Build an AI Assistant for Faster Regulatory Compliance Checks

Attorneys at small firms often rely on manual searches and memory. Using Ctrl+F to find keywords in a 150-page credit agreement is brittle; it misses semantic variations like 'indemnify' versus 'indemnification' and cannot grasp context. A junior associate spending four hours checking an agreement against an internal checklist is prone to fatigue. This manual process is not just slow, it introduces unbillable risk with every document.

Practice management software like Clio or PracticePanther is built for case management, billing, and calendars. These platforms are not designed for deep, programmatic compliance analysis. You can store a vendor agreement in Clio, but the critical task of checking it against CCPA requirements happens outside the system, likely with a printed copy and a highlighter. The core workflow remains unchanged and inefficient.

Larger e-Discovery tools like Relativity are built for massive litigation and are priced accordingly per gigabyte. For a 15-attorney firm that needs to check 50 contracts a month, this is architectural and financial overkill. The real scenario is an M&A team performing due diligence on a 200-page disclosure schedule. An associate spends six hours looking for non-standard employee non-compete clauses and misses one because it used the term 'non-solicitation'.

The structural problem is that off-the-shelf software serves either generic practice management or massive-scale litigation. There is no middle ground for small firms that need a custom engine to check documents against their unique, evolving compliance libraries. These tools cannot connect a firm's internal risk model to an external regulatory feed and run that comparison automatically.

Syntora would begin with a discovery phase to map your exact compliance workflow. We would audit 10-15 sample documents, such as loan agreements or vendor contracts, and the specific regulatory checklists you use, like SOC 2 or HIPAA security rules. This process defines the data extraction targets and builds the initial 'golden set' for testing the AI's accuracy. You receive a technical specification detailing the clause types to be extracted and the logic for flagging deviations.

The core system would be a FastAPI service deployed on AWS Lambda for cost-effective, event-driven processing. When a PDF arrives in a designated AWS S3 bucket, a Lambda function triggers. The function uses Amazon Textract for OCR if needed, then passes the text to the Claude 3 Sonnet API. A carefully crafted prompt instructs Claude to extract specific clauses and compare them semantically against your firm's approved clause library stored in a Supabase Postgres database. This vector-based comparison finds conceptual overlaps, which is critical for legal language, and would complete in under 120 seconds for a 50-page document.

The delivered system provides a simple dashboard where attorneys view the results. Each document receives a compliance score, and flagged clauses are highlighted with a side-by-side comparison against the approved version. An audit trail records every check, and a human-in-the-loop gate requires an attorney to approve the final report. Hosting costs would typically be under $50/month for processing up to 1,000 documents.