EVAL-STACK

Continuous evaluation for RAG and Agentic AI

EVAL-STACK continuously evaluates your RAG and agentic AI applications, providing evidence that quality, safety, and reliability remain intact as your system evolves

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WHY EVAL-STACK

Built for continuous AI assurance

Technique-Driven Evaluations

Select the right evaluation techniques for your AI application, not just generic benchmarks

Continuous Execution

Automatically re-run evaluations whenever prompts, models, knowledge, or workflows change

Evidence-Based Scoring

Measure quality, safety, and robustness using industry-standard metrics with actionable evidence

How Eval-Stack Works

From strategy to continuous assurance.

  1. 1

    Understand Your AI

    Map the system, data, and use case.

  2. 2

    Infer Capabilities

    Identify what must be assured.

  3. 3

    Select Techniques

    Choose eval methods that fit risk.

  4. 4

    Generate Plan

    Build a scoped evaluation plan.

  5. 5

    Execute Continuously

    Run on every meaningful change.

  6. 6

    Produce Evidence

    Ship decisions with proof.

Why Eval-Stack is Different

Traditional evaluation tools

  • Static benchmark scores
  • One-off pre-launch checks
  • Generic, one-size metrics
  • Manual result interpretation
  • Breaks when models change

Eval-Stack

  • Capability-aware evaluations
  • Continuous runs on every change
  • Technique-driven test design
  • Automated evidence packs
  • Keeps pace with model drift

See Eval-Stack in Action

Built to Fit Your Stack

  • CI/CD
  • Jenkins
  • API & SDK
  • Langfuse
  • Phoenix
  • Google
  • Microsoft
  • OpenAI

Need help shaping your AI evaluation strategy?

Tell us what you are evaluating today and where you need more signal

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