Decisions that know when they expire

Detect when new evidence invalidates an old decision.

Record decisions with the assumptions that support them, then compare new evidence and show which decisions remain valid, are at risk, or must be reopened.

Try the fixture No sign-in · precomputed from the tested fixture

One product.

One decision.

Visible evidence.

Interactive proof

Reveal the verified fixture.

These exact decisions and evidence records are evaluated by product.py.

Decision ledger update

Decisions:
[
  {"id":"D-1","decision":"Use Model A","owner":"Maya","reason":"Best measured quality-cost tradeoff","decided_at":"2026-06-10","invalidate_when":"quality < 80","review_when":"cost > 200000"},
  {"id":"D-2","decision":"Keep vendor B","owner":"Ken","reason":"Uptime remains above the service floor","decided_at":"2026-05-22","invalidate_when":"uptime < 99.5","review_when":"incidents > 2"},
  {"id":"D-3","decision":"Launch workflow C","owner":"Ari","reason":"Pilot completion cleared the launch floor","decided_at":"2026-07-01","invalidate_when":"completion < 90","review_when":"complaints > 5"}
]

Evidence:
{
  "D-1":{"quality":77,"cost":180000,"source":"eval-2026-07-18.csv"},
  "D-2":{"uptime":99.8,"incidents":1,"source":"status-q2.json"},
  "D-3":{"completion":93,"complaints":7,"source":"launch-week.csv"}
}

The tested result is ready to inspect.

Reveal the expected decision generated by the product's deterministic acceptance fixture.

How it works

Small surface. Clear chain of proof.

Register

Store the decision, owner, reason, decision date, assumptions, evidence, and review trigger.

Compare

Match new facts to the assumptions they can weaken or replace.

Reopen

Emit an owner-linked assessment event with the exact evidence that crossed the trigger.

✓ Explicit assumptions✓ Fail-closed evidence state✓ Owner-linked assessment events