Tailor this page to you
Your dashboards are green, but defects still escape — and now AI writes the tests too. Karate joins what your tests actually did to the requirements they must prove, grades what’s genuinely exercised versus merely claimed, and computes a release decision you can defend.
Not hand-maintained links in an ALM. A git-native graph of requirements ↔ tests ↔ runs, graded exercised-vs-claimed, with a deterministic ship verdict produced by versioned code — no model in the path.
Questions you can finally answer
Are we safe to ship?
A computed verdict with named blockers
What looks tested but never ran?
Exercised vs. merely claimed
What AI work is unreviewed?
The @ai review ledger — nothing self-approves
Does every requirement have real evidence?
A live requirements-traceability matrix
Computed, not guessed · reproducible · defensible in an audit
The business case
When AI writes the code and its tests faster than anyone can review, the bottleneck isn’t building software — it’s trusting it. That trust is what we make measurable.
We grade each requirement’s evidence as exercised vs. merely claimed — so “looks tested but never ran” is caught before it ships. Exactly the failure mode when AI writes both the code and its tests.
A confidence-to-ship verdict computed by code with no model in the path — reproducible, auditable, gateable in CI. The AI explains it; it never decides it.
Every AI-authored test, rule, or requirement is marked review-pending until a human signs off. You always know what your AI wrote that nobody has checked — and an agent can’t self-approve.
The dashboards are green but defects still escape — and now AI writes the tests too. I can’t tell what a human reviewed versus what the AI signed off for itself.
— What enterprise engineering leaders tell us
You feel this when…
Under the hood
An ALM stores links a human typed. We derive the trace from what the tests actually did — and grade it.
ALM / traceability suites
Karate — computed from runs
A test declares the requirement it covers; the run proves it — the link is real only when both are true.
# the test declares intent with a tag
@req=ORD-001
Scenario: Premium is calculated for a commercial-auto quote
When method post
Then status 200
# the run records the real call — coverage is observed, not asserted
# ORD-001 -> exercised (declared AND its real artifact ran)
# ORD-014 -> NEVER RUN (linked in the ALM, but no run touched it) ← the catchHow we make trust computable
Coverage and risk derive from recorded run evidence — the actual HTTP/gRPC exchanges — not a model re-reading source and guessing. The difference is a recording versus a guess.
API CoverageEvery requirement graded: genuinely exercised, partial, incidental, or never run. The deterministic detector for the agent-era failure mode — tests that grade the output, not the requirement.
Testing AI-generated codeThe release decision is versioned code, reproducible and hash-verifiable, and runs with no AI in the path — the answer you defend to an auditor. The model explains it; it never decides it.
Enterprise AI testingUse cases
When the agent writes the code and the tests, prove the requirement was actually exercised — not just that a green appeared.
TraceabilityA requirements-traceability matrix computed from real runs — the audit artifact, generated, not hand-maintained.
RegulatedA reproducible, no-AI verdict and trace for the regulated programs that must demonstrate requirement-to-verification coverage.
Point us at one of your services. We’ll show you what’s genuinely covered, what only looks covered, and whether it’s safe to ship — computed, not guessed.