No model training on your data
LiveWe do not train models on customer content. There is no opt-in, because there is no training programme.
- ✓No model training or fine-tuning on customer content
- ✓Three model vendors, all disclosed: Anthropic, OpenAI (embeddings and multi-agent), Google (multi-agent)
- ✓Sub-processor list generated from live configuration, published unauthenticated
- ✓Every model vendor hard-disabled when the deployment is air-gapped
What it is.
Your documents are not used to train or fine-tune any model. That is not a setting you have to find and switch off — there is no training pipeline in the product to switch off, and no per-organisation opt-in toggle. The page previously described one; it does not exist, and describing a control that is not there is worse than describing none.
The models we send content to are third-party APIs — Anthropic for the main analysis path, OpenAI for embeddings and part of the multi-agent path, and Google for the other part. Their API terms, not their consumer terms, govern that content. The current list is published on our privacy policy page, which is generated from live configuration rather than maintained by hand, so a new vendor appears there when it is added rather than when someone remembers.
Three steps.
End to end.
There is no training toggle, because there is no training on your content.
Three model vendors receive content on different paths. All three are disclosed and all three are disabled outright in air-gapped deployments.
The sub-processor list is unauthenticated and derived from live configuration — read it rather than take our word for it.
What you get.
- ✓No model training or fine-tuning on customer content
- ✓Three model vendors, all disclosed: Anthropic, OpenAI (embeddings and multi-agent), Google (multi-agent)
- ✓Sub-processor list generated from live configuration, published unauthenticated
- ✓Every model vendor hard-disabled when the deployment is air-gapped
- ✓No per-organisation training toggle — there is nothing for one to control
Quick answers.
No. If we ever build one, it will appear here and in the DPA before it appears in the product.
Error monitoring and product analytics are gated behind a single flag that is off in air-gapped deployments. Model spend and token counts are logged per request against your organisation, which is your data about your usage, not your content.
More in AI Controls & Guardrails.
User-pasted text is sanitised before reaching the LLM.
A monthly AI token quota per plan, an 80% warning by email and in-app, and a hard stop at 100%.
Lower the model ceiling, switch AI features off, cap daily spend, and read the AI audit history and security events.
Stable prompt prefixes are cached. Faster responses, lower bill, passed to you.