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CASE STUDIES

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

An inside look at our architecture for parsing, indexing, and reasoning over complex legal contracts and filings.

BET
Botmartz Engineering Team
AI Engineer, Botmartz · April 5, 2024 · 14 min read
◈ Open in Google Colab⌥ View Code on GitHub
Read Time
14 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 Legal documents present unique AI challenges: dense terminology, multi-page cross-references, scanned PDF layouts, and strict accuracy mandates. ## System Blueprint - **Layout-Aware Optical Character Recognition (OCR)**: Preserving table hierarchies and footnotes during ingestion. - **Hierarchical Indexing**: Indexing parent document summaries alongside fine-grained semantic clauses. - **Audit Trails & Exact Citations**: Providing verifiable paragraph-level source links for every generated legal synthesis.

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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BET
Botmartz Engineering Team
AI Engineer at Botmartz, building enterprise RAG and agent systems in production. Contributing to open-source libraries.

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