Dataset engineering
Splits, balancing and augmentation strategy.
From dataset to evaluated model
Model Training Support
Dataset engineering, evaluation design and error analysis alongside your ML team.
Support Where the Data Meets the Model
The handover from data to training is where most of the value leaks. Splits that leak, evaluation sets that flatter, error analysis that never happens. We work on that seam with your team rather than delivering a folder and leaving.
What Is Included
Evaluation design
Test sets that measure the thing you actually care about.
Error analysis
Structured investigation of what the model gets wrong and why.
Targeted data collection
New data commissioned against measured weaknesses.
Benchmark maintenance
A stable benchmark that survives model iterations.
Retraining cycles
Scheduled refresh as the domain shifts.
How We Work
- Leak-free splits Grouped by source, session or subject so nothing leaks across.
- Slice-level evaluation Performance reported by condition, not just in aggregate.
- Failure-driven iteration The next dataset comes from the last evaluation.
- Frozen benchmarks The evaluation set is not touched between iterations.
The Controls Behind It
- Documented methodology How splits and metrics were built is written down.
- Reproducible sets Versioned datasets with checksums.
- Statistical honesty Confidence intervals reported alongside headline numbers.
- Regression tracking What got worse is reported as prominently as what got better.
Ready When You Are
Tell us what you are building and we will come back with a plan, a sample and a price.