Connect your data and describe the task. Genematon builds the full ML pipeline—from data cleaning and feature engineering to model creation and hyperparameter tuning. Deploy production-ready solutions on demand via UI, MCP server, or REST API.
Stop wrestling with ML pipelines and infrastructure, and start shipping solutions faster.
Choose your input file, name the job and describe what you want to create
Works seamlessly with your AI stack
Today’s AI agents are brilliant at logic and code, but they fail at hard numbers. You can't prompt-engineer your way to an accurate revenue forecast, a reliable fraud detection model, or a precise customer churn prediction.
Genematon provides the missing mathematical layer. Instantly provision expert-level classification and regression models via API or MCP. No infrastructure to manage, no pipelines to build—just give your agents the predictive power they need to execute complex, data-heavy tasks.
Equal weight. Equal capability. Different audiences, same engine.
For data scientists, ML engineers, and technical operators. Connect your data, define the target, and hand off the rest. Genematon doesn't just run grid searches—it writes custom, end-to-end ML code from scratch. It autonomously codes the multi-table data flattening, engineers bespoke features, and selects or creates the best-performing models for your specific use case. It then trains and evaluates the model. Once the solution is created, you can deploy it on demand with built-in monitoring and drift detection. You get a production-grade pipeline without managing infrastructure or MLOps tooling.
See the platformCoding agents don't just call Genematon—they build with it. Dynamically provision classification and regression models on the fly via MCP or REST API. Genematon abstracts away the complex ML infrastructure and delivers expert-level predictive accuracy, freeing your agents to focus on what matters: writing integration code and building advanced solutions—like turning a simple binary classifier into a full-scale recommendation system.
Read the API & MCP docsDescribe the task you want to solve. Genematon builds the schema and selects the features. You review and hit train. It's that simple.
Upload your data and describe what you want to predict in plain English. Genematon understands the context and intent.
Genematon automatically suggests a parameter schema based on your data and goal. Accept it or make adjustments.
Genematon automatically identifies your target columns and any features unavailable at inference time. You have full control to adjust these before training begins.
Choose your input file, name the job and describe what you want to create
We'll analyze your file and create a parameter schema
Choose which features will be unavailable at inference time
Genematon's pass@1 debug success rate went from 30% to 80% when we turned the platform on itself. The same engine that designs your pipelines is the engine we use to improve Genematon — recursive self-improvement, in production.
Genematon autonomously built a supply chain forecasting pipeline that won a Kaggle competition outright, beating handcrafted solutions from human experts.
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.
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.
Versatile modeling for complex tabular data. Multi-output regression, classification, survival analysis, forecasting, and text understanding.