Service — RAG engineering
Custom RAG that produces grounded answers
Nabhi Labs engineers retrieval-augmented generation systems that answer from your documents and data—with citations, evaluations, and access controls fit for enterprise use.
Evaluations from day onePermissions travel with chunks
What custom RAG implementation covers
Demos retrieve a few PDFs. Production handles messy formats, conflicting policy, and users who will trust a fluent wrong answer.
Retrieval audit
What sources matter, how answers will be judged, and which risks matter most—before pipelines.
Hybrid search
Lexical and dense retrieval chosen because they earn recall on your data—not a tutorial default.
Chunking & reranking
Strategy follows the corpus. Conflicting policies and multilingual content are first-class cases.
Groundedness gates
Refuse when evidence is thin. Cite when confidence is high. Measure groundedness alongside latency.
Secure document retrieval
Permissions travel with every chunk. A user never retrieves what they cannot open at the source.
Observability & runbooks
Evaluation harnesses, citation UX, and rollback paths for model or prompt changes.
Ship retrieval you can inspect—then scale it.
Production notes
Architecture follows evidence. Regressions surface before users do.
Demos retrieve a few PDFs. Production must handle messy formats, conflicting policies, multilingual content, and users who will trust a fluent wrong answer. Enterprise RAG engineering is evaluation, chunking strategy, hybrid retrieval, reranking, and observability—not a weekend vector index. Custom RAG implementation starts by defining what “correct” means for your corpus before celebrating latency charts.
Book a conversation
Common questions
What is custom RAG implementation?
Custom RAG implementation means designing retrieval-augmented generation around your corpus, permissions, and success metrics—not a generic chatbot template. Nabhi Labs delivers ingestion, retrieval, grounding, and evaluation as one system.
Do you offer enterprise RAG engineering without a chatbot UI?
Yes. Nabhi Labs often ships retrieval APIs, agent tools, or workflow hooks first. The interface follows the job—voice, ops console, or embedded answers—after the retrieval quality is real.