Audit & Traces
Governed analytics has to be answerable: who asked what, what the platform did, and whether you can get the same answer again. Colrows records API activity, keeps AI conversation history, shows the reasoning behind every answer, and lets a governed result be re-fetched by ID.
Answer provenance
Every AI answer carries its own explanation - this is what makes Colrows results point-in-time reproducible. Alongside the result you get:
- the generated SQL;
- a confidence score and an explanation;
- the semantic sources used - entity types with their names and descriptions (internal IDs are omitted);
- the columns, rows, row count, and a
truncatedflag.
Conversation & query history
AI conversations are stored per user and can be listed and reopened - the full thread, each interaction's rendered widgets, and the ability to delete a conversation. A governed query execution can be re-fetched by its execution ID, so a result can be reused or re-displayed exactly as it was, rather than re-derived.
| Operation | Endpoint |
|---|---|
| List your conversations | GET /api/ai/chat/list |
| Open a conversation | GET /api/ai/chat/get/{conversationId} |
| Delete a conversation | DELETE /api/ai/chat/delete/{conversationId} |
| Re-fetch a governed execution | GET /api/ai/query/executions/{executionId} |
API activity audit
Every API call is recorded server-side after its response - the request URL, HTTP method, response code, request body, organization, user, and timestamp. Together with conversation history, this is the audit trail behind a governed deployment. MCP tool calls are recorded through privacy-safe telemetry that never logs tokens, natural-language questions, generated SQL, or result rows.
Because access, redaction, and cost guardrails are enforced at compile time, the audit trail records governed activity - there is no path to data outside a persona's allowed subgraph to record in the first place.