Knowledge Search
Semantic search over internal documents, policies, manuals, and archives — find by meaning, not just keywords.
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RAG Development
Retrieval-augmented generation: AI systems that answer from your documents and knowledge — grounded in sources, cited, and current, instead of guessing from training data.
Why RAG
Answers That Point Back to Sources.
A general-purpose AI model knows a lot — but it doesn't know your policy documents, your product manuals, your case files, or the procedure that changed last quarter. Asked anyway, it guesses confidently.
RAG fixes the grounding problem: before answering, the system retrieves the most relevant passages from your knowledge base and generates a response based on them — with citations users can verify. Update the documents, and the answers update with them.
It's the difference between an assistant that sounds informed and one that actually is.
Honest limitations
RAG answers are only as good as the underlying documents — incomplete or contradictory sources produce incomplete answers. Retrieval quality needs evaluation on your real questions, not demos. And for live transactional data (stock levels, balances), a database connection beats document retrieval.
What we build
RAG Systems for Real Knowledge Work.
Grounded Q&A
Question-answering that cites the exact passages it used, so users can verify every claim.
Assistants on Your Data
Domain assistants backed by your organization's knowledge, not generic internet text.
Multilingual Retrieval
Search and answers across English and Indic-language documents in the same index.
Evaluation & Tuning
Test sets built from real questions to measure retrieval accuracy before and after launch.
Ingestion Pipelines
Ongoing pipelines that keep the index current — often fed by document digitization.
How it works
The Retrieval Pipeline.
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01
Ingest
Documents are collected, cleaned, and converted — PDFs, scans, wikis, and structured sources.
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02
Chunk & embed
Content is split into meaningful passages and converted to vectors the system can search semantically.
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03
Index
Passages are stored in a vector index alongside metadata, permissions, and source references.
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04
Retrieve & answer
Each query fetches the most relevant passages; the model answers from them — with citations attached.
Get started
Have Knowledge Worth Searching?
Tell us about your documents and the questions people ask — we'll outline whether RAG fits and what a pilot looks like.