Anomaly detection on live streams
Unsupervised detection on high-volume, real-time telemetry: no labels, adversarial conditions, decisions at stream speed. Built on state-of-the-art deep learning for multivariate time series.
AI monitoring & supervision systems
Anomalies don’t come labeled. Our detectors don’t need them.
DeepFi builds unsupervised anomaly detection for high-volume, real-time telemetry, bringing the state of the art of AI research into production for institutional clients.
What we build
Reliable AI in production, end to end
Applied LLM & agent tooling
An LLM gateway across multiple providers, retrieval-based assistants, and agent workflows, built on the Claude API and open models, engineered to hold up under production traffic.
Research to production, owned
Modeling, evaluation, deployment, monitoring: one owner across the full path, so what ships behaves like what was measured.
Research
The state of the art, out of the lab
DeepFi is grounded in peer-reviewed research on unsupervised anomaly detection: deep learning models that flag abnormal behavior on multivariate time series, with no labeled failures required. We track the state of the art as it moves, and we do the unglamorous work that turns it into systems clients can rely on: rigorous evaluation, deployment, and monitoring on real telemetry.
Where it runs
Decentralized finance, as a proving ground
On-chain markets are open, adversarial, high-volume, and unlabeled by nature: the hardest honest test for a detector. The discipline is reliable AI in production; the telemetry happens to be financial.
- on-chain flows
- wallet behavior
- liquidity pools