AI for law firms and legal software development

AI for law firms: where it helps first

Law firm work is reading work: discovery sets, intake forms and every email in the shared inbox. AI earns its place where the reading volume is high and a lawyer can check every output before it matters.

Three places usually come first:

• First-pass document review: sorting documents by relevance and flagging likely privilege, so attorneys start with the documents that need them
• Matter intake: turning an intake form or email into a structured summary with parties, dates and the conflicts to check
• Inbox triage: classifying incoming mail by matter, deadline and type, and alerting the right person when something is urgent

AI should not give legal advice, and it should not make a privilege call that no attorney has seen. We design every legal tool so a lawyer signs off the decisions that carry risk.

AI legal document review with attorney override

Document review is where legal AI pays for itself or fails in front of a judge. The pattern we build has four parts. A classifier marks each document for responsiveness and privilege and attaches a confidence score. Low-confidence and privileged documents go to an attorney queue instead of being decided automatically. Redactions are proposed by the system and approved by an attorney. Every decision, whether made by the model or overridden by a person, is written to an audit trail, so the review can be explained later.

DecoverAI's platform, which we helped build, works this way: AI classification with confidence scores, attorney override on every AI decision, automated redaction, Bates numbering and privilege logs.

eDiscovery platforms and the infrastructure behind them

Large matters break naive AI systems. A production can mean hundreds of thousands of documents arriving at once, in mixed formats, under a deadline. We build ingestion as queues so documents are processed in the background and nothing is dropped, semantic search with a vector database so reviewers find documents by meaning instead of exact words, and retrieval-augmented generation (RAG) so answers cite the documents they came from. For DecoverAI we built the infrastructure on AWS EKS with Pinecone for vector search, sized for matters of more than a million documents.

Matter intake and legal inbox triage

Shared firm inboxes such as intake@ and billing@ are where deadlines get missed. TriageGrid, our own product, connects to Microsoft 365 and classifies each incoming Outlook email into categories such as intake, court deadlines, billing and conflicts. It then runs the action chain the firm chose for that category: tag, move, forward, reply or flag. Urgent items raise an alert in Teams or Slack, and one-click corrections from attorneys teach it the firm's own categories.

When a firm needs its own matter types, filing into its document management system or a stricter data policy, we build a custom version inside the firm's own cloud account.

Confidentiality and privilege by design

Client files are the reason firms hesitate to use AI. We design around that:

• Documents stay in your own cloud account, in the region you choose
• Data is encrypted at rest, and access to each matter is limited by role
• Where documents cannot go to an outside model API at all, we run an open-weight model inside your account (see private LLM)
• Every AI decision and every human override is logged with who, what and when
• Your matter data is kept out of shared prompts, examples and other clients' systems

Professional responsibility stays with the firm; we build to the controls your general counsel and IT team set.

Legal AI solutions: buy, build or both

Ready-made legal AI products suit standard tasks such as drafting help or research, and many firms should start there. Building makes sense when your workflow, data or confidentiality rules do not fit a product: your own review categories, an intake flow tied to your practice areas, or documents that never leave your servers. Often the answer is both, and the free assessment tells you which.

Legal software development for legal tech companies

Legal tech startups need engineers who understand both the product and the stakes. We build MVPs from $5,000 and AI platforms from $8,000, then stay on as a monthly team, as we did for 13 months with DecoverAI.

What AI for a law firm costs

Legal AI projects start from $8,000, and a first automated workflow, such as inbox triage for one shared mailbox, from $5,000. Large document volumes, private model hosting and integrations with your document management system move the price up. Model and cloud costs are billed to your own accounts. You get a fixed quote in writing after discovery. See pricing.