Your competitors are adding AI features.
Your customers care about outcomes.

The client had four years of historically profitable, manually labelled tick data but no way to automate the signal.

They needed a low-latency machine learning model capable of ingesting live data, analyzing statistical anomalies against historical bounds, and firing webhooks — without hallucinations or false positives.

We bypassed bloated LLMs and started with a clean, fine-tuned architecture suited for time-series anomaly detection.

The first sprint dedicated solely to data cleaning and pipeline creation. By week two, we had a baseline model running in a shadow environment, capturing accuracy progression across 12 distinct iteration cycles.

The chosen architecture surpassed the 90% viability threshold on week three and hit 95.2% validation accuracy before production deployment. The API endpoint resolves in under 120ms, actively triggering profitable trades daily.