LegalTech Document
Intelligence Platform (Under NDA)

A document intelligence system using LLMs and retrieval pipelines to streamline legal case prep, automating clause extraction, precedent discovery and risk flagging while keeping data confidential.
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legaltech
Location
United States
Industry
LegalTech
Partnership
Q1 2024
Team size
6 Engineers
How we started

The client, a U.S. law firm specializing in corporate litigation, approached us after several failed attempts to automate document analysis internally. Their in-house tools relied on keyword search and basic NLP, which could not handle context, cross-references, or nuanced legal phrasing. They needed a system that could understand legal semantics — not just match terms — and return results that lawyers could trust and verify.

Our discovery process included mapping the existing workflow:
  • Paralegals spent 60–80% of their time reviewing documents manually.
  • Version control and document lineage were inconsistent.
  • Search latency and accuracy in their existing document management system were poor.
Partners Q1 2024
Services Delivered
End-to-end LLM integration, retrieval augmentation,
domain adaptation, and secure deployment
team Composition
1 PM, 2 ML Engineers, 1 Backend Engineer, 1 DevOps,
1 QA, AI Architect
Technology Stack
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Why They Chose Us

The firm’s key requirement was precision under legal context and full data isolation. We demonstrated previous expertise in building domain-tuned LLM systems with zero data leakage and auditable privacy controls.
Our architectural approach included isolated compute environments, encrypted embeddings, and a reproducible audit trail — all of which aligned with their compliance standards (SOC 2 and ISO 27001).

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Insights

Common Issues

We Identified

01 Context fragmentation:
Contracts, memos, and attachments were stored across
multiple systems, often missing references or related exhibits.

02 Low retrieval accuracy:
Keyword search couldn’t interpret clause semantics
(e.g., “termination” vs “contract rescission”).

03 Unstructured document formats:
PDF scans, handwritten notes, and OCR artifacts
degraded NLP performance.

04 Data governance risk:
No unified logging or anonymization for sensitive case data.

Solutions

What We Did

We engineered a multi-layered solution combining retrieval-augmented generation (RAG) with custom fine-tuning on the firm’s historical corpus.

Document Ingestion and Normalization

  • Developed a preprocessing pipeline with OCR, 
named-entity recognition (NER), and clause segmentation.
  • Converted all input formats (PDFs, DOCX, emails) into structured JSON documents with metadata and version lineage.
  • Integrated a legal-domain taxonomy to standardize clause types (e.g., indemnity, arbitration, liability).

2. Vectorized Knowledge Base

  • Built a FAISS-based vector store for semantic retrieval.
  • Implemented hybrid retrieval (BM25 + dense embeddings)
to ensure both lexical and semantic relevance.
  • Designed encryption-in-use with Azure Confidential Compute to protect embeddings from inspection.

3. LLM Orchestration Layer

  • Used LangChain to chain retrieval with GPT-4-Turbo for context-aware summaries and risk analysis.
  • Applied context window optimization and chunk ranking to minimize token usage while preserving accuracy.
  • Designed a dynamic “reasoning mode” toggle — short answers for search, long-form legal brief for internal memos.

4. Governance and Auditability

  • Added full event logging and redaction filters for PII before ingestion.
  • Built a “private sandbox” where lawyers can review LLM outputs, give feedback, and mark misinterpretations — these examples automatically feed the fine-tuning queue.
Impact and Results

Impact and Results

01/
Document review time reduced from ~16 hours per case to under 4 hours.
02/
Clause retrieval accuracy improved from 62% (keyword) → 91% (semantic + context).
03/
Internal compliance risk lowered with traceable logging and zero third-party data exposure.
04/
The system became the backbone of the firm’s document discovery pipeline and is now being extended to contract drafting and litigation analytics.
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