Real-time REST endpoint
POST a row, get a prediction. Same JSON in, same JSON out — your apps, agents, or services consume the deployed model from any language. Auto-scaling and failover are handled.
Genematon is exposed as both an MCP server and a REST API. Hand off the modeling problem; we own the full pipeline — from data cleaning and feature engineering to model creation and hyperparameter tuning. Once the solution is created, it can be deployed behind a real-time REST endpoint or a batch file processor, so your apps and agents can consume it however they want. Your code handles reasoning and orchestration; Genematon handles the ML.
Same operation, same response shape — pick whichever fits your stack. No SDK to install in your agent runtime; no model training inside your reasoning loop. One request, full pipeline.
Every model Genematon ships is live behind a REST endpoint and ready for batch file processing out of the box. Same model, same predictions — pick whichever fits the workload, or use both.
POST a row, get a prediction. Same JSON in, same JSON out — your apps, agents, or services consume the deployed model from any language. Auto-scaling and failover are handled.
Upload a CSV or Parquet file. Genematon processes the data and outputs a results file matching the schema you agreed upon. No code path required — works for analyst workflows and scheduled runs.
Every deployed ML API is secured with mandatory API keys scoped per endpoint to control exactly who can invoke your models.
Genematon creates and owns the entire data-to-deployment loop. We're explicit about what's in scope and what isn't.
Training, feature engineering, and deployment is the wrong work to put inside an agent's reasoning loop or your service's request handler. It's long-running, compute-heavy, and stateful — none of which agent runtimes or app servers handle well. Exposing Genematon as a service lets your code issue a single, well-defined request and continue working while the full pipeline runs out-of-band.
We support both MCP (for agentic systems that prefer the tool-call pattern) and a REST API (for any service, in any language). Same operations, same response shapes — pick the one that fits your stack.
It also draws a clean ownership line. Your code owns reasoning, planning, and orchestration. Genematon owns data cleaning, custom feature flattening and engineering, model selection, hyperparameter tuning, training, deployment, monitoring, and retraining. Neither side accidentally bleeds into the other.
Genematon’s reasoning engine runs entirely on open-source LLMs to write custom code, select models, perform hyperparameter tuning, 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.