Automate Contract Review for Your Law Firm with Compliant AI
Law firms use AI to extract clauses and flag non-standard terms against a firm's internal library. Compliance is managed with audit trails and human-in-the-loop gates before any action is taken.
The system's complexity depends on the number of contract types and the variation in your standard clauses. A firm reviewing a single, standardized agreement like a commercial lease is a direct build. A firm handling multiple M&A document types with complex dependencies requires more intricate logic. Our approach ensures all privileged documents remain on your private infrastructure.
We built this system for an 8-person real estate firm. They were spending 45 minutes of paralegal time per lease agreement. The system we deployed now processes each agreement in 90 seconds, flagging only the specific clauses that require attorney review. It runs entirely within their own AWS account.
What Problem Does This Solve?
Many firms first look at off-the-shelf Contract Lifecycle Management (CLM) software. These platforms are built for legal departments with hundreds of attorneys and come with enterprise-level pricing, often with mandatory per-seat licenses. Their AI is a black box, offering no way to tune clause detection to your firm’s specific language or risk tolerance. Crucially, they often require you to upload client documents to their cloud, creating serious compliance and privilege issues.
A common next step is experimenting with general document AI tools. Using a public API like OpenAI's for client work is a non-starter due to data privacy policies. Even with private models, the core problem remains: they are not trained on your firm's unique playbook. This creates a flood of false positives. We saw a 12-person firm try this for reviewing vendor agreements; the AI flagged 80% of the clauses in their own paper as 'non-standard' because it was comparing them to a generic, public template.
These approaches fail because they treat legal document review as a generic text analysis problem. They lack the built-in audit trails, human review gates, and on-premise data security that law firms require. The time spent dismissing irrelevant AI-generated flags ends up being more work than a manual review.
How Does It Work?
First, we codify your firm's expertise. You provide 50-100 examples of your gold-standard contracts. We ingest these into a Supabase Postgres database, creating a dedicated clause library with vector embeddings for semantic search. This library, hosted in your cloud account, becomes the single source of truth for every automated review.
Next, we build a FastAPI service to manage the workflow. When a contract arrives as a PDF email attachment, an AWS Lambda function saves it to your private AWS S3 bucket. The function uses Amazon Textract for OCR and sends the clean text to the Claude API with a prompt to extract key clauses as structured JSON. For a typical 20-page agreement, this entire intake and extraction process completes in under 30 seconds.
The service then compares each extracted clause against your Supabase library. We use a hybrid search to find the closest approved clause. If a clause deviates by more than a 15% semantic threshold or is missing, it gets flagged. The system compiles a summary report showing every flagged item, the AI's confidence score, and a link to the original document. This report is sent directly to the responsible attorney.
The final piece is a simple review interface built with Vercel. It provides a side-by-side view of the flagged clause and your firm's standard language. The attorney makes the final call with a single click to approve or reject the deviation. Every decision is logged in a permanent audit trail in Supabase, creating a defensible record of the review process. The total time from document receipt to an attorney's final approval is often less than 90 seconds.
What Are the Key Benefits?
Review a 20-Page Lease in 90 Seconds
Free your paralegals from manual clause comparison. Our system extracts, compares, and flags deviations in under two minutes, turning 45-minute reviews into quick approvals.
No Per-Seat Fees, Just Cloud Costs
Avoid expensive CLM software subscriptions. After our one-time build, your only recurring cost is for cloud usage, typically under $50 per month on AWS for processing hundreds of documents.
You Own The Code And Clause Library
The entire system is deployed in your AWS account and the code is delivered to your GitHub. Your firm's intellectual property, your clause library, remains yours permanently.
Every AI Decision Has an Audit Trail
We log every clause extraction and comparison with a confidence score. The human-in-the-loop gate ensures an attorney signs off on every flagged item, creating a defensible review process.
Connects Directly to Your Email Intake
No new software for your team to learn. Contracts arrive as email attachments, and review summaries are sent back to the assigned attorney's inbox automatically.
What Does the Process Look Like?
Clause Library Build (Week 1)
You provide access to 50-100 anonymized, exemplary contracts. We extract your standard clauses and build the core comparison library in your new Supabase instance.
Core Automation Build (Weeks 2-3)
We develop the FastAPI service and AWS Lambda functions for document intake, OCR, and Claude API integration. You receive a link to the staging environment for initial testing.
Review & Deployment (Week 4)
We connect the system to your live email intake and deploy the Vercel review interface. Your team reviews the first 25 live documents with our direct support.
Monitoring & Handoff (Weeks 5-8)
We monitor system performance and AI accuracy for 30 days post-launch. You receive a full runbook with architectural diagrams and instructions for maintenance.
Frequently Asked Questions
- What determines the cost and timeline for a project like this?
- The primary factors are the number of contract types and the complexity of your clause library. A firm reviewing one standard agreement type, like commercial leases, is a 4-week build. A firm that needs to process 10 different types of agreements with nested logic requires more development. We scope this during our discovery call.
- What happens if the AI misinterprets a clause or the system goes down?
- Every AI extraction includes a confidence score. If it's below our 95% threshold, the document is automatically flagged for full manual review. The system runs on AWS Lambda, which is highly available. If an API call fails, it retries three times before flagging for manual intervention. You get an immediate email alert.
- How is this different from using a large language model like ChatGPT Plus?
- Sending client contracts to a public service like ChatGPT violates confidentiality and attorney-client privilege. Our system uses the Claude API via a zero-retention agreement and runs within your own secure cloud environment. Your data is never used for training third-party models, and you maintain a clear chain of custody.
- Can the system handle handwritten notes or poor-quality scans?
- Yes. We use Amazon Textract for OCR, which is very effective at handling scanned documents and even low-resolution PDFs. While it can extract some printed text from forms with handwritten notes, it cannot reliably interpret handwriting. Documents with significant handwritten markups are flagged for manual review.
- How does the system get updated as our standard clauses change?
- Your clause library is stored in a Supabase database with a simple interface. Your designated admin can add, remove, or edit standard clauses without any code. The changes are reflected in the review process instantly. We provide a short training session and documentation on how to manage the library.
- Does our firm need a technical person on staff to manage this?
- No. The system is designed to be self-sufficient with automated monitoring and alerts. For the first 90 days, we manage all updates and fixes. After that, we offer an optional monthly support plan that covers monitoring, updates, and minor adjustments. The handoff documentation covers all common operational tasks.
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