AI Product Development
We build AI features into your product rather than bolting them on — recommendations, personalisation, and automation trained on your own data. Use-case definition, data pipelines, model selection, evaluation, and production deployment.
What this includes
- 01
We identify where prediction or generation actually changes a decision, and say so when simpler logic would do the same job cheaper.
- 02
Collection, cleaning, labelling, and the infrastructure to keep it flowing after launch rather than as a one-off export.
- 03
Hosted APIs, open models, or fine-tuning chosen on measured trade-offs instead of defaults.
- 04
Test sets, scoring criteria, and regression checks defined before the model ships, not after complaints.
- 05
Serving infrastructure, latency budgets, and graceful degradation for when the model is slow, unavailable, or wrong.
- 06
Scheduled evaluation against fresh data, with alerts when performance degrades below the agreed threshold.
Built with
- Use case definition
- Data pipeline
- Model selection and training
- Evaluation framework
- Production deployment