Data to Model Pipeline
Collect data through various sources, parse features, analyze correlations, and automatically train optimized Intelligence Models.
Sign in with Google to generate a personal MCP API key.
Pick website + listing type, then pagination limits.
Tune bounded parallelism per run. Start conservative to avoid throttling.
Upload a CSV file to use directly as your dataset, bypassing the scraper entirely.
Fetch raw headers, rename features, and drop nulls.
Choose the column your model should predict, for example price.
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Automatically analyze data types to recommend and plot correlation matrices & distributions.
Use this when you want a different chart angle than the default analysis.
Set parameters and trigger hyperparameter tuning.
Test the trained model with a dynamic form that follows the exact feature columns used during training.
Use the deployed MCP endpoint directly from your app or MCP client.
Train a model first to generate MCP config.
Train a model first to generate an MCP tool call example.
DuckDB answers over your active cleaned dataset.
Ask questions in natural language. The app generates DuckDB SQL, executes it, then answers from the compact result.