Service · Enterprise knowledge
RAG & Knowledge Systems
We design knowledge systems around source quality, permissions, retrieval relevance and the decisions people need to make—not around a vector database alone.
Knowledge preparation
Retrieval quality begins before embedding
We inventory sources, remove noise, preserve useful structure and attach the metadata needed for permissions, filtering and citations. Update workflows keep the index aligned with the source of truth.
- Source inventory
- Parsing and chunking
- Metadata design
- Access-control mapping
- Version and freshness tracking
- Ingestion monitoring
Retrieval
Find evidence, not merely similar text
Queries are transformed, filtered and ranked around the domain. We test retrieval separately from generation so missing evidence is visible and correctable.
- Semantic and keyword search
- Hybrid retrieval
- Re-ranking
- Query transformation
- Permission-aware filtering
- Retrieval evaluation
Answer experience
Make trust part of the interface
Answers can cite their sources, expose uncertainty and let users inspect the underlying material. Feedback and unresolved questions become signals for improving the knowledge base.
- Inline source citations
- Evidence previews
- No-answer behaviour
- Expert escalation
- Feedback and analytics
- Knowledge-gap reporting
Selected work
Evidence over adjectives.
Real operating problems shaped into products people can understand and use.
Common questions
Clear answers before we start.
What data can a RAG system use?
Depending on access and format, it can use policies, product documentation, support material, project files, databases and other approved business sources.
How are document permissions respected?
We map source permissions into ingestion and retrieval so the application only returns information the current user is allowed to access.
How do you measure retrieval quality?
We build representative questions with expected evidence, then measure whether the right material is retrieved before evaluating the generated answer.
Start with the real workflow
Bring us the complicated part.
Tell us what your team is trying to improve, what information is available and where the current process breaks.
aryanchandwani@gmail.com