Stop Building Crypto Data Pipelines. We Built the Fix.

Over the past few months we kept hearing the same story from teams building crypto predictive models and AI agents. It did not matter if they were ML engineers, quant researchers, or indie developers shipping crypto tools -- the first chapter of every project sounded identical. Connect to an exchange. Normalize OHLCV candles. Compute indicators. Handle gaps. Resample. Repeat for every token. Every single team. The problem we kept hearing The engineers we spoke to were not complaining about the modeling. They were complaining about the infrastructure that came before it. One team told us they spent three weeks just getting clean, consistent RSI and MACD data across six tokens before they could train a single model. Another said their model runs varied between experiments not because the model changed -- but because the data pipeline produced slightly different outputs each time. The insight that stuck with us: Building a crypto predictive model is a data problem before it is a modeling