A workforce that learns your way of working.
Every approval, override and outcome feeds back into the playbooks your digital workers run. Changes arrive as versioned proposals — evaluated against your history, approved by a person, reversible in one step.
Run, review, correct, learn, promote.
The same gates that keep humans in control generate the evidence workers learn from — supervision and learning are one system.
Agents work cases under the current playbook version.
Humans approve, adjust and override at the gates you set.
Each correction is captured with the reviewer's reason.
Playbook changes are drafted from the pattern of corrections.
You approve the new version; the task can earn a notch.
What your workers learn from.
Not the internet — your book. Learning draws on the corrections, outcomes and precedents inside your own tenant.
Every adjusted figure, redrafted letter and rerouted case — with the reviewer's reason attached.
What settled, for how much, how long it took — outcomes ground the playbook in results, not opinions.
Closed cases become worked examples: how your best handlers dealt with the same situation.
When rules move, affected playbook steps are flagged to the owner — change arrives as a proposal, not a surprise.
Learning that never outruns governance.
Improvement is welcome; surprise is not. Every change is proposed, evaluated, approved and reversible.
Learning produces proposals. A named owner reviews the diff and approves the version — or doesn't.
Every candidate version replays against your historical cases and must beat the current one to ship.
Playbooks are versioned like code. Roll back to any prior version in one step; the audit trail keeps both.
Learning runs inside your tenant. Your corrections never train another customer's workers — or anyone's foundation model.
Questions teams ask about learning.
Does the model retrain on our data?
No foundation-model training. Learning updates your playbooks, examples and thresholds inside your tenant — inspectable, versioned artefacts, not opaque weights.
Who approves a playbook change?
The playbook owner you name. They see the diff, the evidence and the evaluation result before anything goes live.
Can we see why behaviour changed?
Yes — every change traces to a version, its approver, and the corrections that motivated it. The golden thread covers learning too.
What if a new version performs worse?
Evaluation should catch it first; if something slips through, roll back instantly — affected steps re-run at the prior version.