Legal · AI POC✓ POC Deployed
NDA Analysis for a Law Firm
Redeployed the NDA pipeline with a custom preferred positions playbook for a law firm — demonstrating that the system's architecture cleanly separates pipeline logic from client-specific legal standards.
The Problem
A law firm approached wanting to understand what AI-assisted NDA review could look like for their practice. Their standard language and acceptable counterparty positions were materially different from the PE firm's — different clause definitions, different governing law preferences, different tolerance thresholds.
The question was whether the system could be adapted without rebuilding from scratch.
The Solution
The same multi-step RAG pipeline was redeployed with a fully reconfigured preferred positions playbook tailored to the law firm's standard NDA language. No changes to the pipeline architecture — just a new configuration layer. The system adapted in the same way a lawyer would: same analytical process, different standards to measure against.
Outcome
The system ran successfully against the firm's NDA samples with the custom configuration. The engagement demonstrated a key architectural advantage: the preferred positions playbook is fully decoupled from the pipeline — the same system can serve clients with entirely different legal standards by swapping the configuration, not the code.
Tech Stack
LLMGPT-4o
FrameworkLangChain
Vector DBChromaDB
BackendFastAPI + Azure Functions
OutputClause-level analysis & redlines
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