments targeted for April 2026, the webinar made clear that AI in-place is not a future vision, but an imminent reality for the enterprise.

You can access a recording of the webinar here, and to learn more about X1 Enterprise, please visit us at X1.com.

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