Your AI. Your data. Every answer cited to the source.

RAG (Retrieval-Augmented Generation) is how enterprise AI should work: answers grounded in your policies, your manuals, your product docs — not the model's training data. We build production RAG systems that give citation-grade answers, every time.

What is a RAG system?

A RAG system works in seven stages:

1. Ingest your documents
2. Chunk them into retrievable units
3. Embed them as vectors
4. Store in a vector database (Pinecone or pgvector)
5. Retrieve the most relevant chunks when a query comes in
6. Pass them to an LLM with the query
7. Return an answer that links back to the source document and page number

The result: an AI that knows exactly what your organisation knows, never makes things up, and shows its work.

When do you need a RAG system?

• Customer support chatbot grounded in product documentation
• HR bot that answers policy questions from your employee handbook
• Legal assistant that searches contracts and flags risk clauses
• Healthcare clinic FAQ bot trained on treatment protocols
• Internal knowledge base search across Notion, Confluence, or Google Drive
• Compliance tool that checks new contracts against regulatory documents

Our tech stack for RAG

Vector databases: Pinecone (managed, production-grade) · pgvector on Supabase (cost-optimised). Embedding models: OpenAI text-embedding-3-large · Cohere · Nomic. LLMs: Claude (Anthropic) · GPT-4o · Gemini. Frameworks: LangChain · LlamaIndex · custom pipelines.

Deliverables

• Ingestion pipeline (PDF, DOCX, XLSX, web pages, Notion, Confluence)
• Chunking strategy tuned to your document type (policies vs manuals vs FAQs)
• Vector database setup with metadata filters (department, language, date)
• Retrieval pipeline with hybrid search (vector + BM25)
• Guardrails: hallucination detection, out-of-scope deflection
• Front-end: chat UI, WhatsApp integration, or API endpoint
• Monitoring: query logs, citation-accuracy dashboard, re-ranking controls

Pricing

RAG systems from ₹4L for a single-corpus deployment to ₹8L+ for multi-tenant, multi-language enterprise systems. Delivered in 2–3 weeks.