LLM evaluation suite

1.2–3.2 weeks typical Fixed quote after scoping

An evaluation harness that answers 'is the AI good enough?' with evidence — scored real-world scenarios, a regression budget, and model/cost benchmarking run on every change.

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What we build

The honest answer to 'is the AI good enough to put in front of customers?' — a repeatable evaluation harness that runs realistic scenarios written in your customers' own words, scores the results against explicit standards, and enforces a regression budget so a prompt tweak or model swap can never quietly make things worse. The same harness benchmarks candidate models on quality, latency, and cost, turning 'which model?' from opinion into measurement. Our AI builds include an evaluation suite by default; choose this standalone when an AI feature already exists — built by anyone — and you need to know whether it works, keeps working, or could run cheaper.

What you get

  • An evaluation suite in a Git repository, runnable with one command
  • The suite running on every pull request in your CI
  • A scenario set drawn from your real customer messages
  • A written scoring rubric per scenario category
  • A regression budget encoded in CI that fails the build when scores drop below it
  • A full transcript saved as a CI artifact for each evaluation run

How the work unfolds

  1. Write evaluation scenarios in your customers' language
  2. Build the scoring harness
  3. Set the regression budget
  4. Benchmark candidate models on quality, latency and cost
  5. Wire the evals into CI
  6. Report results with full transcripts

What shapes the price

Before you see a number, our scoping conversation asks:

  • Roughly how many test scenarios should the suite cover — distinct situations your customers actually get into? Scenarios are the suite; each is written in the customer's words and scored.
  • Do you want us to compare candidate models, or are you committed to the one you use today? The model benchmark is up to three days and pointless if the model is already decided.

How an engagement starts

This work builds on Discovery workshop, so scope, boundaries and constraints are agreed before anything is built.

Teams often combine it with:

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