AI Companies
Models serving predictions in the real world
Ship models to production, not notebooks.
You have models that work in a notebook. The gap is everything between a promising experiment and a system that serves predictions reliably, at scale, with the observability and cost control production demands.
The hard parts, and how we take them on.
Research code that never hardened into a deployable service
We wrap your models in typed, tested inference services with autoscaling
Inference cost and latency that break the unit economics
MLOps pipelines for training, evaluation, deployment and drift monitoring
No versioning, monitoring or rollback for models in production
Cost and latency budgets enforced from day one, not discovered in the bill
Everything the platform needs.
Inference Services
Typed, autoscaling APIs around your models
MLOps Pipelines
Training, eval, deploy and rollback, automated
Drift Monitoring
Catch data and model drift before users do
Feature Stores
Consistent features across train and serve
Cost Optimisation
Right-sized GPUs and caching for unit economics
Eval Harness
Automated evaluation and safety guardrails
How the data flows.
The stack we reach for.
How we deliver, end to end.
Discovery
Research
Design
Development
Testing
Deployment
Support
Built to pass audit.
Ready to modernise AI Companies?
Tell us the problem other teams called impossible. We'll show you how we'd solve it.