We deploy AI in an industry where a model hallucination can cost a client their case. We design secure, verifiable AI systems for lawyers — aligned with the EU AI Act and professional secrecy — from RAG assistants, through document analysis and clause classification, to e-discovery automation.
AI in the legal industry has three specific requirements that typical LLM deployments do not have:
That is why "plugging ChatGPT into the contracts drive" is not enough. A good AI system for a law firm is designed around these three requirements from day one.
Our default architectural pattern is RAG (Retrieval-Augmented Generation): instead of asking the LLM "what do you know about X", we feed it specific passages from your documents and ask for an answer based on them. Every answer carries source citations, so a lawyer can verify in seconds where the model got its conclusion.
The system layers:
A lawyer asks in natural language: "Do we have an opinion on assignment of receivables when the counterparty goes bankrupt?". The system finds the passages, cites the source and flags how current it is. No more keyword-grepping the file server.
The AI reads an incoming draft and flags: unusual clauses, risky wording, deviations from your standard, missing mandatory provisions — with a link to your "model" clause and an approval path for deviations.
Automatic tagging of thousands of contracts — document type, parties, jurisdiction, amounts, dates, arbitration clauses, currency clauses, survival clauses. Portfolio indexing without paralegal drudgery.
An LLM-powered document wizard: a paralegal answers 5–10 questions, the system generates a draft NDA / policy / opinion, and the lawyer only reviews and corrects. First-draft time drops from hours to minutes.
Searching thousands of emails and documents in proceedings — surfacing relevant correspondence, identifying themes, automatic categorisation against the lawyer's query.
Summaries of judgments, regulations and corporate documents in due diligence. The AI extracts key facts, risks and transaction-relevant provisions — with a one-click path back to the original.
Every AI system we deploy goes through classification under the EU AI Act: we determine whether it is minimal, limited or high risk (or, rarely, prohibited). Most law-firm use cases land in "minimal/limited" — and there we implement:
For personal data we cover the GDPR baseline: legal basis, purpose limitation, data minimisation, retention, access rights. Where sensitive data appears (health, criminal records), we add extra encryption and access-control layers.
For professional secrecy, what matters is control over the keys and the whole environment — not where the bytes physically sit. By default AI runs on zero-retention API terms in the EU; if we use an external LLM (Azure OpenAI / Anthropic), it is exclusively under business terms where the provider contractually commits to no retention and no training on your data. For the most sensitive cases we stand up a fully sovereign self-hosted deployment where data never leaves your environment.
AI is rarely a standalone product — most often it is a layer inside a web application or a Word add-in. An AI assistant works best where the lawyer already works. And AI needs data, so we also build the integrations with clause libraries, practice systems and public registers.
By default — no. We work exclusively with business-grade services (Azure OpenAI, Anthropic for Business, Google Vertex AI Enterprise), where the provider contractually commits that your data is not used for model training and is not retained longer than needed to serve the request. For especially sensitive workloads we deploy open-source models (Llama, Mistral, Gemma) fully inside your own infrastructure — nothing leaves your VPC.
Most likely yes. The AI Act phases in between 2024 and 2026 and covers practically everyone deploying AI systems in the EU. Most law-firm use cases fall into the "minimal risk" or "limited risk" categories (with transparency obligations), but there are exceptions — e.g. AI for employee evaluation, credit scoring or biometrics. We classify every deployment with you under the AI Act, document the classification and implement the required measures (transparency, human oversight, logging).
Hallucinations are a real risk — which is why we never build systems where the AI answers legal questions "from memory". All our deployments in substantive areas are built on RAG (Retrieval-Augmented Generation): the LLM only sees an approved corpus (your contracts, clauses, opinions, legislation) and every answer carries citations to its sources. On top of that: a validation layer, full logging, and quality evaluation during the pilot phase with real lawyers.
Yes — multilingual work is our default context. Modern models (GPT-5.x, Claude 4.x, Gemini 2.x) handle European languages very well, including legal and procedural register. We use multilingual embeddings for retrieval, and for specialised tasks (clause classification, OCR of court filings) we fine-tune models or add language-specific tooling as needed.
It depends on scale and scope. A RAG pilot (an assistant over your knowledge base, 50–500 documents) is in the range of €10,000–20,000 net. A production system with multiple users, integrations and custom fine-tuning starts around €35,000. Add LLM running costs (typically €100–1,000+ per month depending on volume). We always present a TCO calculation before the project starts.
That's a good sign. Let's talk about a concrete use case — starting with a pilot that shows AI can be deployed without compromising confidentiality or quality.
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