Now we are beginning to see what happens when that standard is applied. In one recent evaluation, experienced attorneys reviewed CoCounsel Legal’s performance across 50 complex CoCoBench tasks. Each task was estimated to require a lawyer an average of six hours to complete.

CoCounsel Legal completed each task in less than eight minutes.

More significantly, attorneys determined that CoCounsel Legal produced a stronger response than the original expert-written reference answer on nearly 40% of the tasks. The speed is remarkable. But speed is not the most important finding.

The more consequential result is that an agentic legal system, evaluated by experienced lawyers against realistic legal work, can do more than produce a plausible response. It can produce work that attorneys judge to be complete, accurate, well-supported, and usable in practice. That is a materially different standard from performing well on a public benchmark or delivering an impressive product demonstration.

And it changes how legal AI performance should be understood.

From benchmark performance to professional performance

Traditional AI evaluation often begins with a predefined answer and asks whether the system reproduced the expected elements.

That can be useful for testing a discrete capability. But legal work is rarely a matter of locating one answer or checking one box.

A lawyer may need to review an unfamiliar complaint, identify the relevant claims, research the governing law, assess potential defenses, and translate the analysis into a memo appropriate for a client. The value of the final product depends on how well all of those steps work together.

A response can identify the right doctrine but apply it incorrectly. It can reach a defensible conclusion while omitting a material issue. It can cite a real authority that does not actually support the proposition attached to it.

Those failures can disappear inside a benchmark score reliant solely on binary criteria. They are much harder to hide from an experienced attorney reviewing the output as actual work product.

That is the shift CoCoBench is designed to make: from measuring whether data is present to determining whether the work is professionally usable.

What a realistic legal AI test looks like

Consider one of the tasks included in CoCoBench. The scenario begins when a client receives an antitrust complaint naming it as a defendant. The client asks counsel to assess the claims and identify potential defenses.

The agent receives the complaint as its sole source document. It must identify the salient facts, research the applicable law for each claim and defense, evaluate which defenses may be viable, and prepare a memo written for the client.

This is the type of assignment litigators routinely face at the beginning of a matter. The available information may be incomplete. The issues may be ambiguous. The relevant law is not packaged neatly inside the source materials.

The task was authored by Jon Faria, a Senior Specialist Legal Editor at Practical Law who previously practiced as an antitrust litigation and investigations partner at Kirkland & Ellis.

That background matters.

Jon is not constructing a test that merely resembles legal work. He is reconstructing a problem he encountered in practice and applying the expectations he would have brought to an associate’s work product.

That is fundamentally different from generating a synthetic scenario and using another model’s answer as the standard of correctness.

The difference attorney judgment makes

CoCoBench combines automated evaluation with direct review by experienced attorneys.

The automated layer allows every run to be evaluated consistently and at scale. It identifies regressions, isolates specific failures, examines citation support, and helps engineering and data science teams understand where performance is improving.

Attorney review asks a more demanding question: would this work hold up in practice? Attorneys assess outputs across four dimensions:��

• Correctness: Are the factual and legal claims accurate, and are the citations used properly?��

• Completeness: Does the response address the full assignment, including the analysis that materially affects the conclusion?��

• Readability: Is the output organized and clear enough for a practitioner to use without reconstructing it?��

• Overall judgment: Taken as a whole, is the work fit for professional use?��

This review can reveal distinctions that a conventional benchmark may flatten. Two systems might both mention the relevant legal standard. One simply states it. The other explains its elements, applies them to the facts, addresses competing interpretations, and reaches a conclusion that a lawyer could defend.

A presence-based benchmark may reward both. A practicing attorney will not treat them as equivalent.

The hardest failures are often the ones that look right

One of the most important findings from our evaluation work is that legal AI failures are not always obvious.

A fabricated case is serious, but it is also relatively easy to recognize once someone attempts to verify it.

Misattribution can be more dangerous. A system may make a legally accurate statement and cite a real case, yet the cited passage does not actually support the claim. Everything appears credible: the authority exists, the proposition sounds plausible, and the citation is formatted correctly.

The failure lies in the connection between the claim and the authority. CoCoBench evaluates that relationship directly. It distinguishes among claims that are properly supported, claims that lack citations, claims based on incorrect reasoning, fabricated authorities, and propositions attributed to the wrong source.

It also differentiates between levels of misattribution. A passage that partially supports a reasonable inference is not the same as a claim whose real support appears only in an entirely different authority.

Those distinctions matter because they point to different technical problems and different levels of professional risk. A single accuracy score cannot show that.

Why the results matter

The initial results demonstrate what becomes possible when an advanced agentic system is paired with authoritative legal content, realistic evaluation, citation verification, and continuous attorney involvement. But they also expose a broader problem in the legal AI market.

Systems are often compared using measures that reward fluency, isolated task completion, or performance against synthetic reference answers. Those measures can create the appearance that several products perform at roughly the same level. When the evaluation moves closer to real legal work, the differences become clearer.

Can the system sustain accuracy across a six-hour assignment rather than a single turn prompt? Can it recognize which issues require deeper research? Can it connect each legal proposition to the authority that actually supports it? Can it produce a deliverable that an experienced lawyer would use as a credible starting point without redoing the core work? Those are much harder questions. They also require far more than access to a frontier model.

They require realistic legal tasks authored by practitioners. They require gold-standard responses grounded in substantive expertise. They require evaluation systems capable of testing both the final deliverable and the sources behind it. They require attorneys who can distinguish between an answer that sounds right and work that is right.

�������� can bring those elements together because legal expertise is not being added to the system at the end. It exists throughout the process.

It is embedded in Westlaw and Practical Law content authored, reviewed, and continuously maintained by attorney-editors. It guides the agents. It shapes the tasks used to evaluate the system. It informs the criteria against which outputs are judged. And it provides the continuous feedback used by our product, engineering, and data science teams to improve CoCounsel Legal.

That combination is difficult to build and even harder to sustain at scale.

A higher bar for legal AI

The question facing the legal industry is no longer whether AI can generate legal language.

It can. The question is whether an AI system can complete complex legal work accurately enough, thoroughly enough, and transparently enough for a professional to rely on it.

As agents take on longer workflows, involvement in evaluation becomes more important, not less. Errors introduced early can carry through research, analysis, drafting, and revision. A polished final answer can conceal weaknesses in the work that produced it. That is why the standard cannot be how intelligent the output sounds.

The standard must be whether the work can be examined, verified, explained, and defended.

The early CoCoBench results show that this standard is achievable. They also show why the way legal AI is evaluated will increasingly determine which systems are truly ready for professional work.

Because once legal AI is evaluated like legal work, the leaderboard changes.

Read the for a deeper look at CoCoBench’s attorney-authored tasks, automated evaluation framework, citation analysis, and attorney review methodology.��

Global Connected Platform��

CoCounsel Integration with NetDocuments (Canada)��

CoCounsel Legal customers in Canada can now bring documents directly from NetDocuments into CoCounsel Legal through the new NetDocuments ndConnect integration. Instead of manually downloading files and re-uploading them, legal professionals can access their firm’s NetDocuments content directly inside CoCounsel Legal — reducing context switching and making it easier to bring trusted firm documents into AI-assisted research, analysis, and drafting workflows.


(Click on image to expand)

Smokeball Knowledge Search Connector (US & Australia)��

The new Smokeball Knowledge Search connector makes a firm’s matter documents searchable directly inside CoCounsel Legal, so attorneys can pinpoint the right file no matter where it lives — without bulk-loading every document into the platform. Knowledge Search surfaces only the files that matter, alongside authoritative Westlaw and Practical Law content. By grounding AI in a firm’s own matter documents alongside trusted Westlaw and Practical Law content, attorneys get more relevant answers and can search across every connected repository at once — saving the time otherwise spent hunting across separate systems.

(Click on image to expand)

Unified Search and Upload Modal��

CoCounsel now offers a unified search and file upload experience, powered by Knowledge Search, that replaces fragmented upload flows with a single modal. Users can upload files from their device, pull from CoCounsel, or search across connected document management systems such as iManage and SharePoint — finding what they need across every source without knowing where it lives. The result is less time troubleshooting uploads and more time on the work that matters and greatly simplifying file discovery.

(Click on image to expand)

Built for How You Work

Import Your Complex Playbook in CoCounsel for Word (UK, Canada & Australia)

Instead of rebuilding a firm’s playbook clause by clause inside CoCounsel for Word, customers in the UK, Canada, and Australia can now import an existing playbook directly. Simply upload a file, and CoCounsel intelligently reads and structures the content — including preferred clauses, negotiation guidance, escalation guidance, scenario guidance, fallback clauses, and tabular content — bringing a firm’s institutional knowledge into the platform and ready to work, with no rebuilding required. This eliminates the manual, error-prone work of recreating firm guidance clause by clause — preferred language, negotiation positions, escalation rules, and fallback clauses come in as-is, for faster setup and a playbook that fully reflects the firm’s own language and style.

(Click on image to expand)

Agentic Playbooks in CoCounsel for Word (US)��

A new, agentic contract review layer is now available for transactional attorneys and corporate counsel in the U.S. CoCounsel handles the orientation, setup, and first-pass work automatically — identifying the agreement, the represented party, and the applicable playbook before the attorney even begins. From there, attorneys can review issue-by-issue or accept in bulk, and iterate on redline language conversationally, in plain English, shifting their role from operator to decision-maker. By eliminating the manual setup that slows down every contract review, attorneys move straight to shaping, reviewing, and approving the agent’s work — cutting the time from first read to final redline.

(Click on image to expand)

Personal Injury Content Expansion on Deep Research (Westlaw Advantage UK)��

Personal injury quantum content is now part of Deep Research on Westlaw Advantage UK, including Lawtel Quantum Reports, Kemp Quantum Reports, and the Judicial College Guidelines for the Assessment of General Damages (18th Edition). PI practitioners can now ask natural-language quantum questions and receive a structured Deep Research report that brings this content together — without manually searching Lawtel, Kemp, and the Judicial College Guidelines separately. This gives PI practitioners a faster, more consistent way to benchmark general damages and build quantum assessments, without losing time toggling between separate resources.


(Click on image to expand)

Explore These New CoCounsel Legal Features Today��

Sign in to CoCounsel Legal today to put these new research, drafting, and document-management capabilities to work, or explore training options at the .��

To learn more, please visit the .��������

After relying for years on an alliance-based audit solution, Isler CPA began evaluating new audit technology to determine what would best support its audit team going forward. As a small to mid-sized accounting firm, Isler CPA approached the process as more than a platform replacement, viewing it instead as an opportunity to invest in connected technology that could improve consistency, reduce manual effort, and better support its professionals across the audit lifecycle.

In evaluating providers, the firm wanted confidence the technology was mature, practical, and ready to support audit work in real-world engagements. �������� stood out for the strength of its audit solutions, the demonstrated investment in innovation, and the responsiveness of its team throughout the evaluation and implementation process.

Cody Savey, the lead partner of Isler CPA’s audit department, said: “We’re a small firm, and we rely on bigger providers to design our audit methodology. �������� impressed us with their advancement in technology and exceptional customer service.”

Among the solutions adopted, CoCounsel Audit has already delivered meaningful value by helping reduce time spent researching audit topics. The tool’s ability to provide sourced responses has been especially valuable in an audit environment where speed and verification matter.

CoCounsel has been a game-changer for the firm, saving hours of research time and enabling auditors to focus on high-value tasks. “I love CoCounsel,” said Savey. “It has saved me a ton of time. The amount of time CoCounsel is saving me in researching topics is hours at this point.”

For Isler CPA, the value of �������� audit solutions extends beyond research efficiency. The firm was also looking for a more connected audit process — one that could better link planning, identified risks, fieldwork, and conclusion. Guided Assurance and Engagement Manager help support that continuity, reducing the inefficiencies that can arise when key parts of the audit process become disconnected and helping teams stay aligned throughout the engagement.

“As a small firm, we need audit technology that connects planning, risk assessment, fieldwork, and conclusion,” Savey added. “That kind of automation helps the audit flow more effectively, supports consistency across the engagement, and gives us greater confidence that we are addressing the right risks throughout the process.”

For the firm, access to modern audit technology also supports a broader strategic goal. As competition and consolidation continue across the profession, Isler CPA sees advanced technology as an important part of maintaining its independence and preserving the culture it has built.

“We’re proud of the culture we’ve built at Isler,” Savey said. “Having access to modern technology helps us stay current, work more effectively, and continue growing without losing what makes our firm unique.”

“Firms of all sizes are looking for practical ways to make audit work more connected, efficient, and scalable,” said Corey Wells, General Manager of Audit at ��������. “Isler CPA’s adoption of CoCounsel Audit, Guided Assurance, and Engagement Manager reflects how firms are focused on innovation and using automation in a focused way — reducing time spent on manual tasks, improving workflow continuity, and enabling professionals to spend more time on judgment, analysis, and client service.”

Today, we are sharing early benchmarking results for Thomson, a first of its kind AI model. Across a range of benchmarks assessing legal and general capabilities, Thomson performed competitively with the strongest frontier models on the market, including Claude Opus 4.8, and ahead of GPT-5.5, Claude Sonnet 5, and Gemini 3.1 Pro. ��

Why? Because it knows the work.��

Launching later this summer, Thomson is the newest layer of the �������� AI strategy, and a demonstration of what becomes possible when authoritative content, expert judgment, professional tools, and model development come together.��

In 2024, �������� acquired Safe Sign Technologies, an AI research company. At the time, the market was betting that access to increasingly powerful general-purpose models would be enough.��

We made a different bet. We believed the future of professional AI would require more than general-purpose intelligence. It would require models built specifically for the domains, standards, and consequences of professional work. We believed that the distinct advantages of �������� decades of world-class content and expertise could be best expressed in a model that we ourselves crafted.����

Thomson is the result of that bet. And it is why �������� will continue to set the standard for Fiduciary-Grade AI. ��

Meet Thomson��

Thomson starts from a strong open-source foundation, so it performs general-purpose work just as effectively as the frontier models. It then goes further: trained using state of the art mid-training and post-training techniques on decades of authoritative content from Westlaw, Practical Law, Checkpoint, and Reuters, content professionals have staked their reputations on for generations.��

That training was shaped by hundreds of subject matter experts who evaluated outputs, identified failure modes, and validated that the model reasons the way legal professionals actually work. The same professional standard governs how Thomson is deployed. Customer data is never used to train the model.��

The result is a model that thinks and reasons like a lawyer while outperforming models multiple times larger on the work that matters.����

Thomson Matches the Best. And Beats the Rest.����

We evaluated Thomson against the leading general-purpose models on the market for general professional work and categories spanning:��

Thomson is competitive with the world’s leading frontier models despite being a fraction of their size and cost to train and operate. �������� has achieved that performance�� by combining exceptional AI talent with authoritative proprietary content and deep domain expertise. And with less than 10% of �������� content used in its training so far, there remains significant opportunity to expand its capabilities.��

Instruction Following is a composite average of the IFEval and FollowBench benchmarks. ��
Reasoning is a composite average of the GPQA Diamond, HLE, and MMLU-Pro benchmarks.�� ��
Coding is a composite average of the SWE-Bench Pro and Terminal-Bench 2.1. ��
Long Context is a composite average of the Infinity Bench as well as some internal benchmarks developed by ��������.��

These evaluations show Thomson’s competitiveness with industry recognized benchmarks. Our internal evaluation and training cover a wide range of scenarios, including carefully designed agentic use cases optimized for real-world professional work, tens of thousands of real-world queries written by experts, end-to-end deep research training with human-calibrated judges, and data-centric mid-training on a large amount of our content. To increase safety and robustness, we conduct training and evaluations consistent with �������� values, and stress-test the models through human and automated red-teaming. Thomson is still early in its development. To date, less than 10% of �������� content has been used in its training, leaving significant opportunity to expand its domain knowledge and capabilities through additional training, rigorous evaluation, and expert validation.��

Thomson also showcases its strength when a native integration with �������� content is added. When compared with leading frontier models given unrestricted access to the web, Thomson’s access to proprietary data sources such as Westlaw, Practical Law, and Reuters news ensures both superior completeness and factuality (i.e. the ability to back up claims through accurate citations to trusted sources).

This evaluation covers 53 legal research queries written by our internal Subject Matter Experts to represent real world questions. LLM’s are connected via an in-house agentic harness to Westlaw/Practical Law for TR content and Brave search engine for web search. Completeness and Factuality are scored using LLM’s as a judge. Completeness is scored based on SME-written rubrics that list every element that would be required for a good answer to the question. Factuality is based on extracting the claims made in each report and checking whether the cited sources provide evidence for each claim. These metrics were developed and calibrated against SME scoring.

The First Deployment. Not the Last.��

Thomson’s first integration will launch in August inside Tabular Analysis in CoCounsel Legal, and that choice was deliberate. Tabular Analysis performs high-volume, structured document review against a clear, measurable accuracy standard. It is exactly the kind of work where a purpose-built model has a demonstrable advantage over a general-purpose alternative, and where that advantage is immediately visible to the professionals relying on the output. Thomson will become the default model powering Tabular Analysis, and over the next year we will continue to integrate it across the �������� product portfolio in legal and tax.����

Content. Expertise. Tools. Now the Model.��

�������� has always brought together authoritative content, deep domain expertise, and the tools professionals rely on every day. Thomson adds the fourth element: a model purpose-built to power it all, trained on content competitors cannot access and validated to the standard professional’s demand. It delivers frontier-level performance at a fraction of the size and operating cost of many general-purpose models. A model only �������� could build.��

This is our commitment to Fiduciary-Grade AI in action: AI designed for professionals with duties of care and accountability, where almost right is not good enough.����

General-purpose AI is built for everyone. Thomson is built for the professionals who cannot afford to be wrong.

She’s brought to life by comedic actor Beth Dover, and she’s about to be hard to miss. The CoCo arrives in a fully integrated campaign spanning TV, social, streaming radio, out-of-home and trade events, rolling out across our core professional markets. It’s also the most ambitious brand campaign we have mounted in more than a decade.

Why a character, and why now

Most technology gets explained. We wanted CoCounsel to be felt. Professional audiences have heard every AI promise by now, and most of it sounds the same. A feature list doesn’t cut through that. A personality does. The CoCo is how people meet CoCounsel — not through a spec sheet, but through presence, authority and the kind of confidence that only comes from actually knowing the answer. She’s memorable on purpose, because the point we’re making deserves to be remembered.

There has been a lot of talk about what AI might do someday. The CoCo is here to talk about what CoCounsel already does, for the professionals who can’t afford to be wrong.

“Professionals in law, tax and compliance don’t have the luxury of being wrong. That’s why CoCounsel exists. It combines the trusted content, technology and expertise �������� has spent more than 175 years building and puts it to work in moments that matter most. The CoCo gives that story a voice. She’s confident, authoritative and impossible to ignore—just like the professionals we serve. This campaign is about more than introducing a character. It’s a statement about what �������� is today: a technology company powered by trusted intelligence, helping professionals navigate a world that grows more complex every day,” said Amy Messano, Chief Communications and Marketing Officer at ��������.

Agentic AI, Grounded in Expertise��

Early Access to the Next Generation of CoCounsel Legal is Here and Available for U.S. Customers����

Every U.S.��CoCounsel��Legal customer now has access to the next-generation experience. Through a simple,��in-product toggle, users will unlock a smarter chat interface��that��works the way an attorney works with a teammate. Describe your matter in plain language.��CoCounsel��Legal creates a plan, dives deep into legal authority, reasons through the legal issues, retrieves what it needs from your own precedents and from Westlaw and Practical Law, and drafts with citations. When��new information��changes the picture, it adapts. Instead of getting piecemeal answers, you get a professional standard response that you can iterate on.����

As part of early access, the next-gen experience is still actively being developed, and new capabilities will be added on a rolling basis through general availability later this summer. General Availability later this summer! ����

Read more

Deep Research Verify on Westlaw Advantage UK��

Deep Research��Verify��helps customers review Deep Research reports with greater confidence by automatically checking whether cited authority supports the assertions made. It��validates��cited Westlaw and Practical Law sources, highlights relevant supporting passages, and flags potential misattributions or��mischaracterisations. Instead of manually verifying every assertion from scratch, lawyers can assess whether the cited authority supports the point and��follow��a suggested research path for further review — moving from AI-generated insight to well-grounded legal judgment. Verify is initially available in Westlaw native, with Practical Law native and the��CoCounsel��app to follow.��

International Legal Research��

International Legal Research provides answers to legal queries based on official and trusted legal sources across multiple��jurisdictions. This new skill retrieves legal information from approved legal websites for France and Germany (including EU law) and��is designed specifically for professional legal research — not general web browsing. Legal professionals can��maintain��a natural, conversational flow when conducting cross-border research, asking questions in plain language while the system intelligently��determines��the��appropriate research��path. Every result comes exclusively from authoritative legal sources, ensuring jurisdiction-specific accuracy without concern about unreliable or non-compliant information.��

Joint Deep Research in��CoCounsel��Web Application (Canada)��

Joint Deep Research is now live within the��CoCounsel��web application for Canadian customers. In one seamless experience, lawyers can move from question to strategy to execution in a single, uninterrupted workflow — grounded in the authoritative Westlaw and Practical Law content they already trust. Joint Deep Research can be accessed directly within��CoCounsel��either by asking a legal research question in the chat box or by choosing Deep Research from the��CoCounsel��Library, enabling AI-powered, multi-step legal research without ever switching tools.��

Built for How You Work��

On-Demand Download and Copy Experience in CoCounsel��

CoCounsel now offers a new way to download results from skills and workflows that gives customers more control over how they use their output. Once a skill or workflow finishes, users can choose the exact file format and citation style they want — including two new formats, PDF and Markdown, and citation styles such as endnotes, footnotes, or none. A new Copy option also lets users copy just the core content with formatting and styling retained and citations removed, for quick reuse in documents or emails. The result is an easier way to get the right output, without unnecessary extra files or formatting.��

��

��Clause Library in��CoCounsel��for Word (Canada & Australia)��

The personal clause library in��CoCounsel��for Word — already available in the US and UK — is now available to Canada and Australia customers. Lawyers can save��frequently��used clauses for quick access during drafting and negotiation, adding them to the library manually, saving them after AI drafting, or importing them from contracts or playbooks. Most lawyers keep a file of go-to clauses in a cumbersome document or notes file; now they can save time by pulling trusted language directly inside the��CoCounsel��Word add-in.��

Explore These New��CoCounsel��Legal Features Today��

Sign in to��CoCounsel��Legal today to put��these new research, drafting, and document-review capabilities to work. Or explore training options at the��.��

To��learn more, please visit��the��.��������

Through �������� expanding partner ecosystem of legal technology companies and law firm partners, CoCounsel Legal is connected to the tools legal professionals already rely on, from transaction management and case intelligence platforms to enterprise search and specialized practice workflows. This works in both directions: bringing more capabilities into CoCounsel Legal, and embedding CoCounsel Legal capabilities into other solutions, so firms can move faster, work smarter, and deliver better results with connected workflows.��

The outcome is a legal practice that runs the way the practice of law demands: greater speed, sharper insight, and the confidence to deliver.��

A Partner Ecosystem Built on��Shared High��Standards��

Law firms face an overwhelming market of solutions with few reliable ways to evaluate what is trustworthy, what is secure, and what actually belongs in a legal tech stack. �������� has done that work. Every partner goes through a rigorous vetting process that evaluates their technology, security controls, commitment to data protection, and alignment with the standards legal professionals rely on.��

The outcome is a shared partner ecosystem where legal professionals have seamless access to the intelligence, tools, and connected workflows they need, without the friction of switching between disconnected systems, and without compromising the standards their profession demands.��

“The partnerships��we’re��building around��CoCounsel��Legal reflect how the practice of law��actually works: interconnected,��time‑sensitive, and��outcome‑driven. By integrating intelligence directly into workflows, we help firms move faster and��operate��with greater precision.”

-Steve Assie, General Manager, Global & Large Law Firms, Thomson Reuters

The��Partner Ecosystem��in Action��

��Document Review Inside the Deal Workflow����

Legal teams rely on transaction management platforms to coordinate the work that moves deals forward: collaboration, transparency, checklists, document organization and version control, deal status monitoring, signature page creation and collection, and generating final closing sets. When document review and analysis happen outside that workflow, teams must move files into separate tools and then bring the results back into the platform. This��adds��friction, delays, and the potential for lost context at the moments when speed and accuracy matter most, particularly in time-sensitive transactions.

DealCloser is a transaction management platform used by law firms and in-house legal teams across a broad range of transaction types, including mergers and acquisitions, banking and finance, real estate, venture capital, private equity, restructuring, and other complex transactions. Through its partnership with ��������, CoCounsel Legal’s document review capabilities are now embedded directly within DealCloser, bringing AI-powered review to the full transaction lifecycle. Teams can analyze contracts, amendments, exhibits, and diligence materials without leaving the platform where the deal is being executed — no manual uploads, no version uncertainty, and no need to reconcile findings across tools.��

: Bringing Firm Knowledge into��CoCounsel��Legal����

Law firms store years of work product across document management systems, intranets, email archives, and other repositories. Their briefs, memos, negotiated agreements, and templates��contain��valuable institutional knowledge that differentiates them. Yet that information can be difficult to surface and apply quickly, particularly when content is spread across multiple systems.����

DeepJudge��helps firms realize the full value of that collective knowledge through enterprise search, AI agents and workflows, and governed access controls. Lawyers can rapidly find, analyze, and apply the most relevant information across millions of documents in��iManage, NetDocuments, SharePoint, HighQ, network drives, and many other sources.����

The partnership between DeepJudge and �������� connects this institutional intelligence to CoCounsel Legal. Whether attorneys are working in Microsoft Word, CoCounsel, Westlaw, or Practical Law, they can use DeepJudge to rapidly retrieve and synthesize key language, facts, and precedent from internal documents and work product, making firm knowledge available in CoCounsel Legal for research, analysis, and drafting workflows. Firm precedents and external legal authority are accessible and actionable in the same workflow, with existing permissions and ethical walls carrying through.��

: Practice Management and Legal AI in One Ecosystem����

Small and mid-sized law firms often have too many disconnected tools. When practice management, document storage, legal research, drafting, billing, and document analysis live in separate systems, lawyers spend time on administrative friction instead of legal work, and lean teams with tight margins feel that cost acutely.����

Smokeball is a cloud-based practice management platform built for small to mid-sized firms, covering intake, matter management, automatic time capture, billing, and client communications. The combination of��CoCounsel��Legal and Smokeball unites deep legal content and advanced AI with comprehensive practice management, powered by technology that understands both the substance of legal work and the mechanics of running a firm.����

Smokeball matter documents can now be brought directly into��CoCounsel��Legal in bulk,��eliminating��manual uploads and the��version��confusion that comes with moving files between disconnected systems.����

The result is a more continuous workflow from matter management to substantive legal work. Attorneys can use CoCounsel Legal to rapidly analyze, draft, and research with their actual matter documents at hand, grounded in authoritative Westlaw and Practical Law content. This is just step one in a joint roadmap creating a unified ecosystem for small to mid-sized firms.

A Law Firm as Co-Developer

The partnership with Sterne, Kessler, Goldstein & Fox, one of the leading intellectual property law firms in the United States, takes a different form. Rather than a product integration, this is a co-development relationship in which the firm’s practitioners worked directly alongside Thomson��Reuters��engineers and editorial teams to build a specialized workflow inside��CoCounsel��Legal.��

The focus is Section��101��patent eligibility, a question central to most utility patent disputes, deeply precedent-dependent, and historically requiring significant associate time with no guarantee the research was complete.��

The resulting Patent Claim Eligibility Analyzer inverts the traditional model of legal technology development: instead of practitioners receiving a finished product built by technologists, Sterne Kessler co-developed it from the start, translating the firm’s own Section 101 methodologies into a scalable workflow now available to patent practitioners across��CoCounsel��Legal.��

: Connecting Case Intelligence to Legal��Strategy����

Personal injury cases are won or lost on facts, and facts live in unstructured records that take too long to organize and are too easy to get wrong. Medical records, treatment timelines, damages calculations, liens, deposition summaries: plaintiff firms are managing all of it, under pressure, against better-resourced opponents.��

Supio Agent changes the equation at every level. At the case level, it ingests every file, every system, every data source the firm runs on and executes the work: medical chronologies, damages summaries, causation analysis, and case-specific drafts grounded in verified facts. At the portfolio level, it surfaces what’s urgent, what’s behind, and what needs to move. At the firm level, it gives leadership real-time visibility into case quality, intake patterns, and institutional knowledge that previously lived only in attorneys’ heads.��

Deep Research with Westlaw Advantage is available natively inside��Supio��Agent, invoked when legal research is needed, running in parallel, and surfacing findings directly in the workflow.��

The result:��Supio��builds the record, compounds the knowledge, and works with Westlaw to verify the law, delivering defensible work product at a scale no case-level tool can touch.��

A Growing Partner Ecosystem��

Thanks to �������� partner Anita, German legal research content is now part of the CoCounsel Legal experience, providing authoritative guidance for Germany-based and international teams. A recent document connector integration with practice management solution Litify creates a fast track from matter files to substantive legal work product. ��

Document storage integrations launched early this year with Box and Dropbox extend that reach further, making it easier for firms to work with their documents wherever they live. And with Outlook search now enabled within CoCounsel Legal, users can simultaneously surface and apply relevant information from their email, Westlaw, Practical Law, and other connected sources, all without leaving the platform.��

The��practice��of law looks different depending on where you sit, from solo and small firm practitioners managing tight margins and lean teams, to large global firms and corporations navigating complex,��high-stakes��matters. The��partner��ecosystem is built to serve that full spectrum, meeting legal professionals where they work with the connected intelligence and seamless��workflows��their practice demands.��

As the��legal��profession’s needs evolve, so��will��the��partner��ecosystem.��For lawyers, that means more capability, more connectivity, and more of the intelligence they need,��wherever their work happens.��

Learn more about CoCounsel Legal

Earlier this month, I watched that shift play out on two stages. First, at Snowflake Summit, I talked about the data foundation powering enterprise AI. Then, at Fortune Brainstorm Tech, I sat with tech-forward leaders from Salesforce, Amgen, Optum, SentinelOne, TrustGuard, and May Mobility, debating where these systems break when the stakes are real.��

Different rooms, different framings. Same conversation.��

The AI market is starting to agree trust is what separates AI that demos well from AI that survives contact with production. Traceable, governed, explainable: the words are everywhere. Trust has become the thing we all nod along to.

But we keep talking about it as a feature. Something you bolt on once the model works. A checkpoint before launch.

And there was one thing few people were willing to say out loud:

Trust is not a feature. It is a bill. And much of the industry is about to discover it never paid for it.

The tax nobody put on the slide

The part that often goes unsaid in the keynotes is that the governed data foundation that makes an AI system defensible takes years to build, costs real money, and produces almost nothing you can put on stage while you are building it. There is no demo for “we governed every layer underneath, so the same question never returns two different answers.” Nobody throws a launch event for a semantic layer.

So many companies did not build one. The last few years rewarded the opposite instinct, the one the consumer internet was built on: ship it, watch what breaks, patch it in the next release. Fail fast.��

That reflex produced extraordinary products. It is also precisely the wrong reflex to carry into law, tax, audit, and compliance, where the first failure is not a lesson. It is a brief filed with a citation that does not exist, in front of a judge.

At Snowflake Summit, my colleague Laura Safdie captured it well: you cannot bring AI to a profession you do not understand or transform a workflow you have never done.

For a consumer chatbot, a model that is fluent without being informed may be a tolerable trade. For the lawyer defending an antitrust case, the tax professional signing a multinational filing, or the compliance officer clearing a transaction, “mostly right” is not a trade-off. It is malpractice. And “the AI did it” has never worked as a defense in a courtroom.

The companies sprinting into agentic AI on top of fragmented data and governance bolted on at the end are not necessarily moving faster than everyone else. They are running up a debt that comes due the first time someone asks them to explain an output and they cannot.

The quiet work is about to pay off

In Aspen, in a room full of hands-on technology leaders, one idea kept coming through clearly: the quiet work is about to pay off.

The model is almost never the thing that breaks first in production. Ownership breaks first. Auditability breaks first. When nobody has defined who owns the output or what happens when it is wrong, the model turns out to be the easy part.

Everyone has access to good models now. The layer underneath is the hard part, and it cannot be acquired in a quarter.

At ��������, we made that investment before it was fashionable. The work may be unglamorous, but the results are exactly what enterprise AI now requires: a governed semantic model that turned five conflicting answers from five teams into a single source of truth; complex financial analysis that used to take weeks, now completed in seconds; data refresh cycles reduced from roughly a day to near real time; and AI agents that can explore data, surface patterns, and generate analysis because the foundation underneath them is trusted enough to reason on.

Just as important, every AI capability is assessed across regulatory, privacy, security, ethics, and performance risk before it ships. ISO 42001 certification is treated as a continuous discipline, not a press release. And our trusted data estate across tens of thousands of governed tables is why we could show up as a customer with proof, not just intent.

That is the quiet work. And it is the reason we can put AI into the highest-stakes professional work there is and stand behind the answer because it is traceable and auditable.

The next phase rewards a different kind of company

One line from a fellow panelist, Elena Kvochko of TrustGuard, stuck with me: you do not let AI grade its own work. As agents take on more, the volume of output to verify grows faster than any human team can check by hand, which means oversight must be engineered into the system, not promised in a policy after the fact.

That is the real shift underway: from AI that helps with a task to AI that completes an entire workflow inside an environment it can be trusted to operate in. Give it an objective, and it can plan, research, validate, and coordinate across the whole thing, not just answer a question.

The agent who can navigate proprietary knowledge, permissions, and genuine accountability is worth far more than the that writes a confident paragraph.

Enterprises do not run on models. They run on trusted systems. And the layer where work actually gets completed is where trust and accountability live.

So here is the prediction the demos are hiding: the next two years will not be won by whoever has the most impressive model. They will be won by whoever quietly paid the bill: traceability, security, governance, and explainability.

We paid that bill early, on purpose, because the highest-stakes professional work demands it. That is not where the work ends. It is where trusted AI begins.

General-purpose AI is advancing quickly and delivering real value. It will continue to augment the operational layer: workflow automation, routine document processing, and open-ended brainstorming. Then there is the work that must be cited, audited, and defended. Research that shapes case strategy. Regulatory submissions. Transactions where a partner puts their name on the closing certificate. This is the work that demands something general-purpose AI was not designed for and can’t reliably deliver through prompting alone. The AI to support high-stakes work needs authoritative grounding, citation integrity, and professional governance built into the architecture from the first step, not added at the end. It needs depth.

Those two categories complement each other, but they are separating in terms of their roles in the stack. And the question for every professional organization is no longer whether to adopt AI; it is which kind the work in front of them requires. Legal is where that question is sharpest, and where we are answering it first. But the same divide is opening across tax, accounting, audit, and risk, wherever professionals are expected to produce work that can withstand scrutiny. That is the work we built for, and today the is here for early access.

The profession has already reached this conclusion

Our 2026 Future of Professionals research, drawing on more than 1,800 legal and tax professionals across 62 countries, makes the shift plain. Most now use AI several times a week. The open question is no longer adoption. It is trust. Asked what makes AI accountable to professional standards, 95% pointed to safeguarding confidential data, 94% to outputs grounded in verified content rather than the open internet, and 87% to work built on human expertise that can be explained and defended.

The stakes are no longer hypothetical. Courts have sanctioned lawyers for filings built on AI-generated citations that did not exist and have been clear that responsibility sits with the professional, not the tool. Adoption is accelerating anyway. That combination is exactly why the category of AI now matters as much as the capability of AI.

Agentic AI is not a counting exercise

The word agentic is everywhere, usually used to count things: how many tools a system can call, how many agents it can name. Those capabilities matter, but the number of agents is the wrong measure. We built the next generation of CoCounsel Legal on the principles that make agentic systems genuinely powerful, the same ones that make coding agents work: reason through the problem in testable steps, call the right tools as the work requires, adapt when new information changes the analysis, and keep the reasoning traceable enough to verify. Agentic capability is not about enumerating and building a deterministic set of skills; it is about building an intelligent system that is adaptable while being rooted in the tools and information that underpin the business and the industry.

What makes that reasoning trustworthy is where the knowledge sits. There is a meaningful difference between a system that calls Westlaw at the end of a task to check citations, and one where Westlaw and Practical Law are wired into how the agent reasons from the first step, content that took �������� 175 years to assemble. The first generates and then checks. The second reasons against authoritative knowledge throughout. The outputs are not slightly different; they are structurally different. CoCounsel Legal is the second model. The attorney does not approve the work on faith. They inspect it. Building professional AI requires more than integrating frontier models. It also requires specialized models built for the work itself.

We are strengthening this from underneath, too. CoCounsel Legal is and will remain multi-model by design. Alongside the frontier providers we work with, we are building our own: Thomson, our vertically specialized foundation model, built for depth and accountability rather than breadth. It ships later this year, first in Tabular Analysis inside CoCounsel Legal, where extracting structured answers across large document sets rewards precision a general model handles inconsistently.

A standard you cannot measure is just a claim

If professional AI is moving from answers to defensible work product, evaluation has to move with it.

That is why we built CoCoBench, our lawyer-led evaluation framework: more than 1,000 tasks written by practicing attorneys and scored against gold-standard answers also written by attorneys, from Big Law, government, in-house, and boutiques. It does not ask whether an output sounds like legal analysis. It asks whether the reasoning is right for the jurisdiction, whether the correct framework was applied to the facts, and whether an attorney could defend the result under scrutiny.

Measured against representative legal tasks reviewed by licensed attorneys, CoCoBench shows what professional-grade evaluation requires. These are not abstract benchmark questions. They are legal tasks, informed by practice experience, held to the standard of work a professional would need to review, rely on, and defend. That bar gets set when verified content, agentic reasoning, expert evaluation, and deep workflow integration work as one system.

Built for where work begins, wherever that is

Professional work does not start in one place. It begins in research platforms, drafting tools, document systems, and increasingly in general-purpose AI assistants. So we are not trying to be every interface a professional touches. Through APIs, connectors, Model Context Protocol support, and partner integrations across the AI and , CoCounsel Legal can be reached from the tools customers already use. When a lawyer hands off a task through MCP, they are not querying a content database. They are handing it to a system where authoritative content is wired into the reasoning, and the result can be inspected and defended. The general-purpose model starts the work. CoCounsel Legal finishes it to a standard that holds up.

This is what we mean when we describe CoCounsel evolving into an agentic operating system for professional work. Not a chatbot, not a list of agents, not a collection of plugins, but a trusted system that brings content, context, tools, models, verification, and human oversight together to move work to completion, while keeping people in the parts that matter most: judgment, strategy, review, and accountability.

Professionals will adopt AI when they trust it more than they fear it. That trust will not come from speed alone. It will come from systems that help them finish the work correctly and prove they did.

The future of professional AI will not be defined by who generates the most answers. It will be defined by who can help professionals produce work they can defend. That is the standard we believe matters, and the standard we are building toward.

That is what we built. The next phase of professional AI is here.

Read more:

For our��customers,��flexibility is powerful. Work can begin in��the��environment that is most natural for the task at hand. But it also creates a critical challenge: as work moves across more tools, models, partners, and interfaces, how do professionals��keep track of what happened and why?����The��final output��must��be��accurate, transparent, secure, and defensible.��As AI moves from answering questions to planning, acting, and coordinating work across tools, the system behind the interface matters more than ever.

That question is becoming more urgent as AI changes the speed and scale of professional work. The gap between what is plausible and what is correct, between what is fast and what can be trusted, is widening. In legal, tax, audit, and compliance, that gap is not theoretical. It creates real consequences for clients, organizations, markets, and reputations.

At ��������, we call the standard��required��for this work Fiduciary-Grade AI:��AI designed for professionals whose work��is subject to regulatory oversight��and duties��of care��and where��accuracy,��confidentiality, and��accountability��are critical. In the agentic era, that standard becomes even more important because AI is no longer only generating answers. It is beginning to��help��plan work, use tools, coordinate steps, and help complete complex tasks.��And it is increasingly dependent on many interconnected AI technologies, models, and agents working together to produce a final work product.

That is the standard we are building CoCounsel��to,��as it evolves into an��agentic��operating system��for��the professions we serve.

CoCounsel is designed to meet professionals wherever high-stakes work happens. Often, that means starting and completing work directly in CoCounsel. Increasingly, it also means making CoCounsel accessible across the broader ecosystem of tools, models, platforms, and partners our customers already use.

The goal is simple: put��Fiduciary-Grade��AI��at the fingertips of our customers, wherever professional work begins, and ensure that when the work matters, it can be trusted.��That is what we mean by an��agentic��operating system: not a single agent, chatbot, or workflow tool, but a trusted system that can bring together content, context, tools, models, verification, and human oversight to help professionals move work forward.

Starting Work Is Easy. Finishing It Is What Matters.

AI has made it easier than ever to begin work. A prompt can generate a draft, summarize a document, surface��a possible answer, or move a task forward in seconds.

But in professional environments, starting work is not the hard part. Finishing it��accurately��is.

This need is especially visible in legal, where professionals must ground their work in trusted sources and stand behind the results.��When work carries consequences, when it needs to stand up in a boardroom, before a regulator, in an audit, or in court, what matters is how that work is grounded, verified,��validated, and made defensible.

That is the role CoCounsel is designed to play.

Trust in professional AI cannot be treated as a feature added at the end.��It has to be built into the system itself.��Outputs must be grounded in authoritative, continuously maintained sources. They must be shaped by domain��expertise. They must include mechanisms for validation and transparency, so professionals can understand, verify, and stand behind the results.

This is what separates general-purpose AI from��Fiduciary-Grade��AI.

General-purpose AI can be extraordinarily useful for exploration, brainstorming, summarization, and productivity. But high-stakes��professional work requires more than a plausible answer. It requires systems that understand the domain, apply trusted sources, respect privacy and security obligations, and support the human accountability that��remains��at the center of professional judgment.

We are seeing this first in legal. CoCounsel Legal combines advanced AI with �������� trusted legal content, workflows, and��expertise, including Westlaw, Practical��Law��and��KeyCite,��which can be applied to��customers’ own documents and work��product. It is designed not only to help legal professionals move faster, but to help ensure their work can be trusted when it matters most.

As Elizabeth F. Salsedo-Surovov, Director of Knowledge Management and Information Resources at Robinson & Cole LLP, recently said:��“We’re really excited to be using [CoCounsel Legal] Deep Research… we are using it in Westlaw through our partnership with ��������, in our��on-premise��AI environment to pull in the Deep Research reports so that people are not doing research in the��ChatGPTs��of the world, they’re doing research in Westlaw. …��Utilize��the trusted��resource��and tools of Westlaw to do your legal research.��Don’t��rely on other AI tools.”

That customer perspective captures the shift underway. Professionals are not just asking for faster AI. They are asking for AI they can rely on.

Building Across the Customer’s Ecosystem

The next generation of CoCounsel is being built for that reality.

CoCounsel will continue to be a primary destination for professional work. For many customers and many workflows, the best experience will begin inside CoCounsel itself, where �������� can bring together content, workflow, context, AI, and various 3rd��party applications and agents��in one trusted environment.

But professional work also starts in many other places. It starts��in��documents, emails, enterprise systems, matter management tools, tax workflows, accounting platforms, customer environments, and increasingly general-purpose AI assistants.����That is why we are building CoCounsel to��operate��across a broader ecosystem as well.

Through APIs, connectors, Model Context Protocol capabilities, and emerging interoperability standards, CoCounsel can be integrated with and invoked from the environments where customers are already working. In legal, we are beginning to��demonstrate��this through our work with Anthropic and Claude, enabling customers to connect Claude with CoCounsel Legal so that work can move between a general-purpose AI environment and �������� trusted legal AI system.

And this is just one part of the broader strategy.

We are engaging across the AI and enterprise technology ecosystem,��with Google, Microsoft, OpenAI, AWS, Anthropic, and other partners, to make CoCounsel available where our customers choose to work.��Through MCP and other integrations, CoCounsel can be accessed from the environments where customers are already working.

This is not about turning �������� into a passive content layer for someone else’s interface. It is about making CoCounsel available as the trusted professional AI system that grounds,��validates, and completes high-stakes work, regardless of where that work begins.

The same strategy extends beyond��legal. As CoCounsel continues to expand across ��������, including tax, accounting, audit, risk, and compliance, we are building toward a future where fiduciary-grade AI is available across the professional workflows our customers rely on every day.

What Comes Next

AI is raising expectations for speed and scale across every professional industry. But as the volume of AI-assisted work increases, so does the pressure on accuracy, judgment, and accountability.

The systems that matter most will not be the ones that simply help professionals start��work��faster. They will be the ones professionals can rely on to finish work correctly.

That is the future we are building��toward with��CoCounsel.

We will continue to make CoCounsel a best-in-class destination for professional work. And we will continue to extend it across the ecosystems where our customers are already working, across models, platforms, partners, and workflows.

Because the future of professional AI will not be defined only by who can generate the fastest answer.

It will be defined by which systems professionals can verify, trust, and stand behind.

That is the fiduciary-grade standard we are building into CoCounsel across ��������.��