Enterprise AI Document Intelligence
Private AI document processing that reduced contract review time by 90% for a international logistics company.
$1.8M
Savings
85%
Adoption
10K+
Documents
-90%
Review_Time
The challenge
A logistics company operating across 12 countries managed over 50,000 active contracts, shipping manifests, customs documents, and compliance certificates — most stored as PDFs in shared drives with no structured search. Legal and operations teams spent an average of 45 minutes locating and reviewing each contract, and missed renewal deadlines cost the company an estimated $2M annually in unfavorable auto-renewals.
Leadership wanted AI-powered document search but couldn't use public LLM tools due to confidential commercial terms in contracts and regulatory requirements around cross-border data.
Our approach
LesterTechLabs designed a private RAG (Retrieval-Augmented Generation) pipeline that kept all documents within the client's AWS environment. We indexed PDFs, Word documents, and scanned images using OCR, then built a conversational interface where staff could ask natural language questions and receive answers with citations to source documents.
We also implemented automated contract expiry monitoring and a renewal workflow that alerted legal teams 90, 60, and 30 days before deadlines.
Implementation
Key components delivered:
• Document ingestion pipeline with OCR for scanned PDFs and multilingual support
• pgvector embedding store with semantic search across 10,000+ indexed documents
• RAG chat interface with source citations and access control by department
• Contract lifecycle dashboard with expiry alerts and renewal tracking
• Admin panel for document upload, re-indexing, and user permission management
Built with Python, LangChain, OpenAI embeddings (via private API), Django admin, and deployed on the client's existing AWS infrastructure with VPC isolation.
Results
Contract review time dropped from 45 minutes to under 5 minutes for standard queries. The legal team processed 3x more contract reviews in the same headcount. Automated expiry alerts prevented $1.8M in unfavorable auto-renewals in the first year.
Staff adoption reached 85% within three months, with an average of 200 document queries per day across legal, operations, and compliance teams.
Technologies used
Lessons learned
Private RAG deployments succeed when answers include source citations — users trust the system when they can verify responses. Start with a narrow document type (contracts) before expanding to all company documents; focused accuracy beats broad mediocrity.
Questions about this project
Was client data sent to public AI models?
No. All documents were processed within the client's private AWS VPC. Embeddings were stored in their pgvector database. LLM API calls used enterprise agreements with zero data retention.
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