How Arthiva learns

Learning from outcomes, not clicks

Arthiva's agents don't run on fixed rules. They learn from outcomes in your own workspace: when your team confirms or dismisses an insight, when a predicted SLA breach does or doesn't happen, when a flagged return turns out to be genuine. Every prediction is later compared against what really happened, and that comparison is the training signal.

Your workspace gets its own model

Every workspace starts on a sensible baseline. As your history accumulates, Arthiva trains a model specific to your workspace — your product mix, your customers, your rhythms. It replaces the baseline only when it measurably beats it on your own held-back history; if it can't prove itself, you stay on the baseline. The same measurement runs continuously afterwards, so a model that drifts out of touch is caught and replaced automatically.

What leaves your tenant: nothing

Your business data — customers, products, prices, documents, free text — never leaves your tenant and is never used to train anything for anyone else. Models trained on your data serve only you.

Separately, workspaces can contribute to shared patterns: anonymised, aggregate signals (numbers and category flags only — never names, text, or identifiers) that are used only once the same pattern is visible across at least three independent organisations. That's how a new workspace benefits from edge cases it hasn't hit yet.

The opt-out, plainly

Shared-pattern contribution has an org-level switch on AI → Agents (org admins). Opting out stops your workspace's signals from entering shared patterns, full stop. It does not slow down your own learning — workspace-specific models train on your data for you either way. The choice only affects whether you contribute to (and draw from) the community's pooled edge cases.