Evaby

AI evaluation for teams

Your work is the benchmark.

Leaderboards tell you which model wins a quiz. Eva tells you which AI setup does your team's work well enough, at what cost and in how much time, reviewed blind by the people who do that work.

See how it works

For invited teams. Your administrator gives you access to a workspace.

Meeting synthesisRevealed
SetupAcceptedTimeCost
Current setupthe one you use today7 of 1041 s$0.12
Challenger Aanother vendor9 of 1018 s$0.04
Challenger Bsmaller model5 of 109 s$0.01
Illustration. 5 cases, 2 runs each, 2 blind reviewers. Cost per accepted output.

Why your own benchmark

A model that wins a leaderboard can still lose on your Tuesday.

Leaderboards are not your work

Public benchmarks test olympiad maths and trivia, and may already sit in a model's training data. The top ranked model can lose on your tasks, especially the subjective ones.

One good answer is luck

The same model answers the same prompt differently. Eva runs every case several times, so you see how a setup behaves, not how it behaved once.

Better is not always worth switching

A setup that scores a little higher can cost far more or take far longer. Eva puts quality, time and cost side by side before anyone decides.

How it works

From real tasks to a decision your team trusts.

  1. 01

    Collect the work you really do

    Turn recurring tasks into use cases with real examples and a rubric, including the tasks AI got wrong last month. A manager approves each version before it runs.

  2. 02

    Clear the data first

    Case content goes to a model provider only with a manager's clearance, and only through connections that deny data collection or require zero retention when the content needs it.

  3. 03

    Run your setup against the challengers

    Your current setup and the alternatives run on the same cases, several times each, through your own OpenRouter account and within the budget you set. Results from other tools can be imported.

  4. 04

    Review blind

    Outputs are anonymised and shuffled. Nobody sees which model wrote what, or how others scored, until every assigned reviewer has submitted or a manager closes the round.

  5. 05

    Decide with the evidence

    Quality, time, tokens and cost appear side by side for the people who took part. Record whether you keep, switch or split, and rerun the suite when the next model arrives.

Built for teams, not leaderboards

Everyone reviews. The evidence stays with the team.

  • Your keys, your limitsClient owned OpenRouter keys, budgets per run and per workspace.
  • Roles that fit a teamUsers suggest and review; managers approve, launch and decide.
  • A review inboxEvery assigned output in one place, with deadlines and reminders.
  • Results for participantsComparisons are shown to the people who took part, not to everyone.
  • Data policy per connectionDeny provider data collection or require zero data retention.
  • Open to your own toolsEvery operation is also available through MCP and the API.

Start with the task AI got wrong last week.