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

抖阴成年 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 抖阴成年 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

抖阴成年 acquired Safe Sign in August 2024, in the company鈥檚 first pre-revenue acquisition. The team became 抖阴成年鈥� 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 抖阴成年 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

抖阴成年 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 抖阴成年 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 抖阴成年 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

抖阴成年 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 抖阴成年, 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 肠补苍鈥檛.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 抖阴成年鈥揑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 (抖阴成年 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 (抖阴成年 Foundational Research, 2026) (in Proceedings of the AI for Law Workshop at the International Conference on Machine Learning (ICML 2026), available at ).听

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 抖阴成年. 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.

Today, we’re announcing the next generation of CoCounsel Legal, now available in Beta. Built from the ground up, it delivers on the vision we set out from the start: an AI companion that works alongside lawyers through every task and every stage of a matter, grounded in the trusted sources of knowledge they rely on.

Built on the most advanced AI, and engineered for how legal work actually gets done

Built on Anthropic’s Claude Agent SDK, the next generation of CoCounsel Legal is a unified agentic platform that plans, selects tools, retrieves authoritative content, and adapts mid-workflow just as a senior associate would, not a first-year waiting for the next instruction. Critically, the lawyer remains in control鈥攁ble to see the agent鈥檚 reasoning as it unfolds, step in to redirect its approach, challenge its assumptions, and probe whether alternative angles have been considered.

CoCounsel Legal doesn鈥檛 reason from the web 鈥� it鈥檚 built with Westlaw and Practical Law content and tools natively embedded. Different by design, the technology and the sources are built as one system, making defensibility part of the architecture rather than a feature. As a result, when CoCounsel Legal produces a deal term sheet, contract, or litigation strategy memo, every step of its reasoning is grounded in authoritative legal sources, guided by 35 million West Key Number classifications and 3.9 million Precision Research attributes, and fully transparent through verifiable Practical Law resources and Westlaw citations. Developed and evaluated by practicing-attorney editors working alongside top AI data scientists, the breakthrough isn鈥檛 simply faster task completion 鈥� it鈥檚 the ability to produce complex work product across the many decision points of a legal matter, moving beyond task execution to true legal reasoning.

Our leading evaluation framework encodes quality at each step. This means before any capability ships; we measure it. Licensed attorneys, including our Practical Law editors, define what the correct output looks like for each task type. Every new capability must demonstrate measurable improvement against that benchmark before it reaches production. The framework evaluates not just final outputs, but the full chain of reasoning that produced them, because an agent that arrives at the right answer through flawed reasoning cannot be trusted to do so consistently.

And we’ve gone further to protect the integrity of that reasoning, with patent-pending tools for citation integrity and output verification:

• Verification and grounding as system primitives. Authoritative retrieval, explicit source handling, and verifiable citation flows are product infrastructure -not post-processing or marketing language.
• Patent-pending link integrity.听Our patent pending citation ledger architecture tracks every source the agent brings into context and the specific passages it reads.

This is ; outputs grounded in authoritative content and customer context – making verification part of the system鈥檚 architecture rather than an afterthought. In a profession where a single missed citation can cost a client their case, defensibility isn’t a nice-to-have. It’s the whole point. In a profession where a single missed citation can cost a client their case, defensibility isn’t a nice-to-have. It’s the whole point.

What our customers are telling us

The feedback we’re hearing from customers reflects this.

Brooke Conkle, partner in Consumer Financial Services at Troutman Pepper Locke, asked CoCounsel Legal a broad question about recent TCPA developments across two circuits and the solution “immediately zeroed in on the precise ascertainability nuances” between them, the kind of careful parsing that typically requires significant time and research. Her conclusion: “The underlying legal analysis genuinely blew me away and made me rethink what is possible with AI in complex litigation work.”

That’s not the response of someone who found a faster tool. That’s the response of someone who found a different kind of tool.

Andrew Medeiros, managing director of Innovation at Troutman Pepper Locke, captures something I think is fundamental to why this matters: “Lawyers don’t want to just operate software, and that’s not what great AI should do.” What he’s seeing is that CoCounsel Legal keeps lawyers in the analytical mindset they were trained for, going back and forth, challenging answers and steering the work.

He added: 鈥淭he next generation of CoCounsel Legal seems to be a total game changer as听we’ve听introduced it to litigation and transactional attorneys. It’s meeting them within their workflows, allowing them to ask plain language questions and then see the step-by-step approach that CoCounsel [Legal] takes to help them draft the document relying upon Westlaw Deep Research and the Practical Law guidance.鈥�

The AI Knowledge Management Department at Morgan Lewis, shared, “We were really impressed with the enhancements to the CoCounsel Legal platform. In our evaluation, it demonstrated strong capabilities in supporting efficient document drafting and in addressing gaps in information, such as filing party details, with both speed and accuracy when prompted. The outputs were well-structured and immediately usable, and the overall workflow was intuitive and easy to navigate. Performance was consistently fast. We are really looking forward to what鈥檚 next!”

Why we’re launching this as a beta, and building in public

Just as important as what we鈥檙e building is how we鈥檙e introducing it to customers.

We are deliberately launching the next generation of CoCounsel Legal as a beta, with a clear commitment to building in public and in partnership with our customers. This beta includes leading law firms such as Troutman Pepper Locke, Morgan Lewis, Carlton Fields, and Caplin & Drysdale, as well as four large enterprise customers. As we move through successive beta waves ahead of general availability later this year, we鈥檙e putting the solution in the hands of real lawyers working on real matters 鈥� listening closely to where it earns confidence, where it doesn鈥檛, and incorporating that feedback directly into how the product evolves.

We鈥檙e inviting customers to help shape what CoCounsel Legal becomes 鈥� an AI that works at the level of a senior associate, built with Anthropic with cutting edge technology, engineers for legal work with authority and verification at its core.

This reflects a core belief I hold: the solution itself should be the argument. The strongest validation won鈥檛 come from launch announcements or benchmarks alone, but from sustained use 鈥� when lawyers choose to rely on the product because it holds up under real professional accountability.

Today鈥檚 beta is just the beginning. I鈥檓 excited to put the next generation of CoCounsel Legal in the hands of more customers as the year progresses.

I encourage you to explore how it works.

Recognizing that client expectations are evolving, BLG is committed to anticipating client needs and exploring new approaches to legal work using emerging AI capabilities. This means reimagining workflows to free up lawyer time for complex problem-solving and strategic work, enabling teams to rethink how matters are handled and drive greater value for clients. By embracing AI, the firm is positioning itself for more agile client service in a rapidly evolving environment. To support these efforts, BLG needed tools that could handle growing work volume and sophistication without compromising quality.听

The firm also identified a talent imperative: today’s emerging legal professionals expect to work with advanced AI tools that will shape the future of legal practice and empower them to do their best work. To continue attracting forward-thinking talent, BLG needed to demonstrate its leadership in responsible AI adoption by giving professionals access to the right tools and training to elevate their impact and redefine exceptional client service.听

As part of its firmwide AI strategy, BLG undertook a comprehensive evaluation of AI solutions, piloting CoCounsel through multiple versions. This hands-on evaluation gave BLG confidence that 抖阴成年 was building something transformative.听

BLG selected CoCounsel for three reasons:听

• 鈥�Integration and workflow fit:鈥疌oCounsel integrates with Westlaw and Practical Law to create a single, trusted legal ecosystem based on authoritative Canadian legal content. Everyone at the firm can access these AI capabilities鈥攍awyers, law clerks, paralegals, and members of business services teams.
• 鈥�Governance and responsibility:鈥兑醭赡�’ guardrails, security practices, and Trust Principles align with BLG’s standards for client confidentiality and responsible AI use.
• 鈥�Product maturity and roadmap:鈥疌oCounsel’s demonstrated performance, together with 抖阴成年’ evolution as a legal technology company, gave BLG confidence in a platform that will continue to evolve.

“We see this technology as fundamentally transforming how legal work gets done,” said David Di Paolo, National Managing Partner & CEO of BLG. “It allows our lawyers to spend their time on the high-impact, complex problem-solving that clients really need from us.”听

BLG’s decision is grounded in a purposeful and holistic review process and a value creation framework designed to support long-term adoption and maximize the positive impact of the firm鈥檚 AI investments.听

“Clients are asking great questions about how AI can drive efficiency, and we see a powerful opportunity to advance how we deliver value for them,” added Di Paolo. “With CoCounsel, we鈥檙e stepping into a future of new possibilities, equipping our professionals with the right tools to meet rising expectations while positioning us to attract the next generation of legal talent.鈥�听

With CoCounsel, BLG deepens its relationship with 抖阴成年 and brings authoritative AI to its teams鈥攅nhancing speed, quality, and client service. As one of only two national, full-service Canadian law firms to partner with 抖阴成年 on this technology, BLG is helping to define legal industry transformation.听

Learn more听

To see how CoCounsel supports responsible AI adoption for leading firms like BLG, visit .听

At the conference, we are unveiling new beta features for CoCounsel Legal that tackle document review and agentic execution in a much more powerful way. Over the coming months, these new features will expand CoCounsel Legal鈥檚 capability to handle the broadest range of legal work with even more precision. We’ve also released several drafting improvements and announced a new partnership to seamlessly bring our trusted, authoritative content together with a law firm鈥檚 proprietary work product.

Built for How You Work

Focusing on speed, security, and collaboration, we’re making CoCounsel Legal faster, more intuitive, and more collaborative 鈥� all while maintaining the professional-grade trust and compliance our customers require.

Bulk Document Review (beta)

The beta release of CoCounsel Legal’s bulk document review redefines the approach to high-volume document analysis. Designed to tackle one of legal practice’s most resource-intensive challenges, this powerful solution will empower legal professionals to ingest vast document sets and receive intelligently structured, sortable results in an intuitive table format.

This capability will overhaul traditional review cycles, moving beyond the limitations of manual methods by enabling the efficient analysis of up to 10,000 documents in a fraction of the time. This can directly reduce the number of write-offs firms take when reviewing documents 鈥� an area that often impacts profitability per attorney.听听 CoCounsel Legal鈥檚 structured analysis provides immediate access to critical data, allowing for rapid filtering and sorting to pinpoint key information across a spectrum of vital workflows: from optimizing litigation discovery and streamlining M&A due diligence to enhancing regulatory compliance reviews and accelerating contract analysis.

Agentic AI, Grounded in Expertise

Leveraging an unmatched foundation of expertise and authoritative content from Westlaw and Practical Law, CoCounsel uses advanced AI to plan and execute complex, multi-step legal workflows.

Independent Execution of Legal Tasks (beta)

CoCounsel Legal will independently plan and execute complex, multistep legal workflows. With a single natural language prompt, legal professionals can trigger sophisticated workflows 鈥� whether conducting legal analysis, drafting documents, or researching case law. The platform intelligently interprets each request, maps the optimal execution strategy, and completes the entire workflow by integrating 抖阴成年 authoritative Westlaw and Practical Law content with the law firm’s proprietary knowledge base. The result? Less time managing tasks, more time delivering strategic counsel.

Custom Workflows (beta)

CoCounsel Legal’s workflow builder lets legal professionals design, save, and share custom workflows that combine 抖阴成年’ trusted content with their firm’s own expertise. It’s a straightforward way to build repeatable processes that capture institutional knowledge and scale best practices across the entire firm鈥攎aking everyone more efficient and consistent in how they work.

Deep Research on Westlaw 鈥� Expanded coverage, enhanced precision

Deep Research now covers state administrative materials in addition to federal sources, giving customers a more complete view of the regulatory landscape. Customers also get timely, curated updates on case law, regulatory changes, and practice developments from JD Supra, Quinlan, and Westlaw Daily Briefings. Plus, the new Clarifying Questions feature asks for additional details when it spots gaps, then reruns the analysis to make each report more precise and tailored to what customers actually need.

Identifying Citation Issues

CoCounsel’s Identifying Citation Issues feature allows users to upload a brief or memo and get an instant review for inaccurate or missing citations eliminating tedious, line by line checks. CoCounsel flags potential errors and presents a clear side by side table showing the original cite and the issue detected, allowing the ability to fix problems fast. Every check is validated against the trusted authority of Westlaw, delivering confidence that the cited sources are accurate before filing. The result: tighter documents, fewer surprises, and more time for the substance of the argument.

AI Overview for Statutes Compare

Comparing statute versions just got easier. With AI Overview in Statutes Compare, users can click once to get a clear, plain language summary of what changed materially between two versions 鈥� so less time is spent wading through long sections and cosmetic edits, and more time spent understanding the impact. It filters out formatting and nonsubstantive tweaks to spotlight the revisions that matter for analysis, improving efficiency, reducing the risk of missing a key change, and briefing clients or colleagues with confidence.

Find Practical Law Drafting Language

Find Practical Law drafting language is a new skill available to quickly find the drafting language needed by simply entering in a prompt for specific clauses or to fit certain scenarios, such as 鈥渇ind me drafting language where party A resides in a different state but agrees to this state鈥檚 forum.鈥� This AI-powered search capability eliminates the need to navigate complex taxonomies or remember exact clause titles, reducing drafting time while maintaining the rigor and reliability users trust and expect from Practical Law.

Practical Law Search & Summarize in Word

Designed to deepen the Practical Law integration within Word, this feature, coming soon, lets legal professionals ask how-to questions and get summarized, synthesized answers pulled from Practical Law’s full collection of practice notes, checklists, and standard documents. Responses include citations to source materials, making it easy to verify information or dig deeper.

Update Contract Terms

CoCounsel can now handle the tedious work of updating contract terms. Upload a term sheet, and it populates your template or precedent agreement with tracked changes and annotations showing what was revised. It’s a straightforward time-saver that reduces manual data entry errors and frees up legal professionals to focus on higher-level work

Rooted in Your Knowledge

By integrating with document management systems, precedents, and templates, CoCounsel Legal delivers insights that draw from both a firm’s institutional knowledge and 抖阴成年 authoritative sources

NetDocuments and the ndConnect Program

A with NetDocuments delivers another advantage for legal professionals. With the introduction of ndConnect, NetDocuments鈥� new interoperability program, legal professionals can securely incorporate AI capabilities from CoCounsel Legal into their document management workflows. Users can now conduct legal research with Westlaw and Practical Law, draft documents based on both their own internal content and that of 抖阴成年, and analyze documents with advanced review capabilities 鈥� all while maintaining their documents鈥� integrity and metadata within NetDocuments.

This unique advantage means we are delivering cutting-edge solutions to enable higher quality work, faster, while reflecting the needs and expectations of how professionals work today and will in the future.

However, there is not an AI system that is entirely free from the possibility of inaccuracies.

A听recent case听was published from the U.S. District Court for the Central District of California that included sanctions for the submission of a brief containing numerous hallucinated citations. Through initial research to create the brief, a public large language model was used and the accuracy of several citations from the preliminary information were not verified.

Our investigation found no evidence that either CoCounsel or Westlaw was the source of the fabricated cases. We confirmed that Quick Check on Westlaw Precision identified all the issues with the erroneous quotes.

This case illustrates that despite the transformative capabilities of AI within the legal industry, .

At 抖阴成年, we strongly advocate for human verification of AI-generated results. That is why our customers see guidance in each of our products for a human to verify all AI generated results. And 抖阴成年 offers multiple solutions including Quick Check on both Westlaw Edge and Westlaw Precision, KeyCite, amongst others, to help verify information.

抖阴成年 is committed to building responsible AI solutions that legal professionals can rely on while respecting the irreplaceable qualities humans bring to the table: judgment, empathy, and understanding of human nuance. This human-centric approach to AI isn’t just good practice 鈥� it’s essential for maintaining the integrity of legal work.

This post was written by Steve Assie, general manager, Global Large Law Firms, 抖阴成年

抖阴成年 President and CEO Steve Hasker shared a state of the industry outlook, noting generative AI is as disruptive and transformative as previous technology shifts yet is happening even faster. He emphasized what differentiates 抖阴成年, including investments the company is making in generative AI to enable professionals to accelerate and streamline entire workflows and deliver more value for clients. 听听

Hasker said 抖阴成年 has invested more than $200M in AI in the last year. He discussed the company鈥檚 vision to provide each professional it serves with an AI assistant; the launch of CoCounsel 2.0, which generates answers three times faster than the previous version; and new work with Microsoft on autonomous agents to increase revenue, reduce costs, and scale impact for customers.鈥�听

Tax, Audit & Accounting听听

鈥淎I is not just changing the landscape of accounting, it’s reshaping it.鈥� That was the message from Elizabeth Beastrom, president of Tax & Accounting at 抖阴成年. 听

While the profession sees AI as a game-changer to help them work differently, tax and accounting professionals also continue to wrestle with the perennial challenge of a talent shortage. This, combined with escalating complexity and more tax regulations, as well as changing client expectations, leaves tax professionals in need of a critical solution.听

抖阴成年 sees the potential of AI to help alleviate these challenges by augmenting human capabilities. Automating mundane, time-consuming tasks will enhance efficiency for tax professionals, helping them reclaim time to channel into higher value tasks. 抖阴成年 is working to bring the power of generative AI, machine learning and automation into its solutions in the following ways:

1. Saving time in tax preparation:

Coming in beta during the upcoming busy season, 抖阴成年 will launch an AI-assisted tax preparation experience to increase firm efficiency. The solution combines the power of CoCounsel, 抖阴成年 professional-grade generative AI assistant, with workflow automation and software integrations. It supports the delegation of data gathering to simplify mundane tasks and automate tax preparation. 抖阴成年 research shows that customers using this solution will save at least two hours per 1040 tax return on average.听

2. Supporting firms鈥� growth with advisory: 听

As client expectations continue to evolve, they鈥檙e increasingly looking to their accountants as trusted advisors. Firms of all sizes are focusing on growing their advisory practices to help bring their clients additional value, as well as supporting their growth. In 2025, the 抖阴成年 Advisory Solution will combine the power of CoCounsel and Checkpoint content to identify advisory opportunities. Advisory services are integrated directly into a firm鈥檚 practice, with technology empowering junior staff to take on higher-value advisory work and seasoned professionals to move beyond technical expertise to value-added synthesis. 听

鈥淚t helps firms build their advisory practice with confidence to deliver unprecedented value to meet clients鈥� evolving needs,鈥� said Nancy Hawkins, vice president of Product Management, Research.听

3. Transforming audit efficiency:听

Halving sample sizes, boosting efficiency and sharpening the focus on high-risk areas are all at the heart of 抖阴成年 Audit Intelligence Analyze solution, which launched in October. Further functionality will be coming in 2025 as it expands the Audit Intelligence suite capabilities. 鈥楾est鈥� will support with automating substantive testing with dynamic transaction tracing, while 鈥楶lan鈥� will harness full data populations with cutting-edge analytics for superior risk assessment. Both will launch with beta programs next year, along with the addition of CoCounsel to the Audit Intelligence suite.听

All three solutions 鈥� Review Ready, 抖阴成年 Advisory Solution and the Audit Intelligence suite 鈥� will be further enhanced with 惭补迟别谤颈补鈥檚 generative and agentic AI capabilities.听

Corporates听

Laura Clayton McDonnell, president of the Corporates segment, shared how enterprise technology, including AI and generative AI, is revolutionizing the profession with innovative and emerging solutions. She emphasized that companies are taking a streamlined and proactive approach to addressing risk and compliance across the enterprise, while driving towards their business goals, will maintain their competitive advantage. Clayton McDonnell also shared how organizations are using solutions including ONESOURCE Pagero, CoCounsel Core, Legal Tracker, Checkpoint Edge with CoCounsel and CLEAR to solve challenges and realize value for their business.听

In addition, Ray Grove, head of Corporate Tax and Trade, 抖阴成年, highlighted the company鈥檚 efforts to build a seamless, integrated compliance network, and Kevin Appold, vice president of US Public Records, 抖阴成年, shared how the company鈥檚 risk and fraud solutions play a critical role in the convergence of compliance and commerce. Also, Valerie McConnell, senior director of CoCounsel Customer Success, discussed how CoCounsel is transforming the general counsel鈥檚 office.听

Legal听

A highlight from the Legal Professionals segment included an in-depth look at the 抖阴成年 2025 AI product roadmap from David Wong, chief product officer; Mike Dahn, head of Westlaw Product; and Valerie McConnell, senior director of CoCounsel Customer Success. They outlined upcoming generative AI features and innovations to support legal professionals, including deeper integration of CoCounsel 2.0 in Westlaw and Practical Law plus generative AI research features including Claims Explorer, Mischaracterization Identification in Quick Check and AI Jurisdictional Surveys.听

Legal SYNERGY attendees also participated in interactive sessions and CLE courses on advanced prompting techniques, the science behind large language models, and optimizing generative AI for tasks like drafting and legal research. Sessions offered attendees a comprehensive view of the future of AI in law.鈥�听

SYNERGY 2024 also included several customer panels and executive briefing sessions. Watch the Innovation Blog for highlights from these sessions and for 2025 product and innovation highlights.听

Below, CJ Lechtenberg, senior director, Westlaw Product Management, 抖阴成年, shares her insights on developing Mischaracterization Identification, a generative AI capability to help detect mischaracterizations and omissions in legal briefs.

In the five years since Quick Check was introduced, you鈥檝e added many enhancements including Quick Check Contrary Authority Identification, Quick Check Judicial and Quick Check Quotation Analysis. How did integrating generative AI make the Mischaracterization Identification enhancement different than previous ones?

Lechtenberg: This enhancement takes researchers beyond the step of knowing what might be a potential mischaracterization to an explanation of why something might be a potential mischaracterization 鈥� and that is radically different from any feature we鈥檝e deployed in Quick Check before.

I鈥檓 sure it鈥檒l come as no surprise when I say that generative AI is just a completely different beast. Lay people may think about the law as being black and white.听You can do this; you 肠补苍鈥檛 do that.听But legal professionals know that the law is really a sea of varying shades of gray. With machine learning, we wrestled with how we could ever give the machine enough data to figure out all the different ways an attorney may mischaracterize the law.

In Quick Check Quotation Analysis prior to the Mischaracterization Identification enhancement, we highlighted the actual textual differences 鈥� additions, omissions, and changes 鈥� in the quotations and showed the context around the quotes.听Doing so certainly saved researchers a significant amount of time and helped them spot issues they might not otherwise find, but the onus was still on researchers to review everything and determine what the precise differences were and how material they might be, if at all.听Even with the additional context provided, it could still be difficult to determine whether the quotations were taken out of context, especially if the quotes themselves didn鈥檛 appear to be different.

In developing Mischaracterization Identification, we recognized that the task of analyzing quotations and their context is so nuanced that attorneys will have different expectations for whether a mischaracterization occurred, so we needed to provide more than just categorizations. We found that large language models (LLMs) can generate nuanced descriptions of potential mischaracterizations, versus just explicit categorizations, and do it well, which is hugely beneficial for this type of task.

How will using Mischaracterization Identification give legal professionals and law firms a competitive advantage? How will judges using it benefit?听

Lechtenberg: The advantages of using the new Mischaracterization Identification are substantial for both legal professionals and the judiciary 鈥� both in terms of speed of review and quality of work product.听When we launched Quick Check Quotation Analysis in 2020, customers, both legal professionals and the judiciary, lamented about how time-consuming it is for them to review quotations and how challenging it is to spot differences. It is a mentally taxing task and often our brains fill in the blanks 鈥� interpreting what we think a brief maybe should say but actually doesn鈥檛.听 Attorneys never have a surplus of time, so the last thing they want to do is spend the little bit they have on the most tedious of tasks and still end up missing potential problems.

For attorneys, Mischaracterization Identification will help them efficiently and accurately make contextual misstatement and omission determinations for their opponents鈥� and their own quotations and the context surrounding those quotations. The fear of missing their own mistakes is very real for attorneys, but the possibility of missing the opportunity to capitalize on their opponents鈥� mistakes is an even larger concern. This new enhancement reduces both of those worries and will help attorneys be even better advocates for their clients.

Judges will also be able to effectively review the filings of parties in matters before them much faster. Attorneys owe a duty of candor to the judiciary and the Mischaracterization Identification feature will help flag any potential issues quickly. An added benefit, which members of the judiciary or their staff perhaps haven鈥檛 considered, is the ability to analyze their own orders and opinions to ensure that they haven鈥檛 made mistakes that could be appealed. This new enhancement will help alert judges and law clerks to potential issues before they finalize their opinions.

What early feedback are you hearing from customers?

Lechtenberg: In a recent survey, 93% of law firm professionals told us they鈥檝e seen opposing counsel misuse a quotation, 66% said they鈥檝e seen misrepresentations by an associate or colleague, and 65% of corporate respondents said they check the accuracy of outside counsel鈥檚 quotations.听The need to review opposing counsels鈥� and colleagues鈥� briefs for mischaracterizations of the law is still a very real issue for attorneys. Likewise, attorneys have said they鈥檙e always concerned about the accuracy of their work and that maintaining their reputation as a credible litigator with courts and opposing counsel is incredibly important.

Customers are extremely excited about this new Quick Check enhancement to help combat these concerns and we鈥檝e received positive feedback from them.听One law firm managing partner stated that they would use this tool a lot.听They cite-check their opponents鈥� briefs, so any shortcuts are beneficial to them. They recognize that most of the time, errors are harmless, but occasionally there are things they want to bring to the court鈥檚 attention and this feature will help them spot those issues more quickly and accurately.

Another law firm partner said this new feature is the 鈥渦ltimate security blanket鈥� because everything attorneys do is based on their credibility, and this feature alerting them to quotes being taken out of context before filing with the court would calm some of those fears.

Any surprising or unexpected moments as the team worked on developing or launching Mischaracterization Identification?

Lechtenberg: The fact that we鈥檝e accomplished this now with the use of LLMs is exciting, a little surprising and a long time coming. I鈥檓 an attorney who leads a team of attorneys; we鈥檙e literally trained to question everything and have a healthy dose of skepticism.听But I have been dreaming about a mischaracterization identification feature in Quick Check ever since we developed Quotation Analysis more than five years ago. At my core, I believed someday this could be achieved, but for years traditional machine learning approaches were just not powerful or nuanced enough to do it well.

Leveraging LLMs for a use case like this is a new frontier like we鈥檝e never seen before.听The LLM鈥檚 ability to analyze text from an uploaded document and compare that text to the text from the cited case used to support the argument and then go beyond highlighting textual differences and provide an actual explanation of what may be problematic 鈥� whether that鈥檚 a selective quote, omitted context or a misinterpreted holding 鈥� has been absolutely astounding.

What鈥檚 the one thing you want everyone to know about Mischaracterization Identification?

Lechtenberg: Mischaracterization Identification will not only help researchers spot contextual misstatements and omissions in their opponents鈥� or their own quotations and contextual statements faster and with more accuracy, but most importantly it will help them understand why those misstatements or omissions may be problematic. And, spoiler alert: Mischaracterization Identification is just the beginning of how 抖阴成年 will harness the power of generative AI in Quick Check to solve important customer problems.

For more on Mischaracterization Identification, read the press release or check out the by Mike Dahn, head of Westlaw Product Management, 抖阴成年.

In the 1990s, statistical and rules-based methods were state of the art. Over time, these evolved into more advanced modeling and machine learning techniques. Today, we leverage transformer-based, generative AI models. Regardless of the era, 抖阴成年 has consistently been at the forefront, figuring out how to best use these tools to serve our customers.

Below is a look at the 抖阴成年 Labs projects, collaborations, and breakthroughs that are an integral part of our approach to innovation. From pioneering research to practical applications, Labs’ role in shaping the future of information technology offers a fascinating glimpse into the synthesis of advanced technology and expert knowledge.

Who is 抖阴成年 Labs?

抖阴成年 Labs is focused on the research, development, and application of artificial intelligence (AI) and emerging trends in technologies. We generate ideas and solutions to determine the art of possible, and in partnership with product engineers and other stakeholders, we deliver smarter and more valuable capabilities for our customers.

AI and 抖阴成年 Labs: A multi-decade partnership

The groundwork for much of what we are doing now with generative AI (GenAI) was established in the early 1990s with our work on Westlaw Is Natural (WIN), the first commercially available search engine with probabilistic rank retrieval.

We have also long employed ModelOps, an extension and adaptation of the DevOps principles for the AI ecosystem. The objective is to shorten the AI delivery lifecycle and ensure long term sustainability and quality levels of AI solutions through a combination of automation, continuous delivery and monitoring best practices.

Dedicated to end-users from the beginning

Trust is one of our most important values. 抖阴成年 Labs exists to deliver the best technology and tools that professionals can trust to make their work lives better. That鈥檚 why we always begin with the end. The end-user, that is.

We take a multidisciplinary approach to developing AI we call human-centric AI. This process involves recognizing the current and future needs of the professional user, starting with formulating a hypothesis to clearly define the problems, and then determining what successful outcomes would entail. Only then do we begin to build by creating and testing prototypes for viability, then iterate that process all the way through to final production.

鈥淎ll our effort is to impact and meaningfully improve our products for the end-users to make their lives easier and their work better,” said Zahra Shekarchi, senior research engineer, 抖阴成年 Labs. “As much as AI is fantastic for making our lives easier and providing smarter solutions, it can influence us in other ways with unintended consequences. That鈥檚 why I have been following ethics in AI 鈥� privacy, bias and fairness, diversity, and social impacts.鈥�

Our cross-functional approach to developing solutions

We believe we have created a culture that taps into the best of all possible worlds. We involve research and data scientists, engineers and designers at an early stage. And by having embedded teams we can move quickly, identify obstacles and opportunities early, and ensure a diversity of perspectives on problem-solving.

We combine skills such as machine learning, search and recommendation, and natural language processing with engineering acumen, design capabilities, and a human-centric approach all in the service of creating the best possible user experience.

鈥淚 work with talented teams in London, Toronto, and Switzerland,” said听John Hudzina, lead research scientist,听抖阴成年 Labs. “We get different perspectives from each other, different experiences, and insight into different legal systems. The reason we thrive in the area of AI is first identifying the need or the problem, then decomposing it, solving all the pieces, and then bringing them all together.鈥�

Innovation that never ceases

There is no such thing as 鈥渄owntime鈥� at 抖阴成年 Labs. We鈥檙e never satisfied that everything is finished. Our continuous pursuit of improvement has enabled us to produce since 2020. The when we find the answers that help our customers thrive.

鈥淚 believe the key to our success at 抖阴成年 Labs is that we embrace change and never stop learning,” said听Mokarrom Hossain, senior research engineer, 抖阴成年 Labs. “AI is reshaping our world, so staying curious and continuously learning is key to not just keeping up but excelling.鈥�

Our priority is to deliver smarter, more valuable tools for professionals. By combining deep customer knowledge with cutting-edge technology, we not only meet but anticipate the evolving demands of our customers. As we continue to explore new frontiers in AI and other emerging technologies, we are dedicated to making a meaningful impact on the professional landscape, helping customers thrive in an ever-changing world.

Follow our AI @ TR timeline to discover our innovation journey and learn more about our core pillars of AI and technology solutions.

This is a guest post from John Duprey, distinguished engineer, 抖阴成年.

For legal professionals filing a lawsuit, defending a lawsuit, or advising clients on potential liability, often their first step is to identify applicable claims or counterclaims. Yet not all claims are equal. Some causes of action have a lower threshold to achieve, some provide for attorneys鈥� fees or higher damages, and some fit better with the facts of a particular case.

In testing with attorneys, those who used the new skill found relevant causes of action three times faster than when using traditional research methods. In addition, in reviewing Am Law 50 litigation where claims were added after the initial pleadings, the new skill found 94% of the claims that were missed in the initial pleadings and later added by the firms.

鈥淔inding claims with traditional research methods can be difficult and time consuming,鈥� said Mike Dahn, head of Westlaw Product Management, 抖阴成年. 鈥淓ven experienced lawyers can miss applicable claims. Customers have told us about the difficulty of claims research for years, and it鈥檚 not just that it can take hours 鈥� it’s error prone, which is easy to see in how often reputable firms attempt to add new claims or counterclaims later in litigation, after the initial pleadings. But courts won鈥檛 always allow you to add a claim later, and missing the best claims can have significant consequences. It can mean the difference between winning or losing a motion, recovering more in damages or attorney’s fees, or potentially losing a case.鈥�

Dahn added this new skill was purpose-built using the latest generative AI plus new claims content created by 抖阴成年 attorney editors. 鈥淲hen we tried to solve claims research issues with AI alone, it didn鈥檛 work very well, so we had our attorney editors create new content about causes of action that enabled AI to work much better. We鈥檒l continue to do work like this for other workflows where AI alone struggles.”

The new skill is the latest milestone in the 抖阴成年 expanded vision for CoCounsel 鈥� the professional-grade GenAI assistant 鈥� to enable professionals to seamlessly complete complicated work involving multiple products through a single generative AI assistant.

For more on the new skill, check out .