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06 / AI Systems / Model Training
AI Model Training & Evaluation Platform
A specialist AI team had raw conversations, documents and domain examples but no consistent process for preparing datasets or measuring model quality. We built a human‑in‑the‑loop evaluation platform around the full improvement cycle.
18,600examples reviewed in the first evaluation cycle
27%reduction in critical evaluation errors
4.2×faster benchmark turnaround
What we built
A system designed around the actual work.
- Dataset ingestion and normalisation
- Annotation workflows
- Review and adjudication queues
- Dataset versioning
- Benchmark test sets
- Model output comparison
- Error taxonomy
- Quality dashboards
How it works
From input to action through one connected flow.
01
PREPARE
Clean and structure source data
02
ANNOTATE
Route samples through human review
03
EVALUATE
Score model outputs against benchmarks
04
IMPROVE
Feed validated failures into the next dataset version
Delivery scopeSolution architecture, workflow design, AI integration, application engineering and production implementation.
TechnologyPython · PostgreSQL · Annotation UI · Evaluation API · ML tooling
Measured outcome
Operational work became visible, measurable and easier to scale.
The team replaced disconnected spreadsheets with a repeatable evaluation loop. Dataset versions, reviewer decisions and model results could be compared in one place.