Three guardrails run through this work: right delivery, right quality, right ongoing operations. We make each one real and measurable rather than a slide in a governance review.
What we build and run
We create the tests. For each domain, roughly three tests per table and two per column:
- Domain tests that know what your business means by a valid record.
- Business-focused tests tied to the numbers people actually report.
- Raw source tests that catch a vendor feed the day it changes.
- Accelerator pack tests from work we have already done in your industry.
- AI-generated tests for the long tail.
- Custom tests where your data earns them.
Then we run everything around those tests. Data contracts. Quality dashboards. Observability on freshness, volume, and schema. We test inside your production workflows, at every layer of the medallion, not in a side process nobody looks at.
When something breaks, we report it, gather feedback from the people who found it, push results into your catalog, and run a retrospective so it does not break the same way twice.
The number nobody wants to hear
Real coverage across a commercial data estate runs into the hundreds of thousands of tests. Take 4,500 tables and 90,000 columns. Three per table and two per column puts you near 200,000.
Nobody writes 200,000 tests by hand. That is the whole reason the tooling matters. One of our pharma customers runs 12,071 auto-generated tests on a single launch dataset, plus 1,639 built unique to their organization, with AI and our expertise doing the building. That is what it takes to trust one product’s data. Multiply the discipline across your estate and you get the number above.
From audit to practice
A quarterly audit proves nothing about Tuesday. Someone assembles evidence, everyone nods, and the data breaks again on Thursday.
We embed the policies into the development workflow instead and run automated oversight continuously. Your teams develop fast inside explicit guardrails, and you get proof the guardrails are working, on any day you care to look.
The work runs in cycles: discovery, profiling, semantic refinement, quality-rule development. AI-assisted detection and disposition handles the volume, flagging changes, drift, and downstream impact. A person weighs in where judgment is needed and skips the other 90 percent.
The playbook you keep
We write down the standard so your teams can follow it without us in the room. What test types exist, when to apply each one, and instructions by dataset type. A raw claims feed, a curated gold table, and a CRM extract behave differently and get tested differently.
Inside it we define what certified means. Certification is not a stamp someone applies by hand after a meeting. It is a set of tests that pass, on a schedule, with known freshness, a named owner, and a clean run history. Anyone can look up which tests exist on a table, how often they run, and whether they passed this morning.
We also package the standard as context and skills your agents can read. They implement and enforce the rules on their own: what to test, how to test it, and what certified requires.
What it runs on
DataOps TestGen is the single pane of glass on testing and quality. It auto-generates most of your tests and finds hygiene problems without being asked. Three ways in: a UI for people who do not write SQL, an MCP interface for AI agents, and an API for pipeline and CI/CD test gates.
Our commercial test packs give you a head start on industry-specific checks. DataOps Observability adds SLA tracking, so you know data is late before the business does.
TestGen is open source. Your tests, your configuration, and your baselines live in your environment, not ours.
Beyond the tables
We test data science models and the data feeding them, not just the tables underneath.
We add observability and testing to your AI analytics, including confidence scoring on generated answers, so a wrong answer gets caught before an analyst builds a decision on it.
We bring regression testing to new pipeline code. A change proves itself before it ships, not in the business review three weeks later.
Build, run, transfer
We build the tests and the monitoring. We run them as a managed service. When you want to own it, we hand over a documented, open-source foundation and your team takes it from there.
No lock-in. No proprietary black box. We size and phase the build by domain, so your highest-priority areas get covered first and the rest follows as coverage proves out.
Where to start
A working session with your team. Pick two or three domains, point TestGen at them, and look at real test results from your own data inside two weeks.
You see what it finds before you commit to anything.
