Retrieval-Augmented Generation is the most practical way to put LLMs on your private knowledge. We build the full pipeline — ingestion, chunking, embeddings, hybrid search, reranking, generation with citations, and evaluation — so answers stay accurate as your content changes.
Enterprise RAG for knowledge, support, policy, and product documentation teams worldwide.
Demos ignore messy PDFs, access control, chunk boundaries, and evaluation. Production RAG needs permission-aware retrieval, hybrid lexical + vector search, reranking, citation UX, and continuous measurement of retrieval and answer quality. That operational layer is our focus.
We design for your corpus: policies, tickets, wikis, product docs, or multi-tenant SaaS content. Pipelines include incremental ingestion, metadata filters, hybrid retrieval, optional knowledge graphs, and generation policies that refuse when evidence is weak. Deployments can be cloud-managed or private.
Search and answers often fail for Hindi and other Indian languages if embeddings and chunking are English-centric. We build multilingual retrieval experiences — a differentiator for public services, education, and consumer products in India and the diaspora.
Inventory sources, formats, ownership, and who is allowed to see what.
Ship a measurable retrieval stack before polishing generation.
Add generation, citations, and eval sets; tune chunking and reranking.
Security, observability, fallbacks, and runbooks for ops teams.
We measure retrieval hit-rate and groundedness, not vibes.
Built for Indic and multi-language corpora, not English-only samples.
Lessons from shipping GenAI search products inform every RAG build.
Simple internal pilots can start in the low thousands of USD (or equivalent) for a focused corpus. Production multi-source systems with SSO, permissions, evaluation, and SLA-backed ops typically range from mid five-figures upward depending on corpus size, languages, and integrations. We quote after a short discovery.
Use RAG when answers must reflect changing private documents. Use fine-tuning for style, format, or specialised behaviour. Many systems combine both; we recommend based on your data and risk.
Yes. We can deploy vector stores, embedding services, and models inside your VPC or on-prem environment when policy requires it.
Strong retrieval, citation requirements, refusal when evidence is weak, answer validation, and ongoing evaluation on real queries — not prompt tweaks alone.
Share your corpus and goals. We will outline pipeline design, risks, and a realistic pilot scope.
Book a free 30-minute consultation. We'll map the fastest path from concept to a production-ready product.