Draft. This document has not yet been reviewed by counsel and is not in force. It is published here for review only. Bracketed tokens mark facts that still have to be filled in.

How Seros Uses AI

Effective date: [[EFFECTIVE_DATE]]

This is the short, plain version of how Seros uses AI. The binding terms are in Section 7 of the Terms of Service, the Privacy Policy and the DPA.

What the product does with AI

Seros reads the work you connect — messages, tickets, documents, calendar entries — and uses large language models to draft tasks, suggest who should do them, set out what "done" looks like, and summarise status. It proposes. Your people decide.

What data goes to model providers

To generate something, we send the model provider the parts of your content that the feature needs: the text of the message or document being turned into a task, related task records, and the names and roles of the people involved so it can suggest an assignee. We do not send your whole workspace, and we do not send content from features you are not using.

The model providers we use are listed in SUBPROCESSORS.md, with where they process data.

Training

• We do not use your content to train our own general-purpose models.
• We do not permit our model providers to train their models on your content. Our aim is to use enterprise or API terms where training is off by default, and where prompts and outputs are retained only briefly for abuse monitoring, or not at all. The exact retention window per provider is [[AI_PROVIDER_RETENTION]] and must be confirmed against each provider's current terms.
• We may use aggregated statistics that cannot identify you or any individual — for example "tasks generated per week" or error rates — to run and improve the Service.
• If we ever want to use customer content to improve our own models, we will ask for opt-in consent first, in writing, per customer. Silence will not count as consent.

Human oversight

• Everything the model produces is a draft until a person accepts it.
• You must review output before acting on it, and you must not use the Service to make decisions with legal or similarly significant effects about a person without meaningful human review. That is a rule in the Acceptable Use Policy, not a suggestion.
• Administrators can control which integrations are connected and what data is in scope.

Limitations, stated plainly

• The models are probabilistic. They can be wrong, out of date, or confidently invent detail that is not in your data.
• They can reflect bias in their training data, including in suggestions about who should do what. Watch for that in assignment recommendations.
• The same input can produce different output on different runs.
• Output is not legal, tax, medical, financial or other professional advice.
• Similar inputs from different customers can produce similar output. Output is not unique to you.
• Model providers change their models. Behaviour can change without an announcement from us.

Your controls

• Choose which systems to connect, and the scope of each connection.
• Turn AI features off for a workspace: [[AI_OPT_OUT_AVAILABLE]] — confirm whether this is actually possible in the product before publishing.
• Delete generated content the same way you delete any other content.
• Ask us what was sent to a provider for a given generation: [[AI_AUDIT_LOG_AVAILABLE]].

Questions: privacy@seros.dev.

What I need from you

1. Confirm the training and retention position against each provider's live terms, and note whether it requires a specific plan or a signed addendum.
2. Confirm whether AI features can genuinely be switched off, and whether a per-generation audit log exists.
3. Keep this page in sync with the model providers listed in SUBPROCESSORS.md.