A Series A legaltech company needed to scale their contract analysis platform beyond rule-based systems. We built a custom LLM-powered review pipeline that analyses contracts for risk clauses, compliance issues, and key terms extraction across both French and English legal documents.
The client, a Series A legaltech startup based in central Paris, had built their initial contract analysis platform on a foundation of hand-crafted rules and regular expressions. While this approach worked for a narrow set of standardised contracts, it was collapsing under the weight of real-world legal document diversity.
Their existing system could not handle the sheer variety of contract structures encountered across industries. Each new client onboarding required weeks of manual rule configuration, and accuracy was declining as document volume increased. Contracts were taking an average of 45 minutes each for manual review, and the growing client base was demanding significantly faster turnaround times.
"We had pushed our rule-based system as far as it could go. Every new contract type meant weeks of engineering work to add new rules, and we were still missing critical clauses. We needed AI that truly understands legal nuance, not just pattern matching."
CTO, Paris Legaltech StartupThe startup had already attempted to integrate a generic document processing API, but found that off-the-shelf solutions lacked the legal domain specificity needed for reliable clause extraction. Risk clauses were frequently misclassified, and the system could not differentiate between boilerplate language and genuinely problematic terms.
With their Series A runway in play and enterprise clients expecting faster, more accurate results, the team needed a purpose-built AI solution that could handle the complexity of real-world legal document analysis at scale.
We began with a deep-dive analysis of over 500 contract types from the client's existing corpus. Our legal AI specialists mapped the full taxonomy of risk clauses across French civil law and English common law traditions, identifying 47 distinct extraction patterns and 12 critical risk categories. This groundwork ensured the model would understand the structural and semantic differences between jurisdictions from day one.
Using the domain assessment as our foundation, we fine-tuned a large language model on the client's proprietary legal corpus covering both French and English contract law. We developed custom prompt engineering templates for each contract category, optimising for clause-level extraction accuracy. The fine-tuning process included adversarial testing with edge-case contracts to harden the model against ambiguous or unusual phrasing.
We built a retrieval-augmented generation pipeline that connects the LLM to the client's legal precedent database and a curated knowledge base of French and EU regulatory requirements. This enables contextual clause analysis, so the model doesn't just extract terms in isolation but understands them in relation to applicable legal standards. The RAG architecture also provides citation-backed explanations for every flagged risk, giving reviewers confidence in the AI's reasoning.
We designed and built a reviewer dashboard that surfaces AI-generated insights in a clear, actionable format. Each contract review includes confidence scores per clause, colour-coded risk highlights, extracted key terms with definitions, and one-click approval or escalation workflows. The interface integrates directly into the client's existing platform via API, so their end users experience faster results without any workflow disruption.
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