AI assistant & semantic search

Stop grepping your specs. Ask a question in plain English and Elva’s chat agent searches, retrieves, and answers straight from your live catalog — grounded in the real operations, schemas, and security of the APIs you can access.

Ask Elva about your catalog; it retrieves the real spec before it answers.

Chat that actually reads your specs

The assistant is a RAG (retrieval-augmented) agent, not a chatbot that hallucinates endpoints. When you ask a question, it runs a tool-calling loop over the collections you have access to, pulls the exact spec content it needs, and answers from that evidence. The system prompt is explicit: gather information with tools first, never guess endpoint names, fields, or counts.

It runs in-app through POST /api/ai/chat, scoped to your account. Every answer is limited to the collections you can see, and the assistant automatically attaches your active collection context so questions about the API you are looking at just work. Responses can stream token by token, and each one carries the source chunks it was built from.

The agent’s toolbox

Instead of stuffing an entire spec into the model, the agent picks the right tool for the job. Each tool is access-checked against the collections you own before it returns anything.

How a question becomes a grounded answer

The agent runs up to three tool rounds at temperature 0, then makes a final answer call. Sources are de-duplicated by collection and chunk id before they come back to you.

graph LR;
  Q[Your question]-->E[Embed query];
  E-->R[Retrieve chunks];
  R-->T[Tool calls: search / list / usages];
  T-->G[Grounded answer + sources];
  classDef accent fill:#ffffff,stroke:#ffffff,color:#15152A;
  class G accent;