Every day, markets generate terabytes of data. Most of it is noise. The challenge isn’t collecting data — it’s knowing what to ignore.
Our ML pipeline processes raw feeds from 40+ sources and distills them into structured signals. Each signal is scored, timestamped, and attributed to its source.
What Makes a Good Signal
A good signal is falsifiable, timely, and independent. If you can’t define when it’s wrong, it’s not a signal — it’s a narrative.
Architecture
We use a multi-stage pipeline: ingestion, normalization, feature extraction, scoring, and delivery. Each stage is independently scalable and observable.