Prove the agent works before it goes live
AI is not deterministic, so the same input will not always give the same output. That makes testing harder and more important. We build the evaluation harnesses, test suites and quality gates that measure how often the agent is right, catch the failures, and keep it honest as the model and your data change.
Everything included,
nothing bolted on.
Every build starts with a fixed-scope conversation, no surprise line items after the fact.
- Agent evaluation harnesses
- Accuracy and quality metrics
- Automated and regression tests
- Edge case and failure testing
- Integration and end to end coverage
- Adversarial and safety checks
- Drift and regression monitoring
- Release quality gates
FAQ
Evaluation harnesses that test the agent against real examples and measure how often it gets things right, plus full test coverage across the code, integrations and interface around it.
AI is not deterministic, so the same input will not always give the same output. That makes accuracy something we measure and track as a number rather than assume from a single test run.
No. Ongoing drift and regression monitoring flags when a model update or a data change quietly degrades quality, before your users are the ones to notice.
We hunt the tricky inputs, edge cases and adversarial cases specifically, so they surface in testing rather than in front of a customer.
Interested in solving your problems with software testing & quality assurance?
Tell us what you are trying to do and we will reply with how we would build it, no obligation.
Let's chat
You go live with evidence the agent works, and a safety net that catches problems as the model and your data shift, instead of finding out from an unhappy customer.
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