A boundary before a surprise.
Owners, envelopes, policies and approvals around AI usage, so teams move fast without cost and value drifting apart.

Why Control
Control should make adoption faster.
Teams know the boundary, the owner and the escalation path. Finance gets predictability, not an approval queue.
The ladder · enforcement is earned, not implied
- 01ObserveRecord the breach. Nothing changes for runs.
- 02WarnNotify the owner. Runs continue.
- 03 · todayRequire approvalRuns past the boundary queue for the owner. 2,734 would wait.
- 04EnforceRuns are refused. Reversible by raising the threshold.
Envelopes · monthly
80%
of spend under a policy with an owner
- Draft-reply agent111% of $18k
- Triage agent57% of $3k
- Knowledge search39% of $1k
- Escalation summarizer24% of $1k
Defaults come from the ledger’s own history. One breach, caught by the backtest.
2 of 4 agents owned
Ownership
Two agents have no business owner. The registry names the gap rather than assigning one, and the envelope still holds.
1 breach, caught early
Envelopes
Budgets and thresholds around teams, agents and workflows, set from the ledger's own history.
0 blind switches
Policies and approvals
Templates with data-derived defaults, a backtest before they switch on, and a record of each decision.
Scope
Economics and accountability. Nothing else.
Valistry governs what AI costs and who answers for it. Security, privacy and model risk stay with the systems built for them.
Spend, envelopes and unit cost.
Who owns a workload, what it may cost, what a successful outcome should cost, and who approved the exception.
The record of each decision.
Owner, evidence, exception and expected value, appended to the same ledger the number came from.
Security, privacy, model risk.
Those systems keep their own controls. Valistry reads usage metadata only, never prompts, and never gates a model on content.
How Control works
Three screens, one boundary.
An envelope, a rule tested against history, and a record of every exception.



Make every AI dollar accountable.
A 30-minute demo on your own estate, or connect one source and define your first AI policy in observe mode.
With the loop closed, any question about the estate has one grounded answer.
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