Find Hidden Legal Risks in Contracts with Custom AI

Many firms start with contract management software like Ironclad or ContractWorks. These platforms are excellent for tracking key dates and organizing documents, but their AI features are often limited to keyword search. They can find an indemnity clause but cannot analyze its language to see if it deviates from your firm’s approved text. The logic is too rigid for nuanced legal review.

A paralegal at a growing business might try a Microsoft Word add-in that promises AI review. The problem is these tools use a generic model trained on public data. It might flag a clause as 'uncommon' but lacks the context of your business's specific risk profile. For a medical device company, a standard limitation of liability clause might be flagged as risky, creating false positives and wasting an attorney's time.

This forces a manual process. A new vendor agreement arrives. The add-in flags five clauses. A paralegal then has to open the firm's clause library in a separate document, manually compare each flagged clause line-by-line, and then search past contracts to see what variations were accepted before. The 'AI' tool added a step instead of removing one.

Syntora approaches contract risk identification as a custom engineering engagement. The process would begin with a discovery phase to understand your firm's specific legal playbook, risk tolerance, and existing document workflows.

The first step in building the system would involve ingesting your firm's standard clause library and a representative set of historical contracts into a secure cloud storage, such as an AWS S3 bucket. A custom FastAPI service would be developed to handle Optical Character Recognition (OCR) for incoming PDFs. This service would also classify each document into relevant matter types, such as 'Vendor MSA' or 'Commercial Lease', to ensure the appropriate risk rules are applied.

For each new contract, the Claude API would be used to extract and categorize individual clauses. These extracted clauses would then be converted into vector embeddings and compared against your approved clauses, which would be stored in a Supabase vector database. This semantic comparison capability allows the system to identify conceptually similar clauses even when the wording differs. Clauses falling below a predefined similarity threshold would be flagged for review.

The delivered system would expose a simple review interface. An attorney would see the original flagged clause, a corresponding standard version from your library, and a clear indication of potential risk. This approach ensures a human-in-the-loop gate, where no contract is approved or rejected without an attorney's explicit sign-off. Every AI suggestion, confidence score, and final human decision would be logged in Supabase, establishing a permanent audit trail.

The system architecture would be designed for scalability and minimal operational overhead, often leveraging serverless technologies like AWS Lambda for processing and orchestration. Integration points, such as dedicated email inboxes for new contracts or notifications via Slack, would be tailored to your firm's operational needs. Importantly, all data processing and storage would occur within your firm's own cloud infrastructure, maintaining data security and compliance.