Decision evidence
A deterministic grounding check and a decision-native trace show what an answer stood on.
Prove
Make every consequential AI action accountable.
Ceez captures the evidence, decisions, approvals, actions and outcomes needed to understand how AI operated.
The proof chain
From intent to outcome, on the record.
Each step of a run leaves something behind, so the path from a goal to a result can be replayed.
- IntentThe goal, and the objective and owner that carry it.
- EvidenceWhat the answer was grounded in, traced to its source.
- ReasoningA trace of how the crew reached its decision.
- DecisionSaved on a record that shows if anyone edits it.
- AuthorizationThe named approvers, and how many were needed.
- ActionWhat ran, and when.
- OutcomeMeasured against the objective’s own success criteria.
A run, on the record
What a short run leaves behind.
Each entry carries a hash of the one before it, so changing an earlier entry breaks every later hash.
| Time | Who | What | Entry hash | Previous |
|---|---|---|---|---|
| 09:02:11 | Crew · finance close | Read the sub-ledger, read-only | 7c1e…a904 | 0000…0000 |
| 09:02:40 | Crew · finance close | Matched three differences to their sources | a904…5be2 | 7c1e…a904 |
| 09:03:05 | Crew · finance close | Proposed a journal, over the single-approver limit | 5be2…d310 | a904…5be2 |
| 09:14:52 | Controller | Approved, 1 of 2 | d310…88f7 | 5be2…d310 |
| 09:20:07 | Finance director | Approved, 2 of 2 | 88f7…2c6a | d310…88f7 |
| 09:20:08 | Crew · finance close | Posted the journal | 2c6a…e1b3 | 88f7…2c6a |
Illustrative example · not customer data
What is captured
Five things you can inspect.
Audit trail and versions
A hash-chained ledger of every action. Agent versions are immutable, and capabilities are pinned by digest, so a run can be reconstructed.
Approvals and policy
Who approved what, and against which rule, at which version.
Outcome and evaluation
Results are measured against the objective’s own success criteria. Evaluations come before autonomy is raised.
Accountability
Every objective has an owner, and every ruling carries its owner, its reason and a review date.
Go further
See the controls, and the detail.
Get started
Give Ceez one objective.
See how Ceez turns a real business objective into a working, governed AI workforce. A Ceez engineer uses the FDE Workbench to understand your environment, ground the objective and show you how it would run, with controls from day one.
- A 45-minute walkthrough with an engineer
- Your objective, scoped against your own systems
- Your environment: your cloud, your data center, or air-gapped
- Your controls, from day one
Prefer email? Write to info@ceez.ai