01 / Discover
◎
AI quality & risk review
Map the feature, intended behavior, user impact, and the failure modes worth testing first.
Focused AI Quality Engineering
AI, tested for
real-world use.
AI systems can behave unpredictably, and traditional QA doesn't tell the whole story. Kitech helps you test what matters, define how to measure quality, and make your AI feature easier to trust in production.
ONE AI FEATURE • TEST ARCHITECTURE + HANDS-ON QA • SCOPE AGREED UP FRONT
ONE SCENARIO. A CLEAR TEST PATH.
Scenariouser intent
Test runrealistic input
Evaluatequality criteria
Findingclear next step
Kitech in focus
The architecture of AI quality.
A useful AI test starts with the user journey and the risks—not a pile of generated test cases. We help your team turn product expectations into scenarios that can be evaluated and repeated.
AI quality evaluation
See the behavior behind the score.
One example of how a scenario can become a repeatable test: define what should happen, compare it with the model’s response, and capture the risk when behavior falls short.
AI quality reviewTEST RUN
Scenario: refund policy · no order details provided
Expected: ask for an order ID before advisingEXPECTED
Observed: promises a refund without checking eligibilityHIGH RISK
Repeat check: missing context and policy edge casesNEXT TEST
One focused engagement
A practical AI test sprint.
Start with one AI feature. Agree the scope, establish how quality should be judged, then test representative scenarios and review what the results mean for your team.
02 / Architect
⌘
Evaluation strategy
Define realistic test scenarios, quality criteria, review methods, and a repeatable evaluation set.
03 / Validate
↗
Hands-on AI testing
Run the tests, investigate unexpected behavior, and document reproducible findings for your team.
Sprint deliverables
Useful evidence. A clear next step.
The work is scoped with you up front and focused on a single AI feature, so the result is practical for the team that owns it.
01
Prioritized findings
Observed behaviors, supporting examples, impact notes, and the issues that deserve attention first.
02
Reusable evaluation set
A structured collection of representative scenarios and criteria to rerun as your AI feature changes.
03
Test architecture outline
A practical recommendation for how evaluation fits your product, team workflow, and release process.
04
Next-step plan
Clear options for improving the feature and deciding whether more automation or ongoing QA is warranted.
A good place to start
When AI behavior needs a closer look.
A focused sprint can help if one AI feature is close to launch, changing quickly, or producing answers your team cannot yet evaluate consistently.
“We’re preparing to launch.”
Review one AI feature before users rely on it, and surface the scenarios most likely to expose problems.
“The answers keep changing.”
Define representative tests and criteria for behavior that cannot be checked with one fixed expected answer.
“We need a repeatable check.”
Leave with a structured evaluation set your product team can revisit as prompts, models, or content change.
“We found a risky failure.”
Investigate the behavior, understand its likely impact, and document a reproducible example for the team.
Our process
How we work together.
Kitech works directly with your team, keeping the scope focused and the findings easy to act on.
01
Understand
Review the AI feature, user journey, expected behavior, and key risks.
02
Plan
Agree on evaluation scenarios, quality criteria, and the sprint scope.
03
Test
Run realistic scenarios and capture the behavior that needs attention.
04
Improve
Walk through findings and leave a practical plan for repeat testing.
FAQ
A few things you may be wondering.
We’ll agree the scope together before work begins, based on the AI feature and the access your team can provide.
What is an AI Quality Sprint?
A focused review of one AI feature that combines test planning, hands-on evaluation, prioritized findings, and a repeatable next-step plan.
How is testing AI different from traditional QA?
AI output can vary between runs, so testing needs to assess quality across representative scenarios and define what acceptable behavior means—not only check fixed outputs.
What kinds of AI features can Kitech test?
The initial focus is LLM-powered assistants, copilots, generative features, and agent workflows. We’ll confirm fit and scope before agreeing to a sprint.
Do you build a complete AI testing platform?
No. Kitech offers focused testing and test-architecture work for your product. If deeper automation is useful, the sprint can identify a practical path for your team.
Start with one AI feature
Let’s look at what your AI feature actually does.
Share the feature, the behavior you’re unsure about, and where you are in the release. We’ll discuss whether a focused test sprint is a good fit.
One point of contact throughout the sprintScope agreed before work beginsFindings tailored to your product
Prefer email? dk@kitech.lt