Implement Custom AI for Due Diligence Research

Many legal compliance departments, particularly in smaller firms, still rely on basic keyword search within their document management systems like iManage Work or SharePoint. While these tools can find every instance of a term like 'indemnification,' they lack the contextual understanding to determine if a specific clause is non-standard or introduces unacceptable risk. This forces legal associates to manually review hundreds of pages of contracts or PDFs from document intake, a slow process prone to human error and inconsistency.

Consider the recurring workflow of contract review where a firm needs to process 50 or more new documents weekly. An associate might spend a full day extracting clauses related to change of control, liability caps, or data privacy obligations, then manually copying these findings into a master spreadsheet. This not only consumes a significant portion of their work week but also leads to varying interpretations of risk across different reviewers, creating compliance vulnerabilities.

Some firms attempt to build internal automation, but this often results in Python scripts siloed across individual developer workstations, lacking centralized code management. Automation distributed as standalone EXEs becomes difficult to update, test, and audit. Without formal code review processes, these tools introduce operational risk and potential compliance gaps, especially when dealing with sensitive client data. Even seemingly simple tasks like email ingestion for document routing can suffer from pagination bugs in scrapers, missing critical updates.

Furthermore, while enterprise eDiscovery platforms like Relativity offer advanced features, they are designed for massive-scale litigation rather than the continuous, moderate-volume workflows of weekly due diligence or contract review. The overhead of setting up a new matter, the need for certified administrators, and the per-gigabyte pricing models make them prohibitively expensive and inefficient for a team needing quick risk summaries. There remains a significant gap between basic search and overly complex, expensive litigation tools for specialized legal teams requiring customized, context-aware analysis.

An engagement for AI-assisted legal compliance would begin with a thorough audit of your firm’s existing document management systems, data sources (e.g., SQL Server), and a representative sample of historical legal documents. Syntora would collaborate closely with your attorneys and compliance experts to precisely codify your firm’s specific risk factors, non-standard terms, and clause library into a machine-readable schema. This discovery phase culminates in a detailed technical specification outlining the integration plan and data processing workflow.

The technical architecture would typically involve a FastAPI service deployed within your AWS environment, acting as the core automation engine. This service would integrate with your document intake points, whether through webhooks from a modern DMS, ingesting PDFs from an AWS S3 bucket, or custom adaptors using Selenium for legacy system interfaces. Documents would be securely processed using the Claude API, leveraging its large context window to accurately parse lengthy legal contracts. The Claude API would extract relevant clauses, classify them against your firm's defined risk schema, and assign a confidence score.

Results, including the extracted clause, its classification, confidence score, and contextual links, would be stored in a Supabase PostgreSQL database, providing a permanent, searchable audit trail for every AI decision. The delivered system would include a secure web application that acts as a human-in-the-loop interface, allowing your attorneys to review flagged items, verify classifications, and override decisions before any action is taken. This ensures attorney oversight and maintains compliance.

Syntora would establish a robust code management framework using GitHub, complete with GitHub Actions for CI/CD, and CODEOWNERS-style required reviewer gates. This prevents siloed scripts, standardizes automation, and enforces formal code review, mitigating the compliance risks associated with unmanaged developer workstations. All systems would operate within your client infrastructure, with data remaining behind Okta MFA, ensuring data security and regulatory compliance. We have built document processing pipelines using Claude API for financial documents, and the same pattern applies to legal documents for clause extraction and classification. For a system of this complexity, integrating with existing firm infrastructure, a typical build and deployment could range from 12-20 weeks, dependent on initial client-provided data and integration points.