Your own ML platform — plus the best of every cloud.
A custom, portable ML platform for AI sovereignty with no hard cloud dependency — and pragmatic use of Vertex, Azure ML, and Bedrock where they earn their place.
Sovereignty, cost, and lock-in concerns make an all-in bet on one cloud ML service risky. But rebuilding everything yourself is slow. The answer is a portable core with managed services used deliberately, not by default.
If a few of these sound familiar, this is the work to start.
- 01Promising models never leave notebooks to influence real decisions.
- 02Data-sovereignty rules rule out sending data to one cloud service.
- 03You are worried about lock-in to a single vendor's ML stack.
- 04Models that did ship are quietly degrading with no one watching.
What we do
Sovereign ML platform
A cloud-portable platform — training, serving, feature store, registry — that runs on your infrastructure with no lock-in, for full data and model sovereignty.
Predictive analytics
Forecasting, churn, risk, demand, and anomaly detection models — built, validated, and put into production against real decisions.
Managed ML, used well
Vertex AI, Azure ML, and AWS Bedrock applied where they accelerate you — behind portable interfaces so you are never trapped.
MLOps & governance
Reproducible pipelines, monitoring for drift, and model governance so ML is dependable in production, not just a notebook.
A public-sector team under a data-sovereignty mandate needs forecasting without shipping data to a hyperscaler's black box. We stand up a portable platform on their own infrastructure, build and shadow-deploy the models, and use managed services only behind interfaces they can swap. They get production ML and keep full control of their data and models.
Illustrative scenario — not a specific client.
Frame the decision the model will drive and define what "good" means.
Stand up the portable platform and data/feature foundations.
Build, validate, and shadow-deploy models before they influence outcomes.
Operationalize with monitoring, retraining, and governance.
Concrete artifacts, not a slide deck.
- A portable ML platform — training, serving, feature store, registry.
- Validated models running in production against real decisions.
- Drift and performance monitoring with a clear retraining path.
- Managed-service integrations behind portable interfaces, no lock-in.
- Governance — lineage, versioning, approvals — for every model.
Good questions.
Do we need our own platform, or just a cloud service?
Both, deliberately. We use Vertex, Azure ML, or Bedrock where they accelerate you, behind portable interfaces, so you get the speed without being trapped.
How do you keep models trustworthy over time?
Every model ships with drift and performance monitoring and a retraining path, plus lineage and versioning — so degradation is caught and corrected, not discovered by users.
What about generative AI and LLMs?
We apply them where they fit, grounded in your data with evaluation and guardrails. For agents that take actions, see our Agentic AI capability.