The Built Correct AI workbench
Good decisions.
In plain sight.
Give AI a useful job. See its evidence, inspect the decision, and change the settings. Every run leaves a trail you can follow.
Fictional cases with authored expected answers. An open workbench, not a claim of production accuracy.
03 / The evidence
Follow the decision.
A useful answer has a paper trail.
Choose a case and run the rules baseline. Then try an available model with the same input.
The settings above have changed. These results still belong to the saved run. Run again to compare.
What was actually confirmed?
Signal and confirmation methodRead the evidence behind the handoff.
Sources are selected by product and a lexical shortlist, then ranked by the chosen engine. Relevance is not proof that the job meets a requirement.
Every step, in the open.
Choose a stage to see its inputs and outputs. Rules, provider responses, and template output are labeled separately.
Same case. A different approach.
Compare saved runs on this workflow. Changes to input, references, or settings are shown so a difference has context.
Was the next step useful?
Your feedback stays attached to this run. It does not change the expected answer or become an approved golden case.
Starter suite / Development fixtures
What happened across the cases?
A small authored suite is useful for finding regressions. These results are not a representative accuracy benchmark.
Keep the evidence
Your recent runs.
Saved to this browser’s workbench session. Open a run to inspect its original inputs, model, and review.
| Case / time | Engine | Result | API estimate | Review | Action |
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Build something useful
The next step is
your workflow.
Start with a decision your team makes every day. We can help connect it to your records, your rules, and a scorecard that means something.
Meet your engineering team