Golden datasets
Curated, versioned test sets built from your real traffic and expert judgment — the ground truth for every decision.
AI Infrastructure
AI Evaluation & Testing
Ship with confidence using offline and online evaluations, red-teaming, and continuous regression suites.
- 100sof eval cases per critical workflow
- 0silent regressions reaching production
- <10 mineval feedback in every CI run
Capabilities
Ship AI changes with evidence, not hope
Quality gates, eval harnesses and regression testing for production AI systems.
Automated eval pipelines
LLM-judge and programmatic scoring wired into CI, so every prompt, model or code change is gated.
Human review workflows
Calibrated expert review where automated judges aren't enough, feeding back into the golden set.
Production evaluation
Live sampling and scoring of real traffic to catch drift before users do.
How it works
The test suite your AI never had
Traditional tests check code paths. AI needs its outputs judged — for correctness, groundedness, safety and tone — on every change and continuously in production.
Golden sets
Ground truth built from your real work
We mine production traffic, escalations and expert knowledge to build test sets that represent reality — including the adversarial and edge cases that break demos.
- Representative cases mined from real usage
- Edge cases and adversarial inputs deliberately included
- Versioned datasets that grow with every incident
DocsCRMWiki
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CI gates
No prompt change ships unmeasured
Every change — prompt, model, retrieval config — triggers the eval suite. Scores below threshold block the merge. Your team iterates fast because the safety net is automatic.
- Eval runs on every PR with pass/fail gates
- Side-by-side diffs of output quality between versions
- Cost and latency tracked alongside quality
Groundedness
96%
Safety
100%
Task success
93%
Tone & format
91%
CI gate passedrelease promoted to production
Production truth
Catch drift before your users report it
Sampled live traffic is scored continuously. When quality dips — new input patterns, model updates, data drift — you get an alert with examples, not a support ticket a month later.
- Continuous scoring of sampled production traffic
- Drift alerts with failing examples attached
- Feedback loop from escalations into the golden set
99.9%Uptime
1.2sp95 latency
$0.021Cost / task
QualityCostLatencyDrift
Use cases
Where evaluation & testing delivers value
Pre-launch validation
Prove an agent or copilot meets the bar before it ever meets a customer.
Model migration
Swap models with side-by-side evidence instead of crossed fingers.
Prompt engineering at scale
Let teams iterate on prompts safely behind regression gates.
Vendor evaluation
Score competing AI products on your tasks before you buy.
Safety and compliance sign-off
Documented eval evidence for risk, legal and audit stakeholders.
Production monitoring
Continuous quality scoring wired to alerting and dashboards.
Delivery
How we build it
We start from the business outcome, then design agents, models, tools and guardrails that can survive production — not just a demo.
- Production-ready architecture
- Secure tool integrations
- Measurable business KPIs
- Operate & improve playbooks
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1
Discover
Map workflows, data, constraints and ROI.
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2
Architect
Define models, tools, memory and trust boundaries.
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3
Build
Ship a production-ready system with evals and observability.
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4
Scale
Optimize cost, quality and adoption across teams.
We went from arguing about anecdotes to reading scoreboards. A model upgrade that would've taken six weeks of debate shipped in four days — the evals settled it.
Works with your stack
Built on the tools you already run
We integrate with your models, clouds, data platforms and enterprise systems — no rip-and-replace.
- Anthropic Claude
- OpenAI
- Braintrust
- LangSmith
- Weights & Biases
- pytest
- GitHub Actions
- GitLab CI
- Datadog
- Grafana
- Postgres
- dbt
FAQ
Common questions
Can LLM judges be trusted to grade LLM outputs?
With calibration, yes — for most criteria. We validate judges against human expert labels until agreement is high, use programmatic checks where possible, and keep humans in the loop for high-stakes or ambiguous criteria.
How many test cases do we need?
Meaningful signal starts around 50–100 well-chosen cases per workflow; critical systems grow into the hundreds. Coverage of edge cases matters more than raw volume.
We already have unit tests. Isn't this the same?
Unit tests check deterministic code. Evals judge probabilistic outputs against quality criteria — correctness, groundedness, safety, tone. You need both; they catch different failures.
How does this fit our existing CI/CD?
Evals run as a pipeline step in GitHub Actions, GitLab or Jenkins, with thresholds as merge gates. Engineers see quality diffs in the PR, next to the code review.
Next step
Stop shipping AI on vibes
We'll build your first golden set and CI eval gate in weeks — then every change comes with evidence.