Agentic AI Grounded in Deep Legal Expertise

The next generation of CoCounsel Legal (US)

The next generation of CoCounsel Legal has launched in the U.S. A complete rebuild of CoCounsel Legal, it鈥檚 engineered to work at the level of a senior associate, reasoning through legal issues the way an attorney does and grounded in authoritative Westlaw primary law, trusted Practical Law guidance, and your firm鈥檚 own knowledge.听Describe a matter in plain language, whether听it’s听contract review, litigation strategy, deal structuring, or sifting thousands of discovery documents, and CoCounsel Legal handles the rest.听It develops the approach, works through each step, and links every citation so you can verify it yourself.听听You can give each matter its own workspace, keeping documents, templates, and conversations together. 听听It also meets you in the tools you already use, including Microsoft 365 and Claude. Your data always听stays听your firm’s 鈥� private, and never used to train models.听This is Fiduciary-Grade AI your firm can stake its reputation on.

Learn more at

Westlaw Brief Builder (US)

A transformation of research and drafting, Westlaw Brief Builder helps litigators move from research and issue analysis to first-draft brief creation while validating authority along the way. It follows a workflow purpose-built for briefs, guiding the drafter through each stage of the process in a way that litigators actually think and work 鈥� an organized, connected experience developed and validated by attorneys. Powered by Westlaw Deep Research, KeyCite, and Practical Law 鈥� authority no other legal AI can match 鈥� it proposes relevant facts, arguments, and supporting authority while keeping lawyers firmly in control of strategy, legal theory, and final decisions. And because output is verified against the law and the facts, then surfaced for review and confirmation, Westlaw Brief Builder delivers briefs not only faster, but briefs you can stand behind.

Global Connected Platform

Firm Document Management Connectors via Syncly (US, UK & Canada)

Admins can now connect CoCounsel Legal directly to a firm’s document management system through new Syncly connectors, including iManage on-prem, iManage Cloud, and SharePoint. Once connected, CoCounsel can access and act on firm content directly, with a connector icon appearing for end users wherever a connection is active 鈥� simplifying the ability to bring trusted firm documents into AI-assisted research, analysis, and drafting without leaving the platform.

Expanded CoCounsel Legal MCP with Claude (US)

CoCounsel Legal’s MCP connection with Claude is expanding. What started with Deep Research now extends to research, analysis, and drafting, bringing more of CoCounsel Legal’s fiduciary-grade AI capabilities directly into Claude. Lawyers can describe a matter in plain language inside Claude, and CoCounsel Legal plans the work 鈥� reasoning from authoritative Westlaw primary law, trusted Practical Law guidance, and a firm’s own knowledge 鈥� and returns cited, traceable work product without leaving Claude.Tabular Analysis Enhancements in CoCounsel Legal (US)

Tabular Analysis is evolving. In the newly launched version of CoCounsel Legal, bulk document review is now built directly into the workspace 鈥� no more exporting files or rebuilding a separate database to review a subset. Creating and editing tables is simpler too, with a redesigned experience built from direct customer feedback, and multiple tables can now share the same set of files instead of duplicating work for each one. Tabular Analysis also runs by default on Thomson, 成人VR视频 own purpose-built legal AI model 鈥� the organizational and technical foundation for a more connected CoCounsel Legal.

Built for How You Work

Verbatim Extraction in Tabular Analysis (US, UK & Canada)

Attorneys building disclosure schedules, privilege logs, and red-flag memos need exact clause language, not paraphrases. A new Verbatim column type in Tabular Analysis returns exact source text instead of AI-generated summaries, so attorneys no longer need to reopen the original document to re-quote a clause. Users select 鈥淰erbatim鈥� when configuring a column and describe what to pull; CoCounsel returns the exact language from the source, validated against the document, and clicking any cell opens the source with the precise span highlighted for verification. Exports carry the verbatim text into Word and Excel with quotation marks already applied, ready for partner review and closing binders.

Judicial Experience on CoCounsel (US)

A new, personalized CoCounsel environment is now available for trial, appellate, and supreme courts. When judges or clerks activate the Judicial setting, the application adapts to their role with a neutral-stance default and a suite of curated judicial workflows, including pro se brief standardization, statute of limitations checks, cross-brief issue synthesis, and case fact drafting 鈥� every output grounded in trusted Westlaw and Practical Law content, with hyperlinked citations and KeyCite verification for auditable, defensible decisions.

Thomson 鈥� New Proprietary LLM (US, Canada & UK)

Thomson, the first proprietary large language model built by 成人VR视频 draws on decades of proprietary content from Westlaw, Practical Law, Checkpoint, and Reuters plus hundreds of subject matter experts to reach Fiduciary-Grade standards at a fraction of typical cost. Early evaluations, including external testing by legal and AI academics, put Thomson on par with leading frontier models, particularly in instruction-following and domain-specific legal reasoning, and the model is now being deployed first in Tabular Analysis within CoCounsel Legal, with plans to extend Thomson across the legal and tax portfolio alongside additional sovereign AI options.

Learn about the Thomson LLM .

Deep Research Verify in Practical Law Premium (UK)

Deep Research Verify is now available in Practical Law Premium, integrated directly into the existing Deep Research workflow. Verify lets users review specific AI-generated legal assertions against relevant supporting passages from underlying Westlaw UK and Practical Law sources, with source support displayed inline alongside the original report. By reducing manual cross-referencing and providing a direct route to further research, Verify helps legal professionals assess AI-generated research more efficiently, transparently, and confidently.

Deep Research Verify in CoCounsel (UK)

CoCounsel UK now offers the ability to verify a Deep Research report directly within the platform. Deep Research Verify checks a Deep Research report against Practical Law and Westlaw UK鈥檚 authoritative sources, confirming citations are accurate rather than hallucinated and surfacing additional relevant law the report may have missed 鈥� moving users through verification faster and giving them more links to explore for further research.

Westlaw Advantage Canada 鈥� Parallel Search (Canada)

Parallel Search is a new feature in Westlaw Advantage Canada that finds cases with facts or issues similar to a scenario described in plain language 鈥� not just documents matching the words in a query. Users describe a fact pattern or issue in a sentence, and Parallel Search surfaces up to 25 Canadian cases with analogous facts or reasoning, helping researchers find true comparators and surfacing analogous outcomes they might otherwise miss, especially on novel or hard-to-phrase issues.

Deep Research Verify in Westlaw Advantage Canada and CoCounsel Legal (Canada)

Deep Research Verify is now available within the Deep Research experience on Westlaw Advantage Canada and CoCounsel Legal. For each assertion in a Deep Research report, Verify presents supporting text from the cited source and flags where support may be lacking, with a clear path for additional manual verification 鈥� helping users review AI-generated research with greater confidence and less manual cross-referencing.

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 .

The phenomenon is known as shadow AI, and according to the 成人VR视频 Future of Professionals Report 2026, it鈥檚 currently occurring among more than one-third (36%) of professionals working in corporate tax, legal, and compliance functions who admit they are actively using AI tools their organization have not sanctioned. For businesses at the center of this issue, the risk of information leakage, inaccuracies, cut corners, and a collapse of standardized processes could create dangerous ripple effects.

Pressure to Move Faster

On the surface, the findings should not come as a huge surprise. AI is everywhere these days, and it鈥檚 already become second nature for many of us to turn to widely available consumer chatbots for guidance on everything from dinner recipes to exercise tips. On top of that, many corporate professionals are facing increased pressure from internal stakeholders and clients to deliver faster, better-informed decisions with better efficiency and cost controls. According to our research, 58% of corporate tax and legal professionals say they’re facing “some” or “significant” pressure from their key stakeholders to move faster on AI adoption.

The disconnect occurs when tools designed for consumer-grade tasks are applied to professional-grade work, which often contains proprietary or sensitive information that should not be shared on external servers, or highly specialized data that consumer large language models (LLMs) were never meant to process. Still, despite the obvious risks associated with using unsanctioned AI tools for high-stakes professional work, many companies are just not moving fast enough on AI adoption and, as a result, employees are taking matters into their own hands.

Understanding the Risks

For many, it鈥檚 a survival instinct. In fact, 15% of professionals in corporate enabling functions say they are already seeing financial consequences of insufficient progress on AI adoption by their companies, and another 29% say they expect them within 12 months. Pressed to continually find ways to do more with less, inundated with news about new AI tools that can do seemingly anything, and drawn-in by the allure of freely available and amazingly powerful consumer tools, it鈥檚 no surprise that many corporate tax, legal and compliance professionals would start experimenting.

The downsides of that trend are already starting to become , and can include everything from lapses in corporate governance to over-reliance on incorrect or incomplete information 鈥� not to mention a lack of standardization whereby each individual employee starts using their own tool.

A New Focus on Collaboration

To address these issues, corporate enabling functions must start to make a clear case to business leadership for why they need professional-grade AI solutions. The fact is that as the AI ecosystem matures, solutions developed for professional grade tasks like corporate tax, law, compliance, and others are becoming highly specialized. These are not the mainstream, consumer-grade chatbots; they are finely tuned pieces of professional software developed for highly specific use cases. Senior leadership may have big-picture AI mandate, but they may not necessarily understand the need for specialized tools. Only the teams in the trenches can deliver that perspective, and these teams need to get a seat at the table where they can advocate for themselves.

It鈥檚 also high time for most corporate tax, legal and compliance professionals to start taking an honest look at what kinds of tools their teams are currently using 鈥� both sanctioned and unsanctioned 鈥� to determine where are AI tools already being used, where are people improvising, and where is there unmet demand.

Functions working in isolation on AI strategy are creating shared risk: inconsistent accountability, incompatible governance, and shadow AI that nobody owns. Fiduciary functions like legal, tax, and compliance hold the professional standards that should anchor the enterprise鈥檚 AI governance. While, currently, the C-suite, technology and operations teams hold disproportionate sway over AI budgets and implementation, a broader conversation is needed. The general counsel, Chief Compliance Officer and corporate tax leaders are particularly well positioned to lead this conversation 鈥� with an emphasis on what鈥檚 at stake if companies get it wrong. The time to start having that conversation is now.

About the author
Liz Zimick is President, Corporates, at 成人VR视频

For litigators, that matters because some of the most important context in a matter does not begin in a research or drafting tool. It begins in the evidence.听

Documents. Testimony. Facts developed over the course of discovery. The record a lawyer ultimately has to connect to the law and turn into analysis, strategy, and work product they can stand behind. AI should make that process easier.听

This week at ILTACON, 成人VR视频 and Everlaw announced plans to integrate Everlaw with CoCounsel Legal. The first planned integration will allow mutual customers to bring Everlaw documents into CoCounsel Legal in bulk, making it easier to use litigation and investigation materials across CoCounsel Legal鈥檚 research, analysis, drafting, and workflow capabilities.听

For customers, that means less friction between reviewing evidence and doing the legal work that follows.听

For us, it is also one more piece of the broader CoCounsel Legal ecosystem we are building: connecting the systems, content, and tools professionals already rely on so they can move through complex legal work with more continuity and less unnecessary handoff.听

Everlaw brings an important part of that ecosystem into the picture for litigation: the evidentiary record.

Connecting evidence, context, and legal analysis听

A litigator rarely asks a purely abstract legal question. The real question is how the facts, evidence, and applicable law come together: whether a particular document changes the argument, whether testimony is consistent with the rest of the record, or whether the evidence supports the position being put before a court. Carrying that context through the legal workflow is essential to making AI genuinely useful in litigation.听

CoCounsel Legal brings together research, analysis, drafting, and other legal workflows with authoritative content from Westlaw and Practical Law. But in litigation, authoritative legal information is only one part of the picture. The evidentiary record matters just as much.听

Bringing those worlds closer together can make AI much more useful in the day-to-day practice of law.听

By reducing the need to manually move documents between systems, we can help lawyers spend less time on those handoffs and get to the substantive legal work faster.听

Moving from evidence to analysis with less friction听

Consider what happens when a litigation team identifies a set of important documents during discovery.听

Those materials may already have been collected, reviewed, organized, and understood within an eDiscovery platform. But when the team moves into other parts of the legal workflow, that context does not always move with them.听

That can mean exporting documents, uploading them into another environment, and reconstructing parts of the matter before the lawyer can move forward. Every handoff creates friction.听

The planned Everlaw integration is intended to shorten that path. Once Everlaw documents are available in CoCounsel Legal, lawyers can apply CoCounsel Legal鈥檚 research, analysis, drafting, and workflow capabilities to those materials, creating a more direct connection between the evidentiary record and the legal work that follows.听

It is also exactly the kind of connection we want to keep adding across the CoCounsel Legal ecosystem: bringing more of the places where legal work already happens into a workflow where professionals can research, analyze, draft, and act with the right context around them.听

Connected AI still has to meet the standard of legal work听

Making more information available to AI cannot mean lowering the standard for what comes out. The lawyer is still responsible for the argument. Still responsible for the citation. Still responsible for understanding whether the evidence supports the conclusion.听

That is why verification, transparency, and professional judgment matter so much. At 成人VR视频, we describe this standard as Fiduciary-Grade AI: AI designed for high-stakes professional work, grounded in authoritative information and built so professionals can review, verify, and ultimately stand behind the work it helps produce.听

Connecting more of the matter into that workflow should strengthen the lawyer鈥檚 ability to exercise judgment, not remove the lawyer from the process.听

Building a broader ecosystem around legal work听

Law firms and legal departments have invested in specialized technology for a reason.听

Evidence may live in an eDiscovery platform. Documents may live in a document management system. Legal research comes from trusted legal sources. Work product may move through several systems before it is complete. AI is not going to make that ecosystem disappear. The opportunity is to make it work together better.听

That is the direction we are taking with CoCounsel Legal. We are building an ecosystem designed to connect the tools, content, and workflows professionals already depend on, while preserving the context, permissions, controls, and trust required for high-stakes work.听

Everlaw is an important addition to that ecosystem because it brings the evidentiary record closer to the research, analysis, and drafting lawyers are already doing in CoCounsel Legal.听

And it is one part of a much broader direction.听

As we continue expanding the CoCounsel Legal ecosystem, we want professionals to be able to bring more of their work, their context, and the systems they trust into a more connected AI experience.听

For customers, that can mean fewer unnecessary handoffs and a more direct path from evidence to insight and work product.听

For the industry, it is another signal of where legal AI is headed. The next generation of legal AI will not just be more capable. It will be built to connect the work around it.听

成人VR视频 is working with Google Cloud to connect HighQ with Gemini Enterprise for Legal, giving legal professionals a secure way to bring authorized matter documents, structured data, and workflows into the AI environment where they are collaborating.

The HighQ MCP connection is now live alongside Google Cloud’s announcement of Gemini Enterprise for Legal.

This is an important milestone, but it is also exactly that: one milestone. HighQ is the natural place to begin because it contains the live matter context AI needs to be useful. Over time, we see opportunities to extend more of the trusted capabilities of CoCounsel Legal across the AI environments our customers choose to use.

Why matter context matters

Enterprise AI continues to improve, but legal work has always depended on context. That context is more than documents. It includes structured matter data, workflows, tasks, templates, collaboration history, deadlines, permissions, and the institutional knowledge surrounding a case or transaction. Much of that lives in HighQ.

Law firms and corporate legal departments rely on HighQ to manage matters, collaborate internally and externally with clients and outside counsel, power secure client portals, and organize work that evolves over weeks, months, or years. Connecting HighQ with Gemini Enterprise for Legal allows authorized users to securely access that context without exporting files, copying information between systems, or recreating it somewhere else.

A legal team could ask Gemini Enterprise for Legal to summarize documents in an authorized matter, identify upcoming deadlines from an iSheet, compare structured deal data with underlying agreements, or bring relevant matter information into a draft.

The value is not simply giving AI access to more information. It is giving AI access to the right information, with the governance legal organizations already depend on.

Governance isn’t optional

Legal organizations should not have to choose between adopting new AI capabilities and maintaining the controls their work requires.

The HighQ MCP connection is designed so organizations don’t have to make that tradeoff. Content remains in HighQ. Once a customer securely connects HighQ, users authenticate using their existing HighQ credentials, and existing matter walls; folder permissions, document-level security, and audit logging continue to apply. Gemini Enterprise for Legal can retrieve only the content an individual user is already authorized to access. The connection is read-only, allowing AI to retrieve authorized information without changing the underlying content.

That means organizations can confidently extend trusted matter context into Gemini Enterprise for Legal while keeping governance exactly where it belongs.

Extending CoCounsel Legal into the enterprise AI ecosystem

This announcement is about more than connecting two products.

CoCounsel Legal remains the trusted platform where legal professionals perform complex legal work. It brings together authoritative legal content, customer context, agentic workflows, verification, and human oversight to help professionals produce work they can verify and stand behind.

At the same time, legal teams increasingly collaborate with business colleagues, outside counsel, customers, and partners who may be working in different AI environments.

Open standards like MCP make it possible to extend trusted legal context and capabilities into those environments without requiring organizations to move their work out of CoCounsel Legal or compromise on governance.

Today’s HighQ integration is the first exciting step in that broader vision with Google Cloud.

Building an open ecosystem for trusted legal AI

The future of enterprise AI will not be defined by a single model or a single interface.

Organizations will continue using different AI environments for different types of work. Our role is to ensure that wherever legal professionals collaborate, they can securely access the trusted matter context, authoritative content, and professional capabilities their work requires.

That is why 成人VR视频 continues to invest in MCPs, APIs, agentic systems, and strategic partnerships across the AI ecosystem.

“Customers have told us they want our AI solutions to connect seamlessly with the trusted systems they already rely on for everyday legal work,” said Satish Thomas, Vice President, Google Cloud. “Working together with 成人VR视频, we鈥檙e making it easy to bring critical matter context directly into their workflow today, while creating a path toward even deeper integrations over time.”

The launch of Gemini Enterprise for Legal and the HighQ MCP connection demonstrates what’s possible when trusted matter context and enterprise AI are designed to work together. It’s an important step in our collaboration with Google Cloud and another step toward making trusted legal AI available wherever professionals need it, while keeping CoCounsel Legal at the center of how that work gets done.

Thomson began as an internal project, built to solve a problem we had ourselves.听

成人VR视频 holds 175 years of authoritative data across legal, news, tax and accounting: Westlaw, Practical Law, Checkpoint and Reuters. We also employ thousands of subject-matter experts whose working lives are spent deciding what is correct. For three years we watched general-purpose models improve rapidly while both assets sat outside the training loop. We also faced the questions our customers were asking us: what dependency are we accepting on someone else鈥檚 architecture and pricing, and what do we do when the capability we need most is on nobody鈥檚 roadmap?听

Our answer to that was the Thomson LLM, and it worked well enough that we now want to share it with the rest of the world wrestling with these same questions.听

Where the argument came from听

The team that built Thomson did not arrive at 成人VR视频 with just a view about legal AI, but with a view about reliability.听

Safe Sign Technologies was founded in 2022 by lawyers and researchers whose background was in model safety, robustness and reliability, several coming out of applied AI in medicine and law, from Harvard and Cambridge. Medicine and law share a property most application domains do not: being nearly right is still wrong, and the cost of a confident error is borne by someone other than the person who made it. Both have long and demanding traditions of rigorous verification as a result, and those shaped how we approached the problem.听

The argument we made from that starting point was, at the time and until recently, unfashionable. In 2022 and 2023 the field was watching capability curves. The consensus was that frontier models would absorb professional work as a by-product of getting cleverer and that any attempt to keep pace with the 鈥渟caling laws鈥� of AI was futile. On that view the sensible move for a small company was to build a layer on top and wait.听

We believed the binding constraint was different. Capability, we argued, would become abundant; it was the object of enormous and well-funded competition, and there was no reason to expect it to stay scarce. What would remain scarce was trust and reliability: being right in a way that can be checked, in a domain where someone whose career depends on it. Trust is not a by-product of capability. It is a separate research problem requiring different evidence, and nobody was going to solve it for law as a side effect of solving it for everything.听

Very few people agreed. Making that case repeatedly, to investors and to ourselves, through pivots and long stretches with nothing to point at, was most of the job.听

成人VR视频 acquired Safe Sign in August 2024, in the company鈥檚 first pre-revenue acquisition. The team became 成人VR视频鈥� Foundational Research team, and crucially, the research posture that pre-dated the acquisition survived the transition. We continued to treat the work as a research problem rather than solely a product problem, which is why so much of the effort below went into measurement.听

Starting from open weights听

I said previously that our starting hypothesis was that capability would be abundant. The rate of progress of open-source AI has continued to prove this thesis over the last several years. Thomson benefits from this directly, with a leading open-weight foundation model as its starting point. We鈥檝e changed the root model of Thomson many times over the last several years, and will continue to do so as the frontier of open-weight models evolves. 听This is a tide that Thomson moves with, not one that washes it away. 听听

At the time of writing, the base model for Thomson is the Imperial College London Snowdon model. This model was developed by the FAIR Lab at Imperial, which 成人VR视频 and Imperial founded jointly, as an academic by-product of the acquisition of Safe Sign. 听

That choice is usually framed as a trade-off, and there is something to it: open-weight models can lag the closed frontier, and published analyses generally put that lag at a few months [1]. The conventional choice is, therefore, between capability and control.听

We did not think this was an acceptable dilemma for professional work. The frontier is measured on general capability, but our customers are judged on something narrower: whether a citation holds up, whether an answer is complete, whether the reasoning survives a partner鈥檚 review. There is no rule that a model strong on the second must concede the first. As we reported at launch, Thomson performs 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. It also leads them on the measure this post is concerned with: whether the citations in a research report survive being checked.听

Turning the archive into training data听

成人VR视频 content is the deepest asset in this field and the reason a model of this kind was possible at all. It is also, as any archive of this scale would be, material that has to be prepared before a model can learn from it well.听

Content has to be found, which in an organisation of this breadth and history is a substantial exercise in itself. It must be assessed for rights, selected for measurable impact on model performance rather than relevance in the abstract cleaned, structured, deduplicated, and finally deployed into a data mixture, which is where the most consequential decisions are made.听

To date we have used less than ten per cent of 成人VR视频 content in continued pre-training. Westlaw, Practical Law, Checkpoint and Reuters News have been drawn on selectively. The areas where the model is not yet best in class are not ceilings we have reached, but areas where the relevant content has not yet been brought to bear.听

The specialisation problem听

Data mixture matters so much because specialising a model can damage it. Fine-tuning on domain-specific data can cause catastrophic forgetting: the model overwrites capabilities acquired during pre-training and its general performance degrades [2]. The effect is well documented, and mitigations exist, including replay of general data and regularisation of parameter updates. None fully solve it.听

One finding matters more than the others for our purposes. Kotha, Springer and Raghunathan鈥檚 听2024 study [3] examining what degrades during domain fine-tuning identified instruction-following as the principal contributor to forgetting: what erodes first is not the model鈥檚 knowledge of the world but its ability to do as it is told. For professional work that is close to a worst case, since real legal work is never only legal reasoning but legal reasoning while adhering to a format, a jurisdiction, a house style, an exclusion, a client鈥檚 standing preference. Output that requires reworking has not saved anyone any time.听

We therefore treated general capability retention as a first-class training objective rather than an acceptable loss. Instruction following is among the capabilities specialisation is most likely to erode, and it is one of the categories in our published benchmark results where Thomson stands up best against the frontier models, scoring 0.914 ahead of Claude Opus 4.8, Gemini 3.1 Pro and GPT-5.5. That is the clearest evidence we have that the model was specialised without being narrowed.听

Where the expertise actually comes from听

Many organisations claim their AI systems are 鈥渢rained with expert input鈥�. The phrase carries little meaning without an answer to the real question: how does a lawyer鈥檚 judgement become a training signal? Experts do not produce training data, but a standard. We have had to work to ensure the collective edge in expertise held by 成人VR视频 domain experts is realised in the quality of our training data. This is how we did it.听

Rubrics at maximum complexity. Partner-level practitioners worked full-time for months constructing evaluation rubrics for the hardest legal research tasks we could specify: the kind of multi-jurisdictional question where a good answer has fifteen necessary components and a plausible-looking one has nine. Each rubric enumerates what a correct response must contain. This is slow, expensive, and cannot be crowdsourced or synthesised.听

Commercial judgement, not only legal judgement. We required lawyers fresh out of commercial practice to ground the training data in what clients actually care about, which is frequently not what a textbook would emphasise. Take an indemnity. In most commercial agreements, it is heavily negotiated and often enforced, and treating it as significant is correct. But in an NDA it is usually neither, and almost never the crux. A model trained only on doctrine cannot tell those situations apart, and one that flags an NDA indemnity as urgently as the confidentiality carve-outs has identified a legal issue and wasted a lawyer鈥檚 attention. Teaching that distinction requires people who have sat on the other side of the negotiation.听

Preference data at scale. Thousands of hours of qualified lawyer time selecting between model outputs against complex criteria. Not 鈥渨hich is better鈥� but which better serves a client with a particular posture, in a particular jurisdiction, at a particular stage of a matter.听

成人VR视频 employs around 1,500 attorney-editors whose day job is producing the analytical content lawyers rely on. The obvious move is to train on their published output. The harder and more valuable move is to capture what happens between the first draft and the published article: the judgement calls, the discarded framings, the reasons a proposition was narrowed, the authority considered and rejected. That intermediate work is where so much expertise lives, and it is almost never written down.听

This problem generalises directly to our customers. A firm鈥檚 advantage is not simply its precedent bank: precedents circulate, deals become public, documents get shared. The advantage is what years of doing the work have built in the minds of its lawyers, who eventually retire or move. Capturing the reasoning rather than the artefact is the same problem, and we have worked on it at scale on our own corpus first.听

Internal deployment as a research instrument. Thomson has been deployed widely inside 成人VR视频, with thousands of domain experts using it on their hardest problems, which gives us failure modes reported by people qualified to diagnose them. Our teams are not incentivised to use Thomson for Thomson鈥檚 sake; if they use it, it is because they have decided it can do something others can鈥檛.听

The consistency problem听

Expertise does not straightforwardly produce consistency. In some respects it produces the opposite: the more experienced the practitioner, the more nuanced their judgement, which is exactly what you want in a partner and exactly what creates noise in a training set. Two excellent lawyers can disagree on a scoring decision not because either is wrong but because each applies a refined intuition the other does not share.听

The literature bears this out. On the LEXam legal reasoning benchmark [4], three legal experts independently scoring the same answers on a ten-point scale reached a quadratic weighted kappa of 0.49, with a mean absolute deviation approaching two points. Work on implicit legal citations [5] reports similar or worse agreement. More troubling, Rehag鈥檚 survey of legal machine learning datasets found they systematically removed all traces of disagreement rather than treating conflicting expert annotations as informative [6].听

Take expert output at face value and train on it, and you teach the model an averaged version of several incompatible standards: vaguely acceptable to everyone rather than correct according to anyone.听

A large share of our effort therefore went into data quality: calibrating annotators against worked examples, measuring agreement continuously and treating drops as signals about the task specification rather than the annotator, and structuring rubrics tightly enough that disagreement surfaces as genuine ambiguity rather than noise. Where it persists, the question is usually contested, which is itself something the model should learn.听

Safety, values and red-teaming听

A dedicated team of lawyers worked on bias, political neutrality and toxic behaviour, with extensive human and automated red-teaming. We treat these as training objectives rather than output filters: a filter catches a bad answer on the way out, an objective changes what the model is disposed to produce.听

Political neutrality deserves particular mention given that Reuters sits inside this company. Realignment towards factuality and pluralism was an explicit part of the training programme rather than a compliance exercise appended to it, and it is measured rather than asserted: Thomson performs strongly against the frontier models on our internal neutrality evaluation, with detail to follow in the technical report. For a company that publishes news as well as legal analysis, that is not peripheral.听

Beyond legal data听

Not all of the training data is legal. We drew on domains rich in explicit chain-of-thought reasoning, where the reasoning must be set out rather than left implicit, on the view that a model reasoning well in structured non-legal settings reasons better in legal ones. Checkpoint and Reuters give depth in tax, accounting and world events, because legal work is rarely purely legal.听

Rigour and factuality听

Our own lawyers publish at leading AI conferences [7] the people building the evaluation apparatus treat it as research rather than quality assurance, which is what makes it rigorous enough to train against.听

We think that rigour produces the result we care most about. In our published deep research evaluation, Thomson working over Westlaw and Practical Law scored 0.83 on factuality against 0.65 and 0.68 for leading frontier models given unrestricted access to the open web. Completeness was close between all three. Factuality was not.听

Completeness is a capability measure: it asks whether the system covered the ground, and on it the three were nearly level, because frontier models with the open web and enough time will generally find the material. Factuality is a reliability measure: it asks whether the system can be checked and survive it, and on that they were not close at all.听

The metric is not a measure of whether an answer sounds authoritative or whether the conclusions are broadly sound. Every claim is extracted and matched against the source cited for it, and the score is the proportion of assertions whose own citations hold up when checked.听

That is the failure that has kept general-purpose AI in the assistant鈥檚 chair. A system reliably right about its own sources is a different category of instrument from one merely fluent about them. It is the difference between something an associate uses and something a partner signs.听

We call this Fiduciary-Grade AI: a standard for AI used where accuracy, accountability and trust are not optional, for professionals working under duties of care and regulatory oversight. Thomson demonstrates that it can be pursued at the model layer rather than bolted on above it.听

Capability and sovereignty are not mutually exclusive听

The lesson is not just about law. Any organisation holding a deep proprietary corpus and real domain expertise has been told it must choose: either rent frontier capability and accept the dependency or own an open model and accept some distance from the frontier. The choice is false, provided you are willing to do the unglamorous work of preparing the data, converting expert judgement into consistent signal, and building the evaluations before you try to move the numbers. The reward is a model you own rather than rent, pointed at the problems you choose, improving on your schedule rather than somebody else鈥檚.听

The compute is not the binding constraint. The corpus and the people who know what correct looks like within it are, and those have never been concentrated in the frontier laboratories. They sit inside institutions that spent a century accumulating them without thinking of themselves as AI companies.听

We are one of those institutions. Thomson is what happened when a research team that had spent three years arguing trust and reliability were the scarce input finally got access to the data and the experts to prove it.听

Thomson enters production this month powering CoCounsel skills including high-volume structured document review, with integration across the legal and tax portfolio to follow. A full technical report is forthcoming. Thomson was built in collaboration with DatologyAI, Lambda, Together AI, Imperial College London and the 成人VR视频鈥揑mperial Frontier AI Research Lab.听

Sources for external claims听

[1] Epoch AI, open-weight capability lag analyses (October 2025; May 2026); Stanford AI Index 2026.听

[2] Luo et al., “An Empirical Study of Catastrophic Forgetting in Large Language Models” (2023); Song et al., arXiv:2501.13669.听

[3] Kotha, Springer and Raghunathan, arXiv:2406.12227.听

[4] LEXam, arXiv:2505.12864.听

[5] “Where Experts Disagree, Models Fail”, arXiv:2603.22973 (2026).听

[6] Rehaag, “I beg to differ”, Artificial Intelligence and Law (Springer, 2023).听

[7] Yejin Bang, Kirsty Fielding, Brandan Oliver, Brian Birke, Nabeel Seedat & Andrew M. Bean, ContractScrub: A Benchmark for Final Review of Legal Contracts (成人VR视频 Foundational Research, 2026) (in Proceedings of the AI for Law Workshop at the International Conference on Machine Learning (ICML 2026), available at ); and Samuel J. Vincent, Daniel Calloway, Fangyi Yu, Andrew M. Bean & Nabeel Seedat, InsufficiencyBench: Evaluating LLM Legal Advice on Underspecified User Queries (成人VR视频 Foundational Research, 2026) (in Proceedings of the AI for Law Workshop at the International Conference on Machine Learning (ICML 2026), available at ).听

That made sense when raw model capability was the biggest constraint. Every new frontier model expanded what was possible. But as AI becomes more widely available in different sizes, capabilities and forms, I think the more important question is becoming: how can I leverage all of the intelligence available to me in the best way?

Answering that requires systems flexible enough to take advantage of the leading frontiers of general intelligence while also exceeding those frontiers in areas of deep specialization and knowledge.

Today, we are launching Thomson, our own AI model built specifically for professional work. It is one of the clearest expressions yet of how 成人VR视频 is evolving as an AI technology company, and of a broader change I believe is coming to enterprise AI.

The future will not be every company sending every problem to the largest model available or the model with the best scores across the widest range of benchmarks. It will be organizations developing the ability to apply the right intelligence to the right work.

And as AI becomes core infrastructure for the enterprise, I don鈥檛 think companies will want to outsource every layer of intelligence that determines how their most important work gets done.

Intelligence should fit the task

Ask a rocket scientist to fix an electrical fault in your house.

They could probably work it out, but they鈥檇 likely bring more complexity to the job than it needs. And they may miss things an experienced electrician would catch instinctively. That isn鈥檛 because the electrician鈥檚 job is smaller. It鈥檚 a different job, mastered just as deeply.

AI can work the same way.

Frontier models are extraordinary systems, and they will remain a critical part of the AI stack. There are problems where frontier intelligence is absolutely necessary, and in some cases clearly the best choice. When the path to the right answer is unclear, frontier models excel at figuring it out, drawing on a wide array of tools and information along the way.

But professional work does not always fit that shape.

Professional work is different. A legal brief, a contract, a tax return: these all have outcomes that require a high degree of accuracy, and the people relying on them need to defend and be accountable for them. The defining challenge isn鈥檛 creativity; it鈥檚 precision, and it repeats across thousands of narrow tasks rather than one open-ended one.

All of these tasks do not necessarily need the same model.

That is why model routing and orchestration matter. The system should be able to understand the work being done and determine what kind of intelligence is best suited to it.

For the user, that complexity should largely disappear. They should simply get the best possible outcome, applying their own judgement, experience and oversight where needed.

Thomson is proof that specialized intelligence works. It鈥檚 built to excel at what professional work actually demands: precision, domain fluency and verifiability, optimized specifically for the professional environments we understand best.

Sovereignty is about owning what makes you different

There is another important shift happening alongside this.

For enterprises, AI sovereignty should not mean cutting yourself off from frontier labs or trying to build everything yourself.

AI sovereignty is about owning the layers of the stack that matter to you, but ownership does not mean exclusivity. You can control your own capabilities while still leveraging the frontier of general intelligence for the things it does best. The goal is to own the capabilities that are strategically important differentiators to your business, the things only you can do or that you do better than anyone else.

As access to frontier models becomes broadly available, access itself becomes less differentiating. What matters is what you can build on top of that intelligence and what you can develop that your competitors can鈥檛 simply buy from the same provider.

Companies spend decades building proprietary knowledge, data, expertise and workflows. As AI becomes a more fundamental part of how work gets done, it makes sense that some of that differentiation should exist at the model layer too.

That is part of what Thomson represents for us.

成人VR视频 has deep expertise in professional workflows and authoritative content built over generations. Thomson gives us the ability to encode more of those advantages directly into the intelligence layer itself.

At the same time, we will continue to work with leading frontier model providers. These are complementary capabilities, not competing philosophies.

The opportunity is to know when frontier intelligence is best, when specialized intelligence is best, and how to bring the two together.

A different kind of AI advantage

I think that will become an increasingly important source of competitive advantage.

The advantage won鈥檛 come simply from having access to the most powerful model or from owning your own. It will come from building an AI architecture that can deliberately use different kinds of intelligence based on the work being done.

The first phase of generative AI was largely about proving how capable general-purpose models could become. The next phase will be about engineering those capabilities into systems designed for specific environments, standards and outcomes.

For professional work, the winners will be the organizations that know which intelligence to use, when to use it, and which parts they need to control themselves.

The next competitive advantage in AI will not come from access to intelligence alone. It will come from knowing how to orchestrate it, and knowing which intelligence is important enough to own.

Those systems hold far more than files. They contain the history of matters, the experience of teams and the accumulated knowledge of the organization.

成人VR视频 and iManage have worked together for years to connect that knowledge with the tools legal professionals use every day. Our renewed and expanded agreement extends those integrations across products including CoCounsel Legal, HighQ and Noetica, while creating a foundation for the next generation of connections between our platforms.

Model Context Protocol (MCP) is one area where that next generation is beginning to take shape. As iManage expands access to its MCP capabilities, we plan to integrate them with CoCounsel Legal, HighQ and Noetica. MCP will add another way for these systems to work together, complementing the integrations customers already use to access, browse and sync iManage documents and bring them into 成人VR视频 workflows.

Legal organizations have established workflows, permissions and governance around their information. New AI capabilities should build on that foundation while giving professionals more ways to use the knowledge they already trust.

A more connected legal workflow

Legal work rarely happens in one application. Research, drafting, document management, collaboration and transactional work all draw on different systems and sources of information.

Our longstanding integrations with iManage already help connect those environments. The renewed and expanded partnership allows us to continue supporting those workflows while opening up new ways for the systems, context and tools our shared customers rely on to work together.

When implemented, the MCP will allow shared customers to query iManage鈥檚 AI within CoCounsel Legal. For CoCounsel Legal, that fits within a broader ambition: helping professionals bring the right legal content, documents and tools together as they work. Westlaw and Practical Law provide authoritative legal content and expertise. Customer documents provide the facts and matter-specific context. Connections with platforms such as iManage make it easier to work across those sources without reconstructing the workflow each time.

Law firms have spent years building valuable institutional knowledge. As AI becomes more embedded in legal work, that knowledge should become easier to use while preserving the controls organizations depend on.

That is where partnerships like this one matter: connecting the systems professionals already trust with the AI experiences they increasingly rely on.

That is the distinction we had in mind when building Westlaw Brief Builder, a new agentic capability in CoCounsel Legal designed specifically for litigation brief writing. As part of the enhanced CoCounsel Legal experience announced this week, Westlaw Brief Builder transforms research and drafting with AI agents that use Westlaw and Practical Law iteratively with the litigator to produce stronger briefs. AI agents conduct extensive research on the facts, arguments, and authority using Westlaw, Practical Law, and material from the case identified by the litigator. With each iteration, the litigator benefits from that research while remaining in control of the arguments, language, and overall structure of the brief.

Generating polished legal prose is becoming the easy part. AI can produce something that looks like a sophisticated brief in seconds. But the appearance of legal reasoning is not the same as legal reasoning, and in litigation, confusing the two can be dangerous. A brief has to do more than sound persuasive. Its arguments need to be grounded in the record, supported by authoritative law, tested against contrary authority, and strong enough for lawyers to put their names behind.

That is where the real opportunity for AI lies. Not in producing more text, but in helping lawyers do the substantive work required to build defensible arguments more efficiently, while preserving the strategy, judgment, and scrutiny that litigation demands.

The harder question is whether that brief is actually good. That goes far beyond checking whether the cited cases are real. If a lawyer has to reconstruct the research and reasoning behind an AI-generated draft before deciding whether to trust it, much of the promised efficiency disappears.

That is why we built Westlaw Brief Builder to work iteratively with the litigator rather than simply generate a finished document. Its AI agents conduct research at each stage using Westlaw, Practical Law, and matter materials identified by the litigator. The lawyer reviews the arguments, facts, and legal authority surfaced by the system, decides what belongs in the brief, and shapes the work as it develops. The result is not a brief handed to the lawyer for inspection at the end. It is a brief the lawyer has actively built with AI agents throughout the process.

Brief writing starts long before the first draft

Strong briefs are built through a series of interconnected decisions. Litigators need to understand the record, identify the issues that matter, determine which arguments are worth pursuing, research the applicable law, and continually reassess those choices as new facts or authority emerge.

Westlaw Brief Builder is designed around that reality. Its structured, multi-step workflow takes the litigator through intake, argument identification, supporting legal research, argument development, and drafting. At key points, the lawyer reviews what the system has surfaced and makes the strategic decisions about what comes next.

For example, Westlaw Brief Builder can propose potential arguments based on the matter and its initial research, but the lawyer decides which ones to pursue. From there, AI agents can investigate the relevant authority and supporting facts, while the lawyer can add information, refine the reasoning, and determine what ultimately belongs in the brief.

That iterative process becomes particularly important when research begins shaping the argument itself.

Research and drafting should work together

The quality of a legal argument depends on what sits behind it. That is why Westlaw Brief Builder integrates Westlaw Deep Research into the drafting workflow and draws on authoritative Westlaw and Practical Law content.

Once a lawyer determines which arguments to develop, the system can research relevant authority based on those arguments, the facts of the matter, and the applicable jurisdiction. The lawyer can review the authorities surfaced through that research, understand how they support an argument, and decide what should be incorporated into the brief.

Bringing those steps together means research can inform the argument as it develops rather than becoming a separate task before or after drafting. For litigators, that is a more natural reflection of how the work actually happens: research changes arguments, facts change research, and the two evolve together until the lawyer is prepared to stand behind the result.

The bar for AI-assisted drafting should be higher than speed

We are going to see continued innovation around AI-assisted legal drafting, and that is a positive development for the profession. Brief writing is demanding and time-intensive work, and there is enormous potential for technology to help lawyers complete it more efficiently.

But as the market evolves, we should be precise about what meaningful progress looks like. The goal is not autonomous brief generation or removing lawyers from legal reasoning. It is helping lawyers spend less time on unnecessary process while giving them better support for the substantive work that requires their expertise.

That principle is central to how we think about Fiduciary-Grade AI at 成人VR视频. In high-stakes professional work, the goal cannot simply be an impressive output. Professionals need authoritative information, transparency into the work supporting the result, and the ability to exercise their own judgment before standing behind it.

Westlaw Brief Builder is one part of the broader CoCounsel Legal experience we are building around that idea. The enhanced CoCounsel Legal brings together research, drafting, firm knowledge, and matter-centric workflows within a single AI-powered environment, helping professionals move from a legal question toward defensible work product without treating each stage of work as a disconnected interaction.

Westlaw Brief Builder takes that approach deeper into one of litigation鈥檚 most consequential workflows. It is designed to help lawyers develop arguments, find and evaluate relevant authority, create a stronger draft, and shape the work as it develops, while preserving the professional judgment that makes a brief more than simply AI-generated text.

AI can make drafting faster. The standard we should be aiming for is whether it helps lawyers produce better, more defensible work they are prepared to stand behind.

Now we are beginning to see what happens when that standard is applied. In one recent evaluation, experienced attorneys reviewed CoCounsel Legal鈥檚 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鈥檚 work product.

That is fundamentally different from generating a synthetic scenario and using another model鈥檚 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:听

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

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

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

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

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.

成人VR视频 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鈥檚 attorney-authored tasks, automated evaluation framework, citation analysis, and attorney review methodology.听

Global Connected Platform听

CoCounsel Integration with NetDocuments (Canada)听

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 鈥� reducing context switching and making it easier to bring trusted firm documents into AI-assisted research, analysis, and drafting workflows.


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Smokeball Knowledge Search Connector (US & Australia)听

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 鈥� 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 鈥� saving the time otherwise spent hunting across separate systems.

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Unified Search and Upload Modal听

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 鈥� 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.

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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 鈥� including preferred clauses, negotiation guidance, escalation guidance, scenario guidance, fallback clauses, and tabular content 鈥� 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 鈥� 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.

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Agentic Playbooks in CoCounsel for Word (US)听

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 鈥� 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 鈥� cutting the time from first read to final redline.

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

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 鈥� 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.


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Explore These New CoCounsel Legal Features Today听

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

To learn more, please visit the .听听听听