What our work and the result look like.
Four common tasks and what the client gets. Below are samples of the result format — a real report is built the same way, on your data.
Visual evaluation sprint: which model version is better?
Task
An image-model team ships a new version and isn't sure it improved on the properties that matter for the product.
What we do
We define the dimensions, build a reference prompt set, run both versions and compare with expert ratings — blind, with quality control.
| Dimension | Version 08 | Version 09 | Δ |
|---|---|---|---|
| Prompt adherence | 82.4 | 88.7 | +6.3 |
| Anatomy | 74.1 | 79.5 | +5.4 |
| Text rendering | 68.9 | 68.1 | −0.8 |
| Style stability | 80.2 | 85.6 | +5.4 |
Illustrative values · each with a confidence interval (e.g. 88.7 [85–91]) · verdict: v09 is better everywhere except text rendering → priority fix.
Where an AI agent breaks: a failure map.
Task
A product has an agent in production (support, assistant), but it's unclear in which scenarios it fails and how often.
What we do
We run the agent through real scenarios, label the trajectories with people and group the errors into a clear map with a frequency for each type.
Illustrative frequencies · each mode carries example traces, the first unrecoverable step and a fix recommendation.
"Which is better" data for tuning a model's taste.
Task
A team needs quality comparisons of answers/images to tune a model toward user preferences.
What we do
We prepare pairs, collect rated judgments with rationale, clean out ambiguity and disagreement, and deliver a versioned set on your training cadence.
- "Chosen / rejected" pairs with a short reason for the choice
- Scores by dimension, not one overall number
- Rater agreement and confidence per pair
- Training-ready format + a quality report
Rescuing a broken dataset or vendor.
Task
There's a large labelled set, but the model doesn't improve from it — or a vendor is missing quality.
What we do
A fast sample audit by source, worker and type; we find instruction defects, fraud, drift — and relabel only what affects the result.
- A report: where and why quality is lost
- An estimate of the defect rate and the "expensive" slices
- A recovery plan — what to fix first
- Rewritten instructions and acceptance criteria