Introducing JITM.ai: The prediction layer for your AI agent
We're launching JITM.ai: an AutoML platform that turns any CSV into a live prediction endpoint in seconds. Built for humans and AI agents alike.
Today we're opening the doors on JITM.ai, a platform we built to answer one question: what if any AI agent could build its own predictive models?
The problem
Machine learning infrastructure is still too hard. Even with modern tooling, getting from raw data to a production prediction endpoint takes days or weeks: data cleaning, feature engineering, model selection, hyperparameter tuning, deployment, monitoring. Most teams spend 80% of their time on plumbing and 20% on the actual prediction problem.
Our answer
JITM.ai collapses the entire pipeline into a single flow: upload a dataset, pick a target column, and get a live REST endpoint backed by a trained model. The platform handles type inference, missing data, feature engineering, training, validation, and deployment. All in under 10 seconds.
Built for agents
Every capability is accessible via MCP (Model Context Protocol), so AI agents can discover, upload, train, predict, and analyse without any human in the loop. Your agent connects once and gets 13 tools covering the full ML lifecycle.
Built for humans too
Drop a CSV on the homepage and watch. The analysis panel shows your data quality, column types, and target candidates. Pick one, click train, and your model is live. Feature importance charts show exactly what's driving predictions. No statistics degree required.
What's included at launch
Tabular data (CSV, JSON, Parquet), XGBoost-based models with automated feature engineering, two-phase training with ensemble support, SHAP-based feature importance, and sub-25ms inference. On the roadmap: time-series support, per-prediction SHAP explanations, batch inference, and webhook notifications. We're building in public. Follow along.