Custom Evaluation Suite

Week 1: Discovery session to define use case, goals, and model context
Week 2: Draft of initial evaluation suite, with stakeholder review
Week 3–4: Iterative refinement + optional run on historical or shadow data
Week 5+: Integration into existing workflows or reporting cadence

Targeted evaluation infrastructure tailored to your use case, success criteria, and implementation.

We triangulate across key metrics to ensure you know your application is working as intended.

Our methods work with the data you're already collecting. No manual annotation, no curated datasets, just signal extraction from real-world use.

Built to plug into your current tools and workflows. We support integrations with GCP, Azure, Databricks, Jupyter, Arize, and more.

Our Process

We collaborate with your team to identify meaningful evaluation criteria based on your AI system’s purpose, risks, and expected outcomes. Then, we build a lightweight, testable suite that reveals not just how your model performs, but why. Whether you're pre-deployment, mid-rollout, or live in production, our custom suites give you the clarity to move forward with confidence.

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