Anomaly detection from first principles. No training. No tuning. Orders-of-magnitude faster. Deployable on workloads where the established methods aren’t practical.
Every other method requires labeled examples, assumes a statistical distribution, or needs a training window on your data. The engine does none of these. It analyses the geometric structure of your signal directly. A fresh signal in a domain you’ve never seen can be analyzed in milliseconds with no calibration.
The detection method was derived from a fifteen-year investigation into how physical reality organizes itself — not optimized against benchmarks. The math comes from the theory. The benchmarks confirm it works.
586,000 samples per second on a single CPU core. 2,000× faster than HTM. No GPU required. No cloud dependency. Monitoring 10,000 metrics with Isolation Forest costs ~$2,500/month in compute. With Discreta, the same workload runs on a single core for under $200.
Choose a sample signal or upload your own CSV with an API key. No data is stored, no training occurs, no calibration is required.
NAB (Numenta Anomaly Benchmark) is the primary standard for evaluating anomaly detectors. Getting on the leaderboard at all places Discreta among the most capable detection systems ever built. Every other detector on this list required training on the data before producing results. Discreta ran cold — no training, no tuning.
NAB scores use Numenta’s native scoring methodology, which rewards early detection and penalizes false positives. On the throughput axis, Discreta occupies a region no other entry is near.
The engine is entering production this year. API access is opening to a select group of companies in infrastructure monitoring, industrial operations, and fintech observability.