RAG Systems · SaaS
RAG Systems for SaaS
A RAG system built for SaaS companies.
Most SaaS teams can build a working RAG prototype in a week and then spend six months discovering it is right seventy percent of the time. We build the evaluation harness first, so retrieval accuracy is a tracked number, and tune chunking, hybrid search, and reranking against it.
Built by VelvoAI, an independent AI systems studio. One engineer scopes and writes every system.
What the system does
- Every claim
- Grounded
- Search strategy
- Hybrid
- Retrieval accuracy
- Measured
Use cases
What this handles for SaaS companies
Ship retrieval that is actually accurate, not a demo that impresses once.
- Add retrieval over customer-uploaded documents with strict tenant isolation
- Build in-product search that answers questions rather than returning a link list
- Ground AI features in your documentation so they never invent product behaviour
- Instrument retrieval quality per release so regressions fail CI instead of shipping
A retrieval layer with a measured accuracy figure you can defend to customers.
How it runs
Day to day for SaaS companies
Your customers' data, your product's answers, and a hard boundary between tenants. The tenant identifier is validated at query time against the authenticated session rather than accepted as a parameter, because metadata filtering is not isolation. Retrieval also respects the individual user's permissions, not just their company's, and every answer carries the passage behind it.
- S.01pgvector under a few million vectors; a managed store with namespaces above
- S.02Your existing auth, for the permission check at query time
- S.03An eval set pinned alongside the prompt and retrieval config
- S.04Tracing, so a bad answer can be traced to the passage that caused it
- The systemRAG Systems
Go deeper
The service and the sector, in full
- 01 · How it is builtRAG SystemsHow every build is approached, what ships with an engagement, and the stack behind it.
- 02 · Sector contextAI for SaaSWhat slows SaaS companies down, and every system VelvoAI builds for the sector.
RAG Systems for SaaS — questions
01How do you isolate tenants in a shared vector store?
Namespace or partition per tenant with the filter applied at query construction rather than in application code, plus tests that specifically attempt cross-tenant retrieval. This is the highest-severity failure mode in multi-tenant RAG, so it is verified rather than assumed.
02What retrieval accuracy is realistic?
It depends entirely on your corpus, which is why we measure on your data before promising anything. The valuable output is a number you can track and improve, not a benchmark from someone else's documents.
Same capability
RAG Systems in other sectors
Same sector
Other systems for SaaS companies
- Written by
- Usman Ali, Founder
- Last reviewed
Next step
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