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AI · Data Science
Custom LLM Integrations
Private AI models fine-tuned on company data for deep, secure conversational intelligence.
Client
Legal & Finance Sectors
Industry
Enterprise
Duration
8 Weeks
Year
2026
100% Private
Data Security
< 1.5s
Query Speed
Unlimited
Document Limit
The Challenge
Enterprise clients needed the power of AI to analyze thousands of internal PDFs, contracts, and financial reports, but could not risk exposing sensitive intellectual property to public models like ChatGPT.
The Solution
Engineered secure, private Retrieval-Augmented Generation (RAG) pipelines and fine-tuned open-source LLMs (like Llama 3 and Mistral) deployed within the client's own VPC infrastructure to guarantee absolute data privacy.
Design Process
Designed a multi-stage chunking and embedding strategy to process massive documents without losing semantic context, enabling the AI to cite specific pages and paragraphs in its answers.
Development Process
Built the ingestion engine using Python and LangChain. Vectorized data into Milvus/Pinecone databases. Served the AI through a highly optimized Next.js chat interface for internal employees.
Technology & AI Stack
PythonLangChainNext.jsAWS VPCMilvus / PineconeLlama 3MistralCohere EmbeddingsRAG
Project Results
- Enabled instant semantic search across 50,000+ pages of internal documentation.
- Maintained 100% data sovereignty and compliance with strict privacy laws.
- Reduced contract review times by up to 90%.
Key Features
Private On-Premise LLM Deployment
High-Density Vector Search (RAG)
Automated PDF & OCR Extraction
Role-Based Access Control (RBAC)
Exact-Match Citation Generation
"Having a brilliant AI that has read every single document in our company's history—without compromising our privacy—is an absolute superpower."
Chief Data OfficerFinancial Institution
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