Automate Document Review for Your Litigation Firm
AI automation uses large language models to extract key data from legal documents instantly. This reduces manual review time from nearly an hour to under two minutes per document.
Syntora specializes in building custom AI automation systems for legal document review. These systems leverage large language models to extract key data from legal documents, significantly reducing manual review time for small litigation firms. Syntora focuses on delivering tailored engineering engagements, not off-the-shelf products.
Syntora designs and builds custom AI systems tailored to your firm's specific needs. A typical system handles discovery documents, contracts, and client intake forms, extracting clauses, classifying matter types, and flagging non-standard terms for attorney review. The complexity of developing such a system depends on the number of document types, which can range from 5 to over 20 for a typical firm, and the specific data points required from each. We have experience building similar document processing pipelines using Claude API for financial documents, and the same robust pattern applies to legal documents.
What Problem Does This Solve?
Small firms often try off-the-shelf legal tech like Clio or PracticePanther for case management, but their automation is limited to simple templates and reminders. They cannot analyze unstructured PDF content. Some attempt to use generic OCR tools like Adobe Scan, but these just convert images to text. They do not classify documents or extract specific clauses, leaving paralegals to manually copy and paste information.
A 12-person litigation firm receives 50-100 discovery documents daily via email as PDFs. A paralegal opens each email, downloads the PDF, opens it, identifies it as a 'Medical Record' or 'Deposition Transcript', renames the file according to a strict convention, and moves it to a specific folder. This takes 5-7 minutes per document, consuming over 4 hours of their day.
These manual workflows are brittle and error-prone. A single misclassified document can delay a case filing. The core issue is that generic tools cannot understand legal context. They lack the specialized logic to differentiate a motion to compel from a motion to dismiss, or to extract specific indemnification clauses from a 30-page contract.
How Would Syntora Approach This?
Syntora approaches legal document automation as an engineering engagement, starting with a discovery phase to understand your firm's unique document types, required extraction points, and workflow for human review. Based on this, we would design and build a custom system.
The core architecture would involve:
An AWS S3 bucket to receive incoming documents. An AWS Lambda function would trigger on each new upload, initiating document processing. We would use Amazon Textract for optical character recognition (OCR) to convert scanned PDFs into searchable text. Both the raw text and original document would be stored in a Supabase table with a unique identifier, ensuring data integrity and traceability.
A FastAPI service would then process the extracted text. This service would send the text to the Claude API, utilizing carefully engineered prompts to classify documents into your firm's specific matter types and extract key entities such as case numbers, party names, and critical dates. For contracts, the system would identify clauses and compare them against your firm's approved library, highlighting deviations for attorney review. We would configure classification and flagging thresholds based on your risk tolerance.
All processing results would be written back to the Supabase database. Documents flagged due to non-standard clauses or classifications below a set confidence threshold would be routed to a human review queue within the system. Once approved, documents could be automatically renamed and filed according to your firm's conventions. An email summary, including a link to the document and extracted key points, could be sent to the assigned attorney.
A critical component of the delivered system would be a comprehensive audit trail, logged within Supabase. This trail would include the prompt used, the raw API response, and the system's confidence score for every decision, supporting transparency and compliance. We prioritize data privacy; the Claude API processes data in-memory and does not retain client data. The entire system would run within your firm's own cloud infrastructure, ensuring privileged documents remain under your control.
Typical build timelines for a system of this complexity range from 6 to 10 weeks, depending on the number of document types and custom logic required. Your firm would need to provide sample documents, define classification categories, and outline current review workflows. Deliverables would include the deployed cloud infrastructure, source code, and documentation. Hosting costs are designed to scale efficiently, with typical operational expenses for a firm processing several thousand documents monthly being nominal.
What Are the Key Benefits?
From 45 Minutes to 90 Seconds Per Document
Reduce paralegal review time by over 98%. A four-hour daily task becomes a 10-minute oversight role, freeing up staff for billable work.
Fixed Build Cost, Not Per-User SaaS Fees
A one-time project fee covers development. After launch, you only pay for cloud usage, typically under $50 per month for hosting and API calls.
You Get The Keys to The System
We deliver the full Python source code in your private GitHub repository and a detailed runbook for maintenance. You are not locked into a vendor.
Every AI Decision is Logged
An immutable audit trail in Supabase logs every classification and extraction with its confidence score, ensuring full transparency and defensibility.
Integrates with Your Existing Email
The system ingests documents directly from a dedicated inbox. No changes to your firm's workflow or need to train staff on new software.
What Does the Process Look Like?
Document Workflow Audit (Week 1)
You provide sample documents (10-15 of each type) and walk us through your current manual review process. We deliver a detailed system design document.
Core AI Engine Build (Weeks 2-3)
We build the FastAPI service, prompt chains for the Claude API, and the Supabase database schema. You receive access to a staging environment to test classification accuracy.
Integration and Deployment (Week 4)
We deploy the system on AWS, connect it to your email intake, and configure the human-in-the-loop review queue. You receive admin credentials and training.
Monitoring and Handoff (Weeks 5-8)
We monitor the system in production for 30 days, fine-tuning prompts and logic. You receive the complete source code repository and system runbook.
Frequently Asked Questions
- How much does a custom document review system cost?
- Pricing depends on the number of distinct document types and the complexity of data to be extracted. A system for classifying 10 document types is a smaller project than one that extracts and compares 30 different clauses. We provide a fixed-price quote after the initial discovery call. Book a discovery call at cal.com/syntora/discover.
- What happens if the AI misclassifies a document?
- The system is designed with a human-in-the-loop gate. Any classification with a confidence score below a set threshold (typically 90%) is flagged for manual attorney review. The audit trail logs every decision, making it easy to spot and correct errors. Over time, these corrections help us fine-tune the model prompts.
- How is this different from hiring a Virtual Legal Assistant?
- A virtual assistant follows a checklist, but an AI system understands context. A VA costs you per hour, forever. This system is a one-time build that processes documents 24/7 for pennies per file. It also handles volume spikes instantly, whereas a VA has fixed capacity. You replace a recurring operational expense with a capital asset.
- What kind of security is in place for sensitive client data?
- We deploy the entire system within your own cloud environment. Your documents are stored in your AWS S3 bucket, and the database runs in your Supabase project. We use APIs like Claude that have a zero-retention policy for business data. Privileged information never resides on our servers or third-party AI training sets.
- What are the ongoing maintenance requirements?
- The system is built for minimal maintenance. We set up automated health checks and alerts. The primary reason for updates is a change in your firm's document types or classification rules. We provide a runbook for your team or offer a simple monthly retainer for ongoing support and enhancements.
- How long does the AI model take to learn our documents?
- We use pre-trained large language models, which already understand legal language. We do not train a model from scratch. Instead, we use a technique called prompt engineering with a few examples. This means the system can be highly accurate with as few as 5-10 samples of each document type from day one.
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