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CASE STUDIES

How We Built a Legal Document Intelligence System for 100K+ Docs

Architecture, challenges, and lessons learned from building an end-to-end legal AI solution.

TB
Team Botmartz
AI Engineer, Botmartz · March 28, 2024 · 8 min read
◈ Open in Google Colab⌥ View Code on GitHub
Read Time
8 min
Failure Modes
5
Code Snippets
3
Runnable Notebook
1
Botmartz AI Insight
Evaluating Retrieval, Chunking, and Generation in Production
Retrieval QualityChunking StrategyHybrid SearchContinuous EvaluationGuardrailsTakeaways
# How We Built a Legal Document Intelligence System for 100K+ Docs Processing large volumes of legal documents requires absolute precision. In this case study, we walk through the design of our Document Intelligence system, built to analyze over 100,000 corporate agreements. ## High-Precision OCR & Processing We combined hybrid OCR strategies with structured extraction models to parse scans and contract clauses. Security and compliance were handled by isolating LLM contexts within dedicated enterprise tenants.

Closing Takeaways

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Measure retrieval precision and recall in isolation before touching the model.
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Chunk along document structure, not arbitrary character counts.
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Combine vector and keyword search — hybrid retrieval beats either alone.
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Treat evaluation as continuous infrastructure, not a launch-week report.
Try It Yourself
A runnable Google Colab notebook with the eval harness and hybrid search code from this post.
◈ Open in Colab⌥ GitHub Repo
#Enterprise RAG#Evaluation#Production AI#LangChain
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TB
Team Botmartz
AI Engineer at Botmartz, building enterprise RAG and agent systems in production. Contributing to open-source libraries.

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