The unglamorous foundation every AI system depends on.
Data pipelines, feature stores, model registries, and the observability that keeps production AI honest. This is the capability that makes every other one maintainable — the reason your AI systems don't silently rot.
Models die quietly.
- Data scientists train on one dataset, production uses another — subtly.
- No one notices when accuracy drifts until a customer complains.
- Feature engineering lives in jupyter notebooks on someone's laptop.
- Nobody can reproduce the exact run that's deployed.
- Cost is "whatever the cloud bill says this month."
Models as governed products.
- Training-serving skew impossible by construction — same feature code both sides.
- Drift caught automatically. Alerts go to the right team with context.
- Features are versioned, tested, reusable across models.
- Every deployment linked to the exact code, data, and hyperparameters.
- Per-call cost visible. Model changes evaluated on cost AND quality.
Data readiness assessment
We review your data sources, quality, access, lineage, and platform maturity. Score your AI-readiness on a concrete rubric. Recommend the smallest set of platform work required before you can safely ship AI.
Minimal chassis for one system
Not a full platform — just the pipelines, registry, and monitoring required to run your specific model in production safely. Often 3–4 weeks of the integration engagement.
Full platform v1
A shared data + AI platform that every subsequent system runs on. Feature store, registry, serving, monitoring, FinOps. Typically Q1 of a transformation programme.