Services · Deploy
Ship AI changes with confidence
AI systems need testing beyond unit tests: data slices, adversarial prompts, fairness checks, and online/offline parity. We build automation that runs in CI/CD and catches regressions before customers do—especially critical for LLM and retrieval stacks that change frequently.
Testing strategy for intelligent systems
Blend traditional software tests with evaluation datasets, human review sampling, and monitoring-based validation.
Offline evaluation
Golden sets, scenario libraries, and metric dashboards for precision/recall, toxicity, and task success.
CI integration
Gates on pull requests for model, prompt, and retrieval changes with reproducible environments.
Safety & fairness
Targeted probes for bias, jailbreaks, and policy violations with escalation workflows.
Production validation
Shadow traffic, canary analysis, and automated rollback triggers tied to SLOs.
What we implement
Tooling selection, harness development, and process design for your release cadence.
Test harnesses
Reusable frameworks for comparing model versions and prompt variants.
Data generation
Synthetic and semi-synthetic datasets to expand coverage responsibly.
Human-in-the-loop QA
Sampling plans and labeling workflows integrated with engineering sprints.
Reporting
Dashboards for quality trends and release readiness reviews.
Why it matters
Without automated evaluation, every change is a gamble—velocity collapses under manual QA.
- Faster releases with explicit quality bars
- Fewer customer-visible regressions on model updates
- Clear accountability when incidents occur
- Better collaboration between QA, DS, and product
Best fit
Teams shipping LLM features, recommendation systems, or high-stakes classification at velocity.
Related services
Works alongside MLOps and generative AI programs.
Automate quality for AI
Tell us about release frequency and risk—we will design a test pyramid that fits.