AI SDLC Consulting
AI test automation consulting that reflects real risk
AI can write tests quickly, but more tests are not the same as better protection. We help your team use AI to design, generate and maintain tests across unit, API and UI layers, so coverage follows business risk, flaky tests get fixed and the suite keeps pace with code that is now written faster.
Who this is for
For teams whose tests are falling behind
When AI speeds up coding, testing often becomes the constraint. Generated tests that only restate the implementation add maintenance without adding confidence. We help you get the benefit without the noise.
QA and engineering teams adopting AI tools
You want AI to help with test design and maintenance, not just produce more test files.
Teams fighting flaky or slow suites
Unreliable tests are ignored, re-run or skipped, and real failures slip through.
Products where defects are expensive
Healthcare, fintech and retail systems where the riskiest paths need the strongest checks.
What we do
We make AI-generated tests worth keeping
We start from your risks and your current suite, then decide where AI helps most.
Map risk to coverage
Identify the business flows and failure modes that matter most, and compare them with what your suite actually tests.
AI-assisted test design
Use models to propose cases from specs and acceptance criteria, including edge cases and error paths people tend to miss.
Generate across layers
Set patterns and prompts for unit, API and UI tests that follow your conventions and assert behaviour, not implementation.
Reduce flakiness
Find unstable tests, diagnose the causes and fix them, using AI to speed up triage and repair.
Maintain the suite
Use AI to update tests when code changes and to flag tests that no longer protect anything useful.
Review generated tests
Agree how people review AI-written tests so weak assertions and false confidence are caught early.
What you get
What you get
Practical changes to your suite and the guidance to keep improving it.
- Risk-based coverage map. critical flows and failure modes matched to the tests that protect them.
- Test generation patterns. prompts and conventions for each test layer in your stack.
- Flaky test report. unstable tests, their causes and what was fixed.
- Review guidelines. how to judge whether a generated test is worth keeping.
- Suite health signals. a few measures to track over time. [TBD: metric]
How it works
How an engagement runs
Step 01
Assess
We review your suite, pipeline results and recent defects to see where coverage and reliability fall short.
Step 02
Improve
We work with your team on the highest-risk areas, generating and fixing tests and refining the patterns as we go.
Step 03
Hand over
We document the approach and coach your engineers and QA to keep applying it.
FAQ
Frequently asked questions
No. AI is good at producing candidate tests and speeding up maintenance. Deciding what matters, judging whether a test is meaningful and exploring the unexpected still need people.
We work with the frameworks and pipeline you already have. The patterns are about how tests are designed and reviewed, not a particular tool.
Best when each test traces to an acceptance criterion. Our Spec-Driven Development offering sets that up, and the test stage is a core part of the development loop.
Related offerings
Keep exploring.
AI SDLC Consulting
All seven offerings for AI-enabled software delivery.
Spec-Driven Development
Specs that people and models can both execute against.
Loop Engineering
A plan, generate, review, test, refine loop your team can trust.
Right Tool for Each SDLC Stage
Vendor-neutral tool selection without the sprawl.
AI-Generated Code Review
Independent review of code written with assistants and agents.
Contact Stavir
Tell us how your team works with AI today.