AI for Legal Firms and Departments

AI contract review uses NLP models trained to identify, extract, and classify clauses across a defined set of clause types relevant to the contract category being reviewed: for commercial contracts, this includes limitation of liability clauses, indemnity provisions, intellectual property ownership and licensing terms, termination rights, governing law and jurisdiction, confidentiality obligations, payment terms, and warranty and representation scope. For employment contracts, the relevant clause types differ; for real estate leases, they differ again. The system reads a contract document and produces a clause-by-clause extraction report: each identified clause is extracted, classified, and, where you have a standard or preferred position, compared against that standard to flag deviations. A limitation of liability clause that is uncapped, mutual rather than unilateral, or that excludes carve-outs your standard requires gets flagged for attorney attention. Clauses that conform to your standard position are noted but do not require detailed review. The output is a structured review memo that the reviewing attorney can act on directly, rather than a document the attorney must read from cover to cover before any analysis can begin. For high-volume contract review, an M&A data room, a supplier contract renewal programme, or a lease portfolio review, the time saving is substantial: what takes an associate several hours per contract can be completed in minutes. The attorney's judgment is applied to the issues the system surfaces, not to the initial pass that identifies them. We map your standard positions and priority clause types in discovery before building the extraction model.

Legal research automation for law firms uses retrieval-augmented generation (RAG) built over your preferred legal databases and your firm's own matter history. When an attorney or paralegal submits a research query, 'what is the current standard for implied duty of good faith in commercial contracts under English law?' or 'find cases where a force majeure clause was held not to apply to a supply chain disruption event in the last five years', the system retrieves the most relevant cases, statutes, and secondary sources from the connected database and generates a structured research memo grounded in those sources. Each proposition in the memo is linked to the source document with the relevant passage. The attorney can verify the source and read the full judgment for any proposition that requires deeper review. This is different from asking a general-purpose LLM a legal question. A general LLM will generate a plausible-sounding answer based on its training data, which may be out of date and will not cite the specific cases your jurisdiction requires. A RAG-based legal research system generates its answer from the documents it retrieves in real time, with citations you can follow. The system can be connected to Westlaw, LexisNexis, or other legal database subscriptions via API, or built over a corpus of case law and statute that you provide. For in-house departments focused on a specific regulatory domain, a custom corpus built from the relevant regulatory guidance, case law, and your own legal opinions often produces better results than a general legal database connection. We assess your research workflow and preferred databases in discovery.

Due diligence AI analysis applies the same clause extraction and document summarisation technology used in contract review to the specific document types that appear in M&A, financing, and real estate due diligence data rooms: share purchase agreements, disclosure letters, material contracts, employment agreements, IP assignments, regulatory licences, litigation schedules, and property title documents. For each document type, the system extracts the information relevant to the due diligence scope: for material contracts, key terms, change of control provisions, assignment restrictions, and termination rights; for employment agreements, compensation, restrictive covenants, and notice periods for key employees; for regulatory licences, scope, conditions, and transferability on change of control. The output is a structured due diligence report that flags identified issues against a risk matrix you define: what is a deal-stopper, what requires a disclosure or warranty, and what is acceptable without further action. For a typical data room of several hundred to several thousand documents, manual due diligence by an associate team takes weeks. AI analysis of the same document set takes hours, with the associate team's time directed at reviewing and acting on the issues the system flags rather than reading every document from scratch. The quality of the output depends on document quality (scanned PDFs versus native digital documents affect extraction accuracy) and on the specificity of the due diligence scope. We assess both in discovery before building.

Billing time entry suggestion uses activity data from your practice management system, emails sent and received, documents accessed and edited, calls logged, court filings submitted, and meeting records, to generate draft time entry descriptions and duration estimates for attorney review. The model is trained on your firm's historical billing data: what do accepted time entries for document review on this matter type look like, what duration is typical for a first draft of a contract of this type, what description format does each billing partner use. When an attorney opens their time entry screen at the end of the day or week, the system presents a draft time entry log based on the activity data for that period. The attorney reviews the draft, edits the descriptions and durations that need adjustment, and approves the entries for billing. The improvement this produces is twofold: attorneys who consistently under-record (doing the work but not recording all the time) capture more billable time because the system prompts them with the activity it observed; and the time spent on time entry, which for many attorneys is a significant administrative burden done under time pressure, is reduced because the first draft is already written. Both effects improve matter realisation. The system requires integration with your practice management platform and email system to access the activity data that drives the suggestions. We assess your practice management setup and the data available for training in discovery.