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NLP 3 ay LLM Integration Specialist & Lead Developer

Enterprise LLM Knowledge Assistant

RAG (Retrieval-Augmented Generation) based enterprise knowledge assistant. Trained on internal documents, GDPR compliant, secure AI assistant.

%65
Productivity Increase
%40
Support Request Reduction
50K+
Indexed Documents
%94
Response Accuracy

Challenge

Efficiently indexing 50,000+ documents, minimizing hallucinations, and providing access control for sensitive information were the most critical challenges.

Solution

Split documents into semantic chunks with smart chunking strategy. Reduced hallucinations by 95% with guardrails and citation mechanisms. Ensured security with RBAC-based filtering.

Highlights

1

Advanced RAG pipeline design with LangChain

2

Multi-model strategy (GPT-4 + Claude fallback)

3

Semantic chunking and smart indexing

4

GDPR-compliant security architecture

Technology Stack

Python
LangChain
OpenAI
Pinecone
FastAPI
Next.js
Redis

About the Project

Developed for a technology company with 500+ employees, this assistant provides instant access to all internal documents, wikis, and procedures.

Technical Architecture

  • LangChain-based RAG pipeline
  • GPT-4 Turbo &
  • Pinecone vector database
  • Chunk-based document processing
  • Smart querying with semantic search
  • Role-based access control

    • Results

    Achieved 65% increase in employee productivity and 40% reduction in IT support requests.