Spot outliers the moment they happen.

Encode normal operating conditions as a dense vector baseline. Any new data point that falls far from its nearest neighbors in the vector space is flagged as an anomaly, no labeled anomaly data required to get started.

Sub-5ms query latency means anomaly scoring happens inline in the data pipeline. Flag suspicious transactions, sensor readings, or log entries before the next step in the workflow executes.

Encode time-series windows as vectors using transformation methods (sliding window, Fourier, wavelet). Detect drift, spikes, and regime changes by comparing current windows against the historical baseline.

Encode transaction behavior as vectors. New transactions that deviate significantly from a customer's behavioral profile or from known legitimate transaction patterns trigger a fraud alert inline.

Encode product images and compare against a reference set of good units. Detect surface defects, assembly errors, and foreign objects on the production line without labeled defect training data.

Embed system logs and event sequences as vectors. Surface anomalous patterns in service logs, network traffic, and security events that signature-based SIEM systems don't catch.