✦ CASE STUDY

Synapse

A custom NLP pipeline that automated contract review for a legal tech company — 90% accuracy, 60% faster turnaround.

ClientLexAI (LegalTech)

Timeline14 weeks

RoleEnd-to-end custom build

StackPython · spaCy · Transformers · FastAPI

The Challenge

LexAI had a team of 15 paralegals manually reviewing 500+ contracts a week. Each contract took 2-3 hours. Burnout was high, errors slipped through, and they couldn't scale to the demand their sales team was generating.

Off-the-shelf solutions didn't work — legal language is too domain-specific, and their clients required clause-level accuracy that generic models couldn't deliver.

Our Solution

We built Synapse — a completely custom NLP pipeline trained on their contract corpus:

Fine-tuned transformer model on 10K+ labeled legal documents (NDAs, MSAs, SOWs)
Clause classification — identified 40+ clause types with 94% precision
Risk scoring — flagged high-risk clauses (indemnification, liability caps, auto-renewal)
Interactive review UI — paralegals could approve, reject, or edit AI suggestions in one click

Tech Stack

Python spaCy Hugging Face FastAPI React PostgreSQL Docker AWS ECS

The Results

90%Overall accuracy

60%Faster review time

94%Clause precision

500+Contracts/week processed

What the client said

We looked at every contract review tool on the market. None of them understood legal language the way we needed. Synapse was built for us, and it shows.

— Marcus Webb, CEO, LexAI

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