Get a Custom AI System for Legal Document Review

Most law firms run on practice management systems like Clio or MyCase and store documents in platforms like iManage or NetDocuments. These are excellent systems of record, but they are not process automation engines. They can store document templates, but they cannot read or understand the content of a PDF that arrives in an email. This leaves paralegals and junior associates to perform high-volume, low-value manual work.

Consider a common workflow: a paralegal at a 15-person firm processes 700 documents a month. For each one, they must open an email attachment, identify the matter, rename the PDF according to firm convention, and upload it to the correct folder in Clio. For a contract review, a junior associate then spends 20 minutes comparing indemnification and liability clauses against a master Word document. This cycle of manual triage and review consumes over 15 minutes per document, introducing risk and delaying response times.

The structural problem is that these platforms were built for storage and organization, not for computation. Their internal architecture cannot run inference against a large language model like Claude. Off-the-shelf OCR tools can digitize text, but they fail to extract structured legal concepts or compare nuanced clause language against a firm's established standards. You are left with a gap that can only be filled by expensive human review.

The project would begin with an audit of your current document workflow. Syntora would map the journey of 3-5 core document types, from email arrival to final storage. We would work with your team to digitize your firm's clause library and standard term playbooks. This discovery phase produces a clear specification for the AI's extraction targets and your firm's business rules.

The system would be a FastAPI service that monitors a dedicated AWS S3 bucket for new documents. When a PDF arrives, an OCR function extracts the text, which is then sent to the Claude API. A carefully engineered prompt instructs the model to classify the document, extract key entities, and flag any clauses that deviate from your approved language. Extracted data and a complete audit log for every action are stored in a Supabase database. All components run on your firm's private cloud infrastructure.

The final deliverable is a simple dashboard for attorneys to review processed documents. Each item in the queue includes a summary, a list of non-standard clauses highlighted for review, and a direct link to the correct matter file in your practice management system. We've built similar document processing pipelines for financial services using this exact stack, and the pattern directly applies to legal document analysis.