AI for law firms that clears the first pass, not the judgment.

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. 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. 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. 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, 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 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.

Due diligence AI applies clause extraction and document summarisation technology 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. The output is a structured due diligence report that flags identified issues against a risk matrix you define. 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.

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 improvement is twofold: attorneys who consistently under-record capture more billable time because the system prompts them with the activity it observed; and the time spent on time entry is reduced because the first draft is already written. The system requires integration with your practice management platform and email system. We assess your practice management setup and the data available in discovery.

Legal AI projects at RaftLabs are scoped and priced before development starts. A first workflow, one contract category with a defined clause set, typically starts around $25,000 to $45,000 and launches as a validated v1 in 12 to 16 weeks. A full legal-AI platform spanning research automation, due diligence, and billing grows into six figures as you add workflows. Legal research automation with RAG over Westlaw or LexisNexis integration is a larger build, typically a first version in 16 to 20 weeks. We give you a fixed price after a discovery phase that maps your document library, matter data, and the specific workflow being addressed. Use our software cost calculator at raftlabs.co/tools/software-development-cost-calculator for a starting range.

Yes, and it's an explicit design constraint. The agent's role is intake, routing, and scheduling, never legal analysis, and it's configured to say so clearly if a caller asks for an opinion on their case. It conducts practice-area-specific intake, personal injury, commercial dispute, family law each ask for different information, checks the practice management system for real-time consultation availability, and books the appointment on the call. For sensitive matters, criminal defence, family law, immigration, we configure the agent to collect only name and contact details and route straight to a human rather than asking detailed questions the caller may not want to answer to an automated system. Everything the agent cannot resolve transfers with a full call transcript and summary, so no enquiry is lost to voicemail.

Yes. Every legal AI engagement starts with a mutual NDA before any documents, workflows, or matter data are shared. We understand that law firms and legal departments handle confidential information for clients across sensitive matters. Data handling, retention, and access controls are scoped in discovery alongside the technical requirements. We have shipped systems for US healthcare clients under HIPAA and for European markets under GDPR, and we apply the same rigour to legal data confidentiality requirements.