Autonomous in the loop. Accountable at the gates. An agentic offering by Plainsight

Tests that keep guarding your data.

The Test agents write the suite, not just run it: not-null, unique, relationship and parity tests that ship with the build, run on every pipeline run, and flag failures and warnings instead of letting them surface in production.

Trust, generated

A suite that either passes or tells you what failed.

The Test agents turn the documented behavior into checks, and the Reconciler proves the new platform behaves like the old one. Every result is a pass, a warning or a failure with a reason, so trust is something you can show, not something you assert.

  • Not-null, unique and relationship integrity
  • Freshness on the feeds that matter
  • Parity against the documented legacy output
  • A clear delta: zero, or explained in writing

Validation report

run #0428 · sample
Schema parity (source vs target) 124/124
Row-count reconciliation delta 0
not_null(customer_id) ok
Freshness: orders feed 6h late
unique(order_id) 3 duplicates
Failures and warnings feed straight back into the build-test-run loop
How the suite earns its keep

Tests with a job, not a checkbox.

Generated from behavior, run on every build, and wired into the loop that fixes what they catch.

Written, not just run

The Test agents generate the suite from the documented behavior, so the checks match what each asset is supposed to do.

Parity against the legacy

The Reconciler compares new output against the old, row by row and aggregate by aggregate, until the delta is zero or explained.

Failures feed the loop

A failing test is not a ticket. During the build it routes straight back to the builders and the loop re-runs until green.

Runs on every build

The tests stay in the pipeline after go-live, so a regression is caught on the next run, not in a board meeting.

In dbt these are dbt tests, generated with the build; in your own framework we build the equivalent checks into it, which is extra effort on our side, and you keep them either way. dbt or your framework →

Good questions

Data-quality tests, answered.

What kinds of tests are generated?

Not-null, unique and relationship tests on the columns that matter, freshness checks on the feeds, and parity checks that reconcile the new output against the documented legacy behavior, row by row and aggregate by aggregate.

What happens when a test fails?

During the build, a failure feeds straight back into the build-test-run loop: the builders patch, the suite re-runs, and it repeats until green. After go-live, a failure or warning surfaces on the run instead of hiding in the data. How the loop works →

Do the tests keep running after the migration?

Yes. They ship with the build and run on every pipeline run, so the guarantees travel with your platform rather than ending when the project does.

Who decides a test is enough to promote?

Your experts. A green suite is the evidence; the promotion gate is still a human sign-off. How the gates work →

Let's talk

Want proof, not promises?

The estate assessment shows the kind of tests the fleet would generate for your assets, so you can see how trust gets measured before you commit.

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