ITNeuroNet
Here you can see how the service is built: the system design, data handling, quality checks, security measures, and any AI-supported workflow behind it. This helps you judge how the work will be done and what to expect in practice.
Quality assurance

Quality gates that hold up at AI-assisted delivery speed.

AI-assisted implementation is only dependable when testing, review steps, and acceptance checks keep pace with the output. We build the quality approach into the delivery process from the start instead of treating it as something to do at the end.

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Who this is for

For teams delivering product work with AI assistance that need quality they can verify without slowing everything down to the pace of fully manual review.

The real problem

When code generation speeds up but tests, types, and reviews do not, defects slip downstream. They become more expensive to fix, and trust in AI-assisted work starts to erode — usually just before the release where that trust matters most.

What we do

We set up the test structure, type checks, interface checks, review steps, and clear acceptance rules for each task. Code written with AI goes through the same checks as any other change before it is accepted.

How it holds together
  • 01

    Test pyramid by intent

    You get unit, integration, contract, and end-to-end tests where they matter most. Test coverage follows the actual risks in your system instead of an arbitrary percentage target.

  • 02

    Contract and type gates

    API and data contracts, together with strict types, catch many forms of AI-generated drift before runtime.

  • 03

    Reviewable acceptance

    Each task comes with written acceptance criteria linked to the documentation. That gives reviewers something concrete to check instead of arguing about what “done” means.

  • 04

    Regression and change trails

    When a test fails, when it is fixed, and when it passes again, that record stays with the change. You can see the quality work instead of having to trust that it happened.

Test pyramid

Quality surface mapped to delivery risk, not to vanity coverage targets.

  1. L1

    Types + contracts

    compile-time + schema checks

  2. L2

    Unit tests

    pure logic · fast feedback

  3. L3

    Integration

    modules · adapters · data

  4. L4

    Contract tests

    API + data shape boundaries

  5. L5

    End-to-end

    critical user journeys

  6. L6

    Acceptance review

    matches documented intent

What you get
  • 01Quality strategy tied to the system's real risks
  • 02Test plan and tooling decisions
  • 03Review gate and acceptance criteria definitions
  • 04Recommendations for CI feedback loops
Parent services

Service areas that use this technical foundation

Related reading

Background on this topic

Next step

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