Production ML used to require a full team — data engineers, ML engineers, MLOps, infrastructure. Genematon collapses that into a single autonomous engineer that takes you from data to deployment, end to end.
Genematon designs custom architectures and code tailored to your data and outcome — and runs the infrastructure to deploy and monitor what it builds.
No templates or one-size-fits-all models. Every pipeline is generated specifically for your data structure and the outcome you described.
Choose batch processing or API deployment. Hosting, monitoring, drift detection, and failover are configured automatically.
What's generated is what ships. No separate prototype-to-production phase. No second toolchain to maintain.
You focus on the modeling problem. Genematon creates and owns the engineering complexity that usually requires specialized headcount.
Exposed as both an MCP server and a REST API. Agents and services call Genematon when they need a deployed pipeline, then continue working while the ML runs out-of-band.
Pointed at its own codebase, Genematon's debug success rate went from 30% to 80%. Recursive self-improvement, in production.
Production ML should be a layer in the stack, not a six-month engineering project — accessible to ML teams, services, and AI agents on equal footing.
Whether you're a data scientist driving from a UI, a service hitting our REST API, or an agent calling our MCP server, Genematon collapses the gap between describing the outcome and shipping the full pipeline.
AutoML picks from a fixed library. Genematon writes the library.
Works with complex relational data automatically, whether tables are related or independent. Most AutoML tools require you to flatten everything into a single table first.
Traditional AutoML picks from a fixed library of predefined models. Genematon writes new architectures and training code tailored to your problem.
Classification, regression, forecasting, text understanding — built, deployed, and monitored from the same platform. No stitching tools together.
Most ML platforms are built for human operators only. Genematon ships a UI, an MCP server, and a REST API so any agent or service can request, create, and manage pipelines directly.
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 with strict tenant isolation, encryption in transit and at rest, and ephemeral execution sandboxes for any code Genematon generates.
Genematon’s reasoning engine runs entirely on open-source LLMs to design architectures, generate training code, and manage deployments.
We adapt to your security requirements. In our managed cloud, you can easily upload files or connect to databases like Delta tables and SQL to get started fast—and rest assured that the engine only passes schemas and minimal samples to the LLMs, never your bulk datasets.
When strict data privacy is paramount, you have the ultimate escape hatch: deploy Genematon into a fully isolated VPC. By self-hosting the LLMs directly alongside your data, you guarantee that your compute and proprietary datasets never leave your tenant.
No deprecation calendars. No silent retraining. No surprise pricing changes inside the platform you depend on.
Or reach out directly at earlyaccess@genematon.com