Point at your data.
Get the best model.
Context Flow turns a dataset into a ranked field of trained models — on AWS, Google Cloud, Azure, or hardware you own. One workflow and four engines, with the execution boundary made explicit for each provider.
By CirrusLabs · Deploy to managed cloud targets, your VPC, or fully air-gapped.
Four steps, no notebook required
The whole loop lives in one screen — upload, train, compare, deploy — and the same workflow drives every engine.
Bring your data
Drop a CSV or Parquet file. Context Flow profiles every column, flags missing values, and infers whether you’re predicting a class or a quantity — you confirm it in a click.
Choose an engine
Pick the cloud you already pay for, or your own hardware. Set a budget — Fast, Balanced, or Best quality — and Context Flow caps the run so training can’t overspend.
Read the leaderboard
Candidates appear as they finish, ranked on the selected engine’s validation metric, alongside training time and the features that drove the predictions.
Deploy and score
Put the winner behind a real-time endpoint in one click, test a prediction from the browser, then graduate the whole experiment into a full pipeline when you’re ready.
Your cloud. Your call.
Most AutoML platforms are an engine you rent. Context Flow is a control plane over engines you connect or configure, and it shows the execution boundary for each one.
SageMaker Autopilot, with the full candidate leaderboard and one-click real-time endpoints in your account.
AutopilotVertex AutoML tabular training and Vertex endpoints, driven from the same screen as every other engine.
Vertex AIAzure Machine Learning AutoML, whose child runs give the richest candidate field of the three clouds.
Azure MLAn open-source engine in a container you run — on your own hardware, with no egress at all. Serving included.
Air-gappedMore than AutoML
AutoML is the fastest way in. Underneath the leaderboard is a workspace for building, deploying and governing AI across your organisation.
Governed by default
Every workspace is isolated and every action is permissioned. AWS, GCP, and Azure use validated workspace connections; on-prem stays fully offline.
Pipelines, not one-offs
Graduate any experiment into a full pipeline — ingestion, preprocessing, training, evaluation, registry, deployment, monitoring — in a visual designer.
Agents and RAG
The same workspace builds retrieval pipelines and AI agents, so predictive models and generative systems live under one governance plane.
Comparable metrics
Every engine reports through one metric schema. On-prem uses a Context Flow holdout; cloud engines report their own validation scores.
Model cards on demand
Generate a written summary of what was trained, how it performed, and where it shouldn’t be trusted — built strictly from the run’s own recorded numbers.
Reversible by design
Endpoints are tracked, teardown is confirmed against the provider, and nothing is quietly abandoned in an account that keeps billing for it.
A control plane, not another silo
The usual trade-off is a great engine that wants your data, or your infrastructure with none of the workflow. Context Flow refuses the trade.
The hosted-platform pattern
- One engine, one vendor — you adopt their runtime along with their workflow.
- Training data moves to their platform, and your compliance boundary moves with it.
- Cloud commitments you’ve already made go unused.
- Predictive and generative AI end up in separate tools with separate governance.
The Context Flow pattern
- Four engines behind one workflow — switch clouds without relearning anything.
- AWS, GCP, and Azure run through validated workspace connections. On-prem stays fully offline.
- Managed training bills to the cloud target configured for that engine.
- Models, pipelines, agents and retrieval under a single governance plane.
Control plane here. Execution there.
Context Flow holds the workflow, metadata and governance. Training and serving run on the configured target for each engine.
How a run is executed
Experiments, leaderboards, pipelines, permissions and audit — the surface your teams work in.
One contract every engine implements: submit, poll, deploy, score, tear down. Adding a cloud changes nothing upstream.
Managed cloud AutoML, or a container on hardware you control. AWS credentials are workspace-scoped and short-lived. GCP and Azure use encrypted, validated workspace credentials during the current pilot.
See it on your own data
Bring a dataset to a 30-minute session and we’ll train, compare and deploy a model in your environment — on the cloud you already use, or entirely offline.