Evaluation design from real tasks
We start from what your users actually ask and what 'good' means in your domain, and turn that into test cases, rubrics and pass thresholds.
Service
Know whether your AI works before your users find out.
AI systems fail differently from ordinary software. The same input can give different outputs, quality drifts when a model or prompt changes, and a confident wrong answer looks just like a right one. Evaluation is how you turn 'it seems fine' into evidence you can act on.
How it fits together
Who it is for
- Teams shipping an LLM feature, chatbot, RAG assistant or agent
- Companies choosing between models or vendors and needing a fair comparison
- Product owners who need a quality bar they can defend to customers and auditors
- Teams that changed a prompt or model and cannot tell whether things got better or worse
Problems we see
- Quality judged by a handful of manual spot checks
- Every prompt or model change is a gamble, with regressions found by users
- No test for what the system should refuse to do
- Retrieval quality and answer quality blamed on each other because they are never measured separately
What we do
What is included.
Golden datasets
Curated, versioned test sets drawn from real or realistic inputs, with expert-reviewed expected outcomes and coverage of edge cases.
Automated graders, calibrated to humans
Code-based checks and model-based graders, validated against human labels so you know how far to trust each score.
RAG evaluation
Retrieval and generation measured separately: did the right passages come back, is the answer grounded in them, and does the system say so when the sources do not cover the question.
Agent evaluation
Tool selection, argument correctness, multi-step trajectories and task completion, plus checks on when the agent should stop and hand off to a person.
Safety, refusal and red-team testing
Prompt-injection attempts, data-leak probes, off-policy requests and abuse cases, with results tied to concrete fixes.
Regression checks in CI
Evaluations that run on every prompt, model or retrieval change, so a drop in quality blocks the release instead of reaching users.
Production monitoring
Sampling of live traffic, drift detection and feedback loops that turn real failures into new test cases.
How it runs
The process, step by step.
- 01
Scope
Define the system, its users, the failure modes that matter and the quality bar.
- 02
Build the test set
Collect and label cases, including the hard ones, with your domain experts.
- 03
Build the graders
Implement automated checks and calibrate them against human judgement.
- 04
Baseline
Run the current system, report where it is strong and where it breaks.
- 05
Automate
Wire the suite into your release process and set up production monitoring.
Deliverables
What you walk away with.
- Evaluation plan: metrics, thresholds and what each one protects against
- Versioned golden dataset and labelling guidelines
- Grader implementations with calibration results
- Baseline report with failure analysis and prioritised fixes
- CI integration so the suite runs on every change
- Runbook for extending tests as the product grows
Questions
AI Evaluation: common questions
What is AI evaluation?
AI evaluation is the practice of measuring how well an AI system performs on the tasks it is meant to do. It uses test datasets, scoring methods and thresholds to check quality, safety and consistency, and to detect regressions when a model, prompt or data source changes.
How do you evaluate an LLM application?
You collect a set of realistic inputs, define what a good output looks like for each, score the system's outputs with a mix of code-based checks, model-based graders and human review, and track the results over time. Retrieval, generation and tool use are measured separately so failures can be located.
Can you trust an LLM to grade another LLM?
Only after checking it. We compare the grader's scores to human labels on a sample, measure agreement, and adjust the rubric until it is reliable enough for the decision it supports. Graders are re-checked when the system or the grader model changes.
We already have an AI product in production. Is it too late?
No. We build the test set from real traffic and known failures, run a baseline on the live system, and add regression checks going forward. Starting from production data usually makes the first test set better.
Do you evaluate systems you did not build?
Yes. We work on systems built in-house, by other vendors, or on top of third-party models, as long as we can send inputs to it and observe outputs.
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