Model role
Parse and proposeSurface claims, gaps, and reversible options.
Local evidence review
Let AI parse. Keep the judgment human.
Inspect what the model noticed, challenge what it assumed, then turn your decision into a better next review.
AI review7 claims parsedConfidence is not authority
Human judgment3 assumptions challengedAccountability stays named
Next loopPolicy correctedFuture review gets sharper
Working review desk
Separate extraction from judgment.
Stored only in this browser
Extracted claims
Select a disposition for every escalated claim.
0reviewed
Human judgment
Resolve escalated claims before deciding.
Human role
Challenge and decideOwn rationale, tradeoffs, and accountability.
Loop role
Correct the policyCarry reviewed assumptions into the next pass.
Decision becomes training signal
The useful output is the loop.
A decision without a correction disappears into history. A reviewed assumption can change what the next pass escalates, what it ignores, and where a person must look.
Renew with standard terms
Generated from the packet before human review.
Waiting for judgment
Resolve claims and record an accountable decision.
No policy generated
The next review policy will reflect challenged assumptions.
Extract without deciding.
Claims remain traceable to source notes and confidence stays visible.
Challenge the recommendation.
People resolve disagreement, explain tradeoffs, and own the outcome.
Turn correction into policy.
The next pass changes because the review left a usable signal.
Portable review record
Leave with evidence, not vibes.
Better parsing creates room for better judgment.
This local simulation never sends data or acts on a decision. It makes the review boundary visible.
Review another packet