Category Archives: m365

August 12, 2026 · 9:15 AM

AI In-Place Classification Takes Enterprise Information Governance to the Next Level

By John Patzakis and Chas Meier

A digital graphic featuring a globe and icons representing various digital communication and data systems, with the title 'AI In-Place Classification Takes Enterprise Information Governance to the Next Level' and a blog attribution for X1.

Ask anyone who has run an enterprise information governance program, and they will tell you the same thing: the defining challenges are scale and massive costs. A regulatory inquiry, privacy audit, data-breach response, M&A data separation, or records-remediation initiative can span mailboxes, file shares, endpoints, Microsoft 365, and other cloud repositories. Enterprise-wide matters routinely involve tens or hundreds of terabytes of unstructured data—and sometimes far more.

With decades of experience in this industry, we have rarely encountered a genuinely small enterprise information governance matter. Once an organization begins operating across its full data estate, it reaches a scale that traditional eDiscovery technology and workflows were never designed to handle.

That reality has shaped—and limited—what organizations could practically do. Conventionally, classifying or analyzing enterprise data required collecting it into a centralized platform and only then performing indexing, classification, search, or review. At multi-terabyte scale, that model becomes problematic on every axis that matters.

Collecting, transferring, and indexing the data can take weeks or months. It creates another large copy of an organization’s most sensitive information outside its original controls. It adds substantial processing, storage, and hosting costs. When the source is a hosted platform such as Microsoft 365, large-scale collection can also encounter service-protection and throttling limits that restrict throughput, often derailing large projects.

As a result, comprehensive classification across distributed enterprise data is most often technically difficult or economically prohibitive. Organizations sampled, narrowed scope, made assumptions, and accepted risks they could not fully see. When a regulator, major incident, or transaction made comprehensive treatment unavoidable, the alternative could be months of work, significant operational disruption, and tens of millions of dollars in expense.

AI In-Place classification changes that operating model. Instead of first moving an entire corpus into a separate AI or review platform, it brings classification to the distributed data through an architecture designed to process content close to its source. The resulting classifications enrich the corresponding indexes without requiring the organization to collect, host, and reindex a second centralized copy of its entire data estate.

That inversion is the difference between applying intelligence only to a small, preselected dataset and making classification practical across the broader enterprise.

X1 Enterprise makes this possible through its distributed micro-index architecture, refined through more than two decades of search and indexing development. Rather than aggregating all enterprise data into a single monolithic index, X1 creates independently managed micro-indexes aligned to meaningful scopes such as user mailboxes, OneDrive accounts, SharePoint sites, endpoints, and file-system locations. Additionally, data can be reviewed in-place for a “spot check” assessment and in-place keyword searches are available as an optional overlay.

X1 has now extended that foundation with patent-pending AI In-Place capabilities. AI models are deployed through the distributed X1 architecture to classify content associated with each micro-index. The classifications are written back as searchable and actionable index attributes. This allows organizations to add AI enrichment without first recollecting the source corpus or constructing an entirely new centralized processing environment.

Because micro-indexes can be created, updated, and enriched independently and in parallel, the architecture distributes work across available infrastructure and limits the effect of any individual update or failure. Classification can also be incorporated into scheduled or incremental index updates, helping organizations maintain a current understanding of their data rather than relying on a one-time snapshot. Millions of AI-enable tags are quickly applied without reindexing.

The practical implications are significant. Organizations can inspect documents, email, messages, and other unstructured content across distributed repositories; identify personally identifiable information, protected health information, payment-card data, privileged material, and other regulated or responsive content; record those findings in the index; and use them to drive search, review, collection, remediation, or policy-based action.

The objective is not to analyze only a sample, a single mailbox, or one repository at a time. It is to apply a consistent classification policy across the relevant enterprise estate.

The economics are equally important. Although the compute and storage requirements are materially reduced, they do not disappear. But the cost curve changes dramatically when classification no longer requires the entire corpus to be transferred, duplicated, centrally hosted, and reindexed before analysis can begin. Existing distributed indexes become the foundation for ongoing enrichment, and only the data that requires further review, collection, or remediation needs to move into downstream systems.

Work that once demanded large, centralized processing environments can instead be distributed across infrastructure designed to absorb enterprise scale. Scale remains a manageable factor, but it no longer has to be the reason an organization cannot perform the analysis at all.

Several governance and compliance use cases that were historically difficult precisely because they are enterprise-scale can therefore become routine. These include:

  • Discovering and remediating PII, PCI, and PHI that has escaped authorized systems and is residing in mailboxes, file shares, endpoints, or collaboration sites where it does not belong.
  • Supporting privacy obligations under GDPR, CCPA, and similar regimes, including data-subject requests, minimization, deletion, and applicable data-location or transfer restrictions.
  • Assessing the scope of a data breach quickly and under regulatory deadlines.
  • Identifying and remediating departed-employee data and insider-risk exposure.
  • Separating data for mergers, acquisitions, and divestitures.
  • Executing records-retention programs and identifying redundant, obsolete, and trivial data across the enterprise.

In each case, the business value has long been clear. The obstacle has been performing the work comprehensively, efficiently, and with minimal additional data movement. AI In-Place classification directly addresses that obstacle.

For years, enterprise information governance has been forced into a largely reactive posture—narrow in scope, expensive, and often a step behind the data. The problem was not a lack of governance objectives; it was a lack of technology capable of supporting them at the scale of modern enterprise information.

AI In-Place classification changes what is operationally and economically practical. It gives organizations a path toward governance that is proactive, comprehensive, and continuously updated across distributed enterprise data—while avoiding the need to centralize another complete copy of the underlying corpus.

There may be no such thing as a small enterprise information governance challenge. There is now a more practical architecture for addressing the large ones.

To learn more about X1 Enterprise and its AI In-Place capabilities, visit x1.com or contact sales@x1.com.

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July 30, 2026 · 9:35 AM

The AI Token Bill Comes Due: Why Enterprise Search Is the Hidden Driver of AI Cost — and How to Fix It

By John Patzakis

A graphic illustrating the blog title 'The AI Token Bill Comes Due: Why Enterprise Search Is the Hidden Driver of AI Cost — and How to Fix It'. It features a funnel shape with icons representing email, chat, and files, surrounded by digital elements and a blue background.

For two years, the enterprise conversation about AI was about capability: What can it do? Is it good enough? In 2026, that conversation changed almost overnight. The question now is the bill. Alexander Embiricos, who leads enterprise at OpenAI, put the shift plainly to TechCrunch in June: “Six months ago, I would have a conversation with a customer, and it would be all about ‘What can it do?’ Our conversations are never about that now. Now the conversations are about, ‘hey, we’re spending so much. What visibility do you have?…What token controls do you have? What is the efficiency of your models?’”

Anthropic — the maker of Claude — identifies inefficient retrieval as a primary culprit in its own engineering writing. In its November 2025 piece on advanced tool use, Anthropic describes the failure mode directly: when an agent fetches records across a data set, “every record accumulates in context regardless of relevance,” and when a large file is retrieved, “the entire file enters its context window.” Independent analysts reach the same conclusion. A June 2026 study measured a 26x per-query token gap between dumping full documents into context and retrieving selectively. A DeployStack analysis traced a routine two-step document workflow that “consumed 120,000 tokens” because a single file passed through the model twice, warning: “Run this 100 times a day across a team, and you’re looking at real money.” And Dennis Pilarinos of Unblocked names the specific culprit without hedging:

“Broad search is the default failure mode, and it’s the most expensive one.”

— Dennis Pilarinos, Unblocked

Enterprise search is the textbook worst case
Nowhere does this dynamic bite harder than search over large, unstructured stores — email inboxes, file shares, chat archives, document repositories. When an employee asks Claude to “find everything about the Henderson matter” or “pull the emails where we discussed pricing,” a single query can drag hundreds of matching messages, long threads, and full attachments into the context window as raw payload. The user pays for all of it, every time — even though only a handful of items actually mattered. The larger the store and the broader the query, the worse the ratio. And broad, unfiltered search over massive stores is exactly how people naturally use these tools.

The fix is architectural: search in place, then bring only what matters
If token cost is driven by data flowing through context, the highest-leverage optimization for enterprise search is not a spending cap — it is a change in architecture. Locate first; retrieve selectively. Search the data where it lives, then bring only the relevant results into the model. A design that returns a ranked result list, targeted snippets, and a pointer to the source document — rather than dumping full inboxes and file bodies into context — attacks the cost problem precisely where the evidence says it lives. This is, notably, the same conclusion Anthropic’s own engineers advocate.

This is exactly what the new X1 Search MCP Connector for Claude does. X1 maintains a local, enriched index over an organization’s actual content — files, emails, attachments, Microsoft 365, Google Workspace, chats — and searches that content in place, on the user’s own machine or behind the corporate firewall. Exposed to Claude through the Model Context Protocol, X1 returns exactly what the model needs and nothing it doesn’t: a compact ranked result list, targeted snippets, and a file location, instead of streaming entire mailboxes and documents through the context window. Claude then reasons over only the specific items that matter, opening full content on demand for the few documents actually under review. Because the matching happens locally, a query across a massive corpus costs roughly the same handful of tokens whether the index holds a thousand items or a million — and because only relevant results ever leave the machine, the data-exposure footprint shrinks at the same time, preserving confidentiality and privilege for legal, compliance, and government teams.

The ROI
The economics are not marginal. At current large-model rates, a single broad search across a year’s worth of emails or a file share can cost roughly $20 in AI tokens when the raw content is streamed into the model — versus a fraction of a cent when only the relevant results are passed to it. Across an organization with thousands of users searching throughout the day, that difference compounds into millions of dollars in avoided token costs each year, while simultaneously improving response speed and reducing data exposure. The savings scale directly with data volume, which means the connector becomes more valuable — not less — as an organization’s data grows.

The takeaway
The token bill has come due, and every fix now being marketed — observability tools, model routers, usage caps — treats the symptom. Each one helps a company watch its spending or throttle it; none of them changes the fact that, by default, enterprise data has to travel to the AI to be searched, at full token price, every single time. The durable answer is to invert that: bring AI to the data, not the data to the AI. Search in place, return only what matters, and let the model reason over the few items that count. That is not a workaround for the cost problem. It is the architecture the evidence — including the AI providers’ own — points to as the way out.

Sources

  • Anthropic, Introducing advanced tool use on the Claude Developer Platform (Nov 2025) — anthropic.com/engineering/advanced-tool-use
  • Anthropic, Code execution with MCP: building more efficient AI agents (A. Jones & C. Kelly, Nov 2025) — anthropic.com/engineering/code-execution-with-mcp
  • TechCrunch, The token bill comes due: Inside the industry scramble to manage AI’s runaway costs (Jun 5, 2026)
  • DeployStack, How MCP Servers Use Your Context Window (Jan 2026) — deploystack.io
  • Unblocked (D. Pilarinos), Why AI Agents Burn Tokens (Jun 2026) — getunblocked.com
  • The Token Tax of Epistemic Accuracy: Comparing RAG and Long-Context Architectures (arXiv, Jun 2026)

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Filed under Best Practices, Business Productivity Search, Cloud Data, Corporations, Data Audit, eDiscovery & Compliance, Enterprise AI, Enterprise Search, ESI, Google Workspace, Information Access, Information Governance, Information Management, m365

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July 7, 2026 · 10:13 AM

Kim v. Cushman & Wakefield: A Federal Court Confirms That Email Search Terms Don’t Work for Microsoft Teams

By John Patzakis

Blog header about the Kim v. Cushman & Wakefield case, discussing a federal court ruling on email search terms and their ineffectiveness for Microsoft Teams. Includes graphics of a gavel, documents, and message bubbles.

A recent decision out of the Central District of California should be required reading for any legal team that includes Microsoft Teams as data source in their discovery plan. In Kim v. Cushman & Wakefield U.S., Inc., 2026 WL 1353455 (C.D. Cal. Apr. 24, 2026), the court held that search terms that may be appropriate for email may not be sufficient for shorter, less formal communications on a collaboration platform like Teams.

The plaintiff, Ms. Kim, alleged pregnancy discrimination after being terminated upon her return from maternity leave. The defendant asserted the termination was part of a reduction in force; Ms. Kim alleged that rationale was pretextual. The discovery dispute arose when it emerged that the defendant had not searched Microsoft Teams at all—even though, as one of the defendant’s own witnesses testified, Teams was one of the primary communication methods used at the company. To its credit, upon discovering the gap, defense counsel immediately ran the existing email search terms against Teams and produced 47 pages of messages, two of which proved relevant to the pretext analysis.

That partial cure satisfied no one. The plaintiff demanded a nearly indiscriminate search of “all reasonably likely repositories,” while the defendant maintained it had already run the terms against Teams and “there’s nothing left.” The court’s response: “Neither position is quite right.”

The Teams Ruling: Keyword Searches Alone Are Not Enough
The heart of the opinion is the court’s recognition that rerunning email-oriented search terms against Teams data is structurally flawed. The defendant’s terms all required “Connie Kim” as an anchor—e.g., “Connie Kim” NEAR “terminat!”. As the court explained:

“It is arguable whether that may work well enough even for emails, but it cannot work for MS Teams chats about transition planning among managers who might say ‘the Smartsheet’ or ‘Brooke’s workload’ without mentioning Plaintiff by name. Keyword searches alone, without more advanced and thoughtful search techniques, will be inadequate for Teams data—a medium where conversations are shorter, more informal, and less likely to include full names than email.”

The court also underscored the certification obligation that attaches once a party elects to search: “An objecting party that elects to search and produce—rather than move for a protective order—undertakes an obligation to search reasonably. See Fed. R. Civ. P. 26(g)(1)(B).” And the Rule 26(b)(1) proportionality analysis weighed in the plaintiff’s favor as to Teams, since the messages already produced confirmed that relevant communications existed in that repository.

Notably, the court declined to dictate methodology, holding that how the defendant fulfills its supplemental search obligation— “whether through custodian-based collection, refined keyword queries, or technology-assisted review—is Defendant’s choice, so long as the search is reasonable and the production is complete.” The court also traced the root cause to a pro forma Rule 26(f) conference: had the parties conducted a substantive ESI conference identifying repositories, custodians, and communication platforms at the outset, the Teams gap would have been caught months earlier.

In his excellent writeup of this case, Michael Berman of E-Discovery LLC consulted eDiscovery expert Tom O’Connor of the Gulf Coast Legal Technology Center, who raised a critical practical question: what tool was actually used for the search? O’Connor explained that while keyword searches inside Teams work, Teams supports only basic keyword matching and a few command-style filters. Per O’Connor, the native “Teams search indexes chat differently than email,” in that it:

• “Prioritizes exact word matches;
• Does not index message metadata as richly as Outlook;
• Often misses partial-word matches; and
• Returns fewer results when the term is too specific.”

In other words, even well-crafted Boolean terms can silently underperform when run against Microsoft’s native Teams index.

Why Kim Illustrates the Case for X1 Enterprise
The Kim decision validates what we have long argued at X1: when addressing MS 365 data for eDiscovery, the search methodology applied to it must be purpose-built. As we detailed when we launched our advanced MS Teams support, X1 Enterprise enables a targeted, iterative search and collection of Teams data in-place, with the ability to target individual custodians and specific messaging threads—displacing any need to mass download channels—plus unified search across Teams, OneDrive, SharePoint, Mail, laptops, and file shares, and one-click upload into Relativity for review.

Critically, X1 does not rely on the limited native Microsoft Teams index that O’Connor describes. X1’s patented technology builds its own full-featured index of Teams data, enabling precisely the “more advanced and thoughtful search techniques” the Kim court demanded. That includes detailed Boolean queries with nested operators, proximity, and wildcard/stemming support that execute consistently across both email and chat data—so counsel is not forced to choose between Outlook precision and Teams looseness. X1 also includes the ability to search on emojis, which is critical for Teams and other chat platforms, where a reaction emoji may be the entire substance of a manager’s response to a message about a “transition plan.”

X1’s patented in-place search and classification capabilities extend this further. Through the X1 API, organizations can programmatically execute searches and apply AI-driven classification models directly where the data lives—before anything is collected. Applied to the Kim fact pattern, that means counsel can iteratively test and refine looser, Teams-appropriate search terms against live data, measure the results, and classify what comes back—building a defensible, documented search methodology of exactly the kind the court invited when it referenced “refined keyword queries” and “technology-assisted review.” And because it all happens in place, the proportionality benefits are built in as only potentially responsive data is collected.

The lesson of Kim is straightforward. Courts now expect parties to identify collaboration platforms like Teams at the Rule 26(f) stage, to search them with techniques suited to informal chat data, and to do so reasonably and completely. Meeting that expectation requires solutions designed for the job.

Learn more about the X1 Enterprise Platform, or contact our sales team to schedule a live demo.

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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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Filed under Best Practices, Cloud Data, Corporations, Cybersecurity, Data Audit, Data Governance, ECA, eDiscovery & Compliance, Enterprise AI, Enterprise eDiscovery, Enterprise Search, ESI, GDPR, Information Access, Information Governance, Information Management, m365, MS Teams, OneDrive, SharePoint

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April 8, 2026 · 9:49 AM

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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