AI Evaluation

Zenjin AI Evaluation

Test the model you inventory, then close the loop.

Zenjin AI Evaluation attaches goal-based evals to models already in your catalog: quality, grounding, agentic behavior, safety, and protocol adherence. Library runs, diffs, and an improve loop become evidence, not a spreadsheet of golden prompts.

Request a demoAll solutions

At a glance

  • Quality

    In the library

  • Grounding

    Tied to catalog models

  • Safety

    Same run, same evidence

  • Improve

    Diffs after a bump

Goals on a named model, not a notebook

Evals attach to models already in inventory. You run library goals, see the diff after a silent bump, and keep the run as evidence.

Catalog-native

Evals attach to models in inventory, not a side database of names.

Library of goals

Reusable packs for quality, grounding, agentic planning, safety, and protocol, not a one-off golden set.

Results and improve

Diffs, regressions, and a loop to fix what failed, then attach the run to evidence.

Split from red teaming

Evals measure quality and policy; red teaming attacks. You need both.

Dark disc hanging in black space with quiet calibration marks
Request a demo

Today vs Zenjin

A golden set in a notebook is not evidence.

Today

Golden sets that die after a silent bump.

Golden sets live in notebooks. Nobody can show an auditor what “good” meant last quarter, or after a silent model update.

  • Notebook
  • Golden set
  • Last quarter
  • Stale
A quiet catalog orb on a black field

With Zenjin

Goals on a named model, then an improve loop.

Library, runs, results, and an improve loop tied to the same models Detect AI put in inventory.

  • Catalog
  • Library
  • Diff
  • Improve
A catalog model with two quiet goal chips

Catalog, goals, improve

Pick the catalog model, run the library, keep the diff.

  1. 01

    Pick from the catalog

    Evaluate a model you already own in inventory, not an anonymous API key.

    One catalog model ringed among quieter orbs

  2. 02

    Run goals from the library

    Quality, grounding, agentic behavior, safety, and protocol packs, with metrics and cases already attached.

    Three goal chips for Quality, Safety, and Protocol

  3. 03

    Improve

    Regressions and gaps feed the next run, and can map into compliance.

    A quiet improve orbit around a catalog model

Who it is for

  • ML
  • Product
  • GRC
  • Platform

FAQ

  • Evaluation measures expected behavior against goals (quality, grounding, agentic planning, safety). Agentic red teaming tries to break the system. Zenjin runs both against the same catalog.

  • Yes. Recorded runs against named goals become evidence objects, not a slide that says we tested it.

  • Reusable packs with metrics and cases already attached: quality, grounding, agentic behavior, safety, and protocol adherence. You run them against a model in inventory.

  • Scoring is one output. The product is catalog attachment, a goal library, diffs, regressions, and an improve loop that can map into compliance.

  • You re-run the same goals against the catalog entry. The diff is evidence of what changed, not a new spreadsheet.

  • Yes. Agentic planning and protocol packs are first-class, not only chatbot quality.

Related