About
Retgen AI
We build retrieval-augmented generation systems, document processing pipelines, and semantic search engines. Vector databases, embedding models, chunking strategies, rerankers — the full stack.
What We've Built
Contract analysis pipelines processing thousands of legal documents. Internal knowledge bases with citation tracking. Semantic search over enterprise wikis and technical documentation. RAG systems with proper evaluation frameworks — not just vibes, but measured retrieval accuracy and answer quality.
What drives us
RAG Architecture
Chunking strategies, embedding models, vector stores (Pinecone, Milvus, pgvector), hybrid search with BM25
Document Processing
PDF parsing, OCR, table extraction, entity recognition, document classification
Retrieval & Ranking
Bi-encoders, cross-encoders, reranking pipelines, query expansion, HyDE
Evaluation & Guardrails
RAGAS, retrieval metrics, answer grounding, hallucination detection, content filtering
How We Work
Audit
Analyze your docs, data formats, and existing infrastructure
Prototype
Working RAG pipeline with your actual data in 2-4 weeks
Evaluate
Measure retrieval accuracy, latency, and answer quality
Deploy
Production system with logging, monitoring, and fallbacks
Ready to start?
Let's transform your unstructured data into AI that works.