Custom Evaluation Suite

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.

Sample Timeline

  • 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

Sample Deliverable

  • A tailored evaluation dashboard or PDF report with:
    • Custom metric definitions and visualizations
    • Precision/recall, suitability scores, and efficiency tradeoff charts
    • Highlighted edge cases and segment-specific findings
    • Actionable recommendations for improvement or expansion
  • Optional: API endpoints or exportable formats for internal tools
  • Get started with
    Custom Evaluation Suite
    Custom Quality, Suitability, and Efficiency Metrics
    Lightweight Deployment Without Required Labeling
    Direct Integration With Your Stack
    Get started now

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