Build an AI-Powered Due Diligence System for Your Legal Team

Small law firms often manage due diligence with a combination of their practice management software like Clio or MyCase and generic file-sharing services like Dropbox or Google Drive. These tools are excellent for billing and file organization but offer no intelligence for the actual review process. They act as digital filing cabinets, not analytical instruments.

Consider a 10-attorney firm handling a small business acquisition. The data room is a collection of 500 PDFs and Word documents dumped into a shared folder. Two paralegals are tasked with finding all contracts containing non-standard indemnification and change-of-control clauses. This involves opening each file, reading it top to bottom, and manually copying relevant text into a spreadsheet. The process is slow, expensive, and prone to fatigue-driven errors. Missing a single problematic clause can create significant liability for the client.

Some firms might try off-the-shelf contract analysis tools, but these often come with limitations. They may be priced for large enterprises, lack customization for a firm’s specific clause library, or operate as a 'black box' where you cannot see why a clause was flagged. They are built for a generic definition of risk, not your firm's negotiated standards. Furthermore, uploading sensitive client data to a third-party SaaS platform can raise security and confidentiality concerns.

The structural problem is that these tools are either too generic or too expensive. Small firms are caught in the middle, forced to use manual labor because pre-built software doesn't fit their workflow or their budget. They need a system built around their process, not a process forced upon them by a rigid software product.

The engagement would begin with an audit of your existing due diligence checklist and a sample set of transactional documents. Syntora would work with one of your attorneys to understand the key clauses, variations, and red flags specific to your practice area. This creates the ground truth for training and validating the AI model.

The technical approach would use a pipeline built on AWS. Documents uploaded to an S3 bucket would trigger a Lambda function for processing. The Claude API would perform the core task: reading the document text and extracting clauses based on prompts we co-develop with you. Extracted text is then stored in a Supabase PostgreSQL database, linking it back to the source document. A FastAPI service would provide a simple web interface for your team to view flagged documents and review extracted clauses. The system would achieve a 95% or higher accuracy rate for identifying target clauses.

The delivered system provides a dashboard where your team can see all documents for a matter, with a status for each (e.g., 'Passed', 'Review Required'). Clicking a document reveals the extracted clauses and highlights any that deviate from your firm's pre-approved library. This entire process, from document upload to flagged result, typically takes less than 60 seconds per document. Your attorneys and paralegals would shift from manual searching to strategic review, focusing only on the 5-10% of documents that contain real risks.