AI for law firms. Faster case work, with confidentiality intact.

Most law firms run a combination of Kluwer Navigator or Lexis for case law, a document management system for files, and a case-management package such as Clio or in-house middleware. These suites do their core job well, but once you want AI features (document review across hundreds of contracts, semantic search over your own precedents, a chatbot for client questions), you run into standard templates that aren't set up for your firm.

M&A practices need due-diligence tooling that flags critical clauses across hundreds of contracts, family law calls for case-file summarisation with privacy safeguards, tax lawyers want regulatory monitoring tailored to each client portfolio, and in-house teams need contract-review workflows that integrate with their ERP and CLM. None of these workflows fits an out-of-the-box package.

At the same time, the market is under pressure. Commodity suites such as Harvey AI, Spellbook, Robin AI and Eve are marketed as revolutionary, but their output is standardised: the same model, the same prompts and the same benchmark playbook at every firm that subscribes. The distinctive strength of *your* firm, its particular clause style, its own precedent bank and its accumulated expertise in a niche segment, doesn't come through in such a suite. What remains is a generic layer that at best speeds up junior work without making the firm any stronger strategically.

We build AI applications that slot into your existing stack: private LLM deployment for sensitive case work, RAG systems that unlock your own knowledge base, and always with source citations so that hallucination is not a risk. We are not replacing Kluwer or Lexis; we build the custom layer on top that sets your specialism apart. For some firms that means focusing on a single use case, such as a document configurator that generates contracts from a question tree. For others it is a broader AI platform supporting several practice groups.

The choice of language model is not a cosmetic detail: it determines whether the solution fits your type of work. For long-context case files, such as a 500-page deed or a complete litigation file, we typically use Claude because of its 200K-token context: the model reads an entire file in one go and delivers a structured summary without us having to chunk it artificially. For broad contract-review flows with rapid iteration we deploy GPT-4o, which is on balance a little faster on shorter documents. And for firms with the strictest privacy requirements, such as trust offices, M&A boutiques with government clients, or lawyers handling state-secrets matters, we deploy Llama 3.1 fully on-premise or in a private VPC. No outbound API connections, with auditable model weights.

The use cases we build most often, grouped: document review and clause extraction for due diligence, RFP analysis and contract renewal cycles. Case file summarisation for litigation teams taking over a file or preparing a conclusion. Semantic search over case law: not just keyword searching on rechtspraak.nl or EUR-Lex, but conceptual searching, for example: "rulings in which an extension clause in a lease agreement was interpreted in the tenant's favour under ROZ conditions". Client enquiry triage via a chatbot front-end on your website that handles first-line questions and delivers the lawyer a structured summary for the intake meeting. Legislative monitoring that scans the Staatscourant, the EU Official Journal and case law for relevance per client or case, with no more manual alerts.

In addition, we build compliance mapping for client portfolios (GDPR, Wwft, AI Act, CSRD), KYC/Wwft screening for client onboarding and, for firms that advise clients on AI compliance themselves, AI bias audit tooling that the firm uses to assess its clients' applications. Time-recording automation is an underestimated gain: an AI that categorises email traffic, calendar entries and document edits against open cases significantly reduces the administrative burden per lawyer, especially in firms under pressure from billable-hour targets.

Regardless of the use case, the approach is the same: a RAG layer that connects to your own knowledge base, an EU-resident deployment of the model, an audit log that retains every model call with its input and output for review, and a human-in-the-loop design in which a lawyer always has the final check before any output reaches a client.