Test what you can’t predict.
For teams shipping LLM features. Evaluation for features you can’t predict.
- Timeline
- 3–6 wks
- Team
- 2
- From
- Scoped after a call
- Platforms
- LLM features
ProblemSound familiar?
- Prompts changed without review
- No regression signal for AI output
- Hallucinations reported by users
We engineerWhat we engineer
Golden datasets, rubric-based evals and red-team suites that run in CI, so model and prompt changes are measured before they ship.
ArchitectureTypical architecture
- Datasets
- Eval harness
- CI gate
- Drift monitoring
CapabilitiesCapabilities
- Eval design
- Golden datasets
- Red-teaming
- Drift monitoring
DeliverablesDeliverables
- Eval suite
- CI gate
- Quality dashboard
ProcessProcess
Discover
We learn the business before the brief: the metric that matters, the constraints, the people involved.
- Kick-off workshop with your decision-makers
- Access to analytics, code and tools
- Agree the one metric this work must move
- Kick-off notes and a shared goal
- Access checklist
- Named pod lead
We learn the business before the brief: the metric that matters, the constraints, the people involved.
What happens- Kick-off workshop with your decision-makers
- Access to analytics, code and tools
- Agree the one metric this work must move
- Kick-off notes and a shared goal
- Access checklist
- Named pod lead
StackStack
Playwright for most web suites, Cypress where it is already established, plus k6 for load and our own AutoGherk for scenario generation.
The first critical-path flows run in CI within two weeks. Coverage then grows on a published weekly plan.
If you want us to. Many clients pair QA automation with a Software & Infrastructure pod for fixes.
Start with ai testing.
