RAG (Retrieval-Augmented Generation)
Retrieval-Augmented Generation connects large language models to your own knowledge so answers are accurate, current, and citable — not hallucinated. We design the full pipeline: ingestion, chunking, embeddings, vector search, reranking, and generation, tuned and evaluated for your domain and deployed securely on-prem or in the cloud.
Capabilities
- Document ingestion & smart chunking
- Embeddings & vector database setup
- Hybrid search & reranking
- Cited, grounded LLM responses
- Retrieval evaluation & tuning
What you get
- End-to-end RAG pipeline
- Vector store & retrieval API
- Evaluation harness & accuracy benchmarks
- Secure on-prem or cloud deployment
Common use cases
Enterprise knowledge search
Policy, legal & compliance Q&A
Support & product documentation assistants
Related solutions
Related guides
RAG (Retrieval-Augmented Generation) questions
How long does a RAG pilot take?+
A focused pilot on a well-defined corpus often takes 4–8 weeks including evaluation. Multi-source enterprise systems take longer.
Can RAG support Hindi and other Indian languages?+
Yes. We design multilingual retrieval and generation for Indic-language content when the use case requires it.
How much does RAG development cost?+
Depends on corpus size, languages, permissions, and integrations. We provide a transparent estimate after a short discovery — see also our RAG development company page for ranges and approach.
Selected projects
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