know your data agent is correct; don't hope it is.
Automated evaluations that catch errors before they reach a decision maker. Ship data agents that your business will actually trust.
01
build ground truth
Your team curates the expected answers to your most important questions: revenue, churn, pipeline. These become your benchmarks.
02
run evaluations
dardar tests your data agent against those benchmarks after every agent update or schema change. No manual spot-checking.
03
gaps and regressions surface
See exactly where answers diverge from ground truth, and which query types aren't covered at all. Catch bad numbers before they reach a stakeholder.
04
fix the root cause
Update documentation, schema definitions, or context. Re-run evaluations to confirm. Your benchmark suite keeps pace as your data and agents evolve.
catch agent drift before it
leads to a bad decision
Evaluations run against your data agent. Regressions surface in minutes, not after someone points out an error in a presentation.
your data infrastructure,
dardar's intelligence
dardar reads your data schema, metric definitions, and documentation, so answers reference your business logic, not generic SQL.