AI Evaluation & Assurance · Model selection evidence

LLM Evaluation & Benchmarking

Every candidate runs the same representative tasks, slices, rubrics, safety constraints, and cost conditions. A public leaderboard cannot do that for your workload, users, language mix, and latency budget.

A leaderboard can't tell you which model fits your workload, users, language mix, and latency budget. We run every candidate on the same representative tasks, slices, rubrics, safety constraints, and cost conditions, then show what each option gains and what it gives up to get it.

Your product owner chooses tradeoffs from a benchmark someone else can rerun, with per-slice exceptions and the rejected candidates recorded next to the chosen one.

Discuss this workSee the evaluation service
Illustration of LLM Evaluation & Benchmarking: a team testing an AI system against representative evidence

Some of the 500+ brands we've worked with

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  • BMW
  • KPMG
  • İstikbal
  • Sporx
  • Hotiç
  • HDI Sigorta

Steps, gates, and who decides

How we work

Comparable evidence comes before ranking. Each candidate runs the same workload under the same recorded review rules, or the comparison means nothing.

  1. Define the selection decision

    We agree the workload, critical slices, quality and safety criteria, latency and cost boundaries, operating constraints, and decision owner.

    AI assist
    The stated product decision and constraints become draft criteria the product owner corrects.
    Human gate
    Do the criteria reflect the product decision rather than a generic benchmark? Your product owner confirms the criteria reflect this decision, not a generic benchmark.

  2. Build the shared benchmark

    We prepare representative cases, rubrics, run settings, dependency notes, and failure examples that apply equally to each candidate.

    AI assist
    The shared case set and run-condition notes arrive pre-assembled for each candidate.
    Human gate
    Can every candidate be tested under comparable recorded conditions? Your evaluation lead confirms every candidate runs under truly comparable conditions.

  3. Run and challenge the comparison

    We test quality, critical slices, safety constraints, latency, and cost, then inspect exceptions and repeated-run variation.

    AI assist
    Candidates whose apparent gains wobble across repeated runs get flagged before anyone reads a ranking.
    Human gate
    Are apparent gains stable, and what does each candidate give up to achieve them? Your product owner decides which tradeoff each candidate is actually making.

  4. Record the recommendation

    We document the evidence, limitations, dependencies, open exceptions, acceptable tradeoffs, and the owner-approved selection or next test.

    AI assist
    The accepted evidence and tradeoffs become a draft decision record for the product owner.
    Human gate
    Which candidate and conditions does your product owner accept? Your product owner accepts the selected candidate or orders another test.

Named artifacts you keep

What you get

The recommendation stays open to inspection, including the assumptions and tradeoffs underneath it.

  • Dashboard

    Reproducible LLM benchmark and limitations report

    Records the candidate versions, workload, run conditions, results, critical slices, and limits of the comparison.

  • Risk register

    Where the benchmark evidence came from

    Lists source cases, evaluation assumptions, provider or system dependencies, missing evidence, and material constraints.

  • Report

    Per-slice readout with the exceptions found

    Breaks out quality, safety, latency, cost, and failure cases with accepted and unresolved exceptions.

  • Decision record

    Handoff note on the chosen candidate and its tradeoffs

    States the selected candidate or next test, accepted tradeoffs, conditions, owners, and review trigger.

Scope and honest limits

When to bring us in

Public scores and vendor demos are useful context, but they can't choose a model for your workload, users, language mix, and operating constraints. This work exists for that decision.

A good fit when

  • Candidate models look similar on generic scores but behave differently on your tasks and critical slices.
  • Quality is only half the question, because a model that answers well can still miss your latency budget or cost more per call than the workload can carry.
  • The product owner will accept a tradeoff rather than wait for a universal winner, so the comparison has to show what each option gives up.
  • Several candidates are in play, but no one has run them on the same tasks, slices, rubrics, safety constraints, latency ceiling, and cost basis.
  • Each team has evidence for its favorite model, while nobody has matched the workload, run conditions, or slices those numbers came from.
  • A recommendation will have to be defended later, so it needs acceptance tests, recorded exceptions, and a named owner attached to it.
  • The comparison has to be repeatable, because provider versions move and last quarter's numbers no longer prove anything.

Better handled as other work when

  • The benchmark has to stand as an audit artifact or regulatory filing. It records model behavior under stated conditions, not a certification anyone can rely on.
  • You want to be told which model is best, full stop. The answer here holds for one workload, one language mix, and the versions we recorded.
  • The next step is migration or a commercial negotiation. Benchmarking ends at the recommendation unless production work is scoped alongside it.

If one of these is closer to your situation, start here instead: See the evaluation service

We operate the systems we test

It's hard to test a system well if you've never had to keep one running. We operate production AI ourselves, so our evaluation, security testing, and LLMOps work starts from what actually breaks. The people on it are senior engineers, and Zeo has been doing client work since 2011.

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Tools we use

Tools behind this work

  • Google Gemini

    one of the candidate models tested under the same shared benchmark conditions

  • OpenRouter

    reaches every candidate model across providers through one integration

  • Groq

    serves open-weight candidates at real low-latency conditions for the comparison

  • Braintrust

    runs every candidate through the same tasks, slices, and rubrics in one harness

  • Helicone

    measures each candidate's real cost and latency under the same conditions

  • Patronus AI

    scores safety constraints across candidates as its own axis, separate from quality

Next step

Choose the tradeoff your product can live with

Bring the candidates, the workload they would actually run, and the owner who must choose the operating tradeoff.
Talk to Zeo

Before you decide

Questions we hear a lot

Approved access to the candidate LLMs, representative tasks and examples, critical slices, rubrics, safety constraints, latency and cost boundaries, baseline evidence, operating dependencies, and whoever is authorized to accept the result.