Connecting Contracts with the Negotiation Context Behind Every Clause
Contracts rarely tell the full story. Critical decisions, approvals, and negotiation logic are often buried across emails, attachments, and communication threads. Red Buffer built an AI-powered contract intelligence platform that links agreements with the context that shaped them.
Outcome
An AI contract intelligence platform that links contracts with negotiation emails, attachments, and contextual discussions using semantic search and LLM-driven relevance analysis.
Linked clauses directly with negotiation discussions, approvals, and supporting email threads.
Semantic Search Across Large Communication Sets
Enabled rapid retrieval of relevant negotiation context using embeddings and vector search.
Accelerated Legal Review
Reduced manual effort required to trace contract lineage and historical decision making.
Scalable AI Infrastructure
Distributed processing pipelines supported large contracts and extensive communication datasets efficiently.
ROLE
AI retrieval architecture, clause intelligence pipeline development, semantic search workflows, embedding infrastructure, and scalable backend engineering.
TOOL
FastAPI, Next.js, PostgreSQL with pgvector, Redis, AWS Elastic Beanstalk, LLMs, Embedding Pipelines, Vector Search
DURATION
Multi-phase product development with ongoing scalability and feature enhancements.
Our Approach
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Contract Upload and Clause Extraction
Built AI-powered document ingestion workflows that extract and interpret clauses from uploaded contracts using advanced LLM models.
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Email and Negotiation Context Integration
Integrated securely with Gmail and Outlook using OAuth 2.0 to retrieve negotiation threads, attachments, and related communications connected to each agreement.
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Semantic Search and Relevance Verification
Processed emails through embedding pipelines stored in pgvector and implemented an LLM-based relevance layer to match discussions directly with related contract clauses.
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Scalable Distributed Processing
Engineered distributed execution pipelines to process lengthy contracts, generate embeddings, and perform clause-to-context matching efficiently at enterprise scale.
Why It Matters
This project addressed a major LegalTech challenge by connecting finalized agreements with the negotiation history behind them. Combining clause extraction, semantic retrieval, and LLM relevance verification transformed fragmented communications into searchable legal intelligence.
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