Flexible data integration
Connect databases, files, or data warehouses. Genematon profiles, cleans, validates, and engineers features automatically — including across multi-table relational data.
The full pipeline: data cleaning, table flattening, feature engineering, training, deployment, monitoring, and retraining. Deploy production-grade classification, regression, forecasting, and text models instantly via our UI, MCP server, or REST API.
The pipeline beats your team would handle one engineer at a time, run automatically by an autonomous ML engineer.
Connect databases, files, or data warehouses. Genematon profiles, cleans, validates, and engineers features automatically — including across multi-table relational data.
Describe the task in plain English. Genematon writes the feature engineering code, evaluates candidates, tunes hyperparameters, and selects or creates the best-performing model for your specific data.
Work with relational tables and unstructured text in one pipeline. No flattening, no manual joins, no separate tooling for text-understanding workloads.
All platform functionality is exposed over MCP for agentic systems and a plain REST API for any service. Same operations, same response shapes — pick the transport that fits.
Agents call Genematon as an MCP tool. Post a data source and a task description; we run the full pipeline and return a deployed model.
Plain HTTPS endpoints for any service or language. Query progress, fetch results, manage deployments — same operations as MCP.
Every agent gets credentials with role-based access and full audit trails of all agent-initiated actions.
Once a solution is created, agents can spin up production deployments and tear them down when no longer needed. Full lifecycle control without manual intervention.
The boring infrastructure work that's the difference between a demo and a model in production.
Get predictions through a deployed API endpoint. Integrate models directly into your apps and systems.
Run scoring jobs on a daily, weekly, or monthly cadence. Useful for forecasting and recurring analysis.
Upload any file or dataset, get predictions back.
Track performance, catch regressions early, and detect when your data distribution shifts before it becomes a problem.
Every deployed ML API is secured with mandatory API keys scoped per endpoint.
When drift is detected, Genematon retrains automatically or rolls back to a known-good version.
Handle traffic spikes without overpaying. Resources scale up and down automatically.
If something goes wrong, backup deployments take over so predictions keep flowing.
Models stay accurate as your data evolves. Genematon retrains on policy or on detected drift.
Genematon runs on Kubernetes. Enterprise clients have the option to deploy a dedicated tenant inside their own environment — where data, compute, and pipelines never leave your perimeter — or use our fully managed hosted service. Developer-tier clients run on our shared hosted infrastructure.