Case study · Software & AI Companies
Ship model-ready datasets and safer AI products faster
Engineers were spending most of their time cleaning and labeling data. There was no test set that reflected real customer cases, and quality problems were found by users. DataplexLabs built a repeatable data pipeline, created labeled evaluation sets and introduced regression tests before release.
A B2B SaaS company
Client details are anonymized at the client's request.
The challenge
- Engineering time was consumed by data cleaning instead of model work.
- No test set reflected the real edge cases customers encountered.
- Quality issues were reported by users before the team caught them.
What we did
- 1
Built a repeatable pipeline to collect, clean and version domain data.
- 2
Created labeled training, validation and test sets with clear documentation.
- 3
Introduced model evaluation and regression tests before every release.
Results
Model teams spend less time preparing data and more time improving models.
Evaluation sets cover the customer cases that matter.
Quality problems are caught before users see them.
Solutions and services used
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Next step
Discuss your use case
Bring one pain point, a data source, a workflow, a margin question. We'll come back with a focused assessment and a clear ROI hypothesis.