Edge
Embedded directly in device firmware. No network required.
nextX CRONOS · Temporal Analytics
Temporal pattern recognition.
Zero training. 100% deterministic.
Detect anomalies, predict trends, correlate signals across any time-series domain — without a single training run. 13 industrial domains validated to ≥86% accuracy.
122 ns drift analysis1M+ analyses/secNo GPU requiredARM Cortex-M4 minimum256 MB RAM minimum
Philosophy
“Traditional AI looks at data and guesses. CRONOS measures what the data actually does.”
Performance
Edge-class hardware. Hyperscale throughput.
Drift analysis
122
nanoseconds per drift
Throughput
1M+
analyses per second
Full pipeline
16
milliseconds end-to-end
| Metric | Value |
|---|---|
| Drift analysis latency | 122 ns |
| Full pipeline | 16 ms |
| Throughput | 1,000,000+ analyses/sec |
| Edge latency | < 1 ms |
| Minimum hardware | ARM Cortex-M4 · 256 MB RAM |
| GPU required | No |
Comparison
Traditional AI vs CRONOS.
| Aspect | Traditional AI / LLM | CRONOS |
|---|---|---|
| Training required | Yes (often massive) | No |
| Determinism | Probabilistic | 100% deterministic |
| Latency | Variable · hundreds of ms | 122 ns per drift |
| Hardware | GPU typical | CPU only · edge-class OK |
| Explainability | Limited | Full audit trail |
| Adaptation | Re-training | Zero-shot to new domains |
| Cost per inference | High | ~$0.000001 / analysis |
13 validated domains
Validated across thirteen industries.
Public datasets, pre-registered thresholds, reproducible numbers. Full methodology in the Industry Handbook.
| Industry | Dataset | Accuracy |
|---|---|---|
| Industrial IoT — Bearing Fault Detection | CWRU | 95.9% F1 |
| Medical — EEG Seizure Detection | CHB-MIT | 100% |
| Medical — ECG Arrhythmia Detection | MIT-BIH | 97.4% F1 |
| Geophysics — P-Wave Detection | CREW | 100% |
| Geophysics — Tsunami Early Warning | DART (Tohoku) | 5-minute warning |
| Energy — NILM Timing | REDD | 99.9% |
| Finance — News-Market Correlation | SVB Case | Validated |
| Space — Exoplanet Transit | Kepler | 99.3% |
| Cybersecurity — Network Intrusion | NSL-KDD | 86.0% F1 |
| Environmental — Air Quality Prediction | Beijing PM2.5 | 89.9% F1 |
| Wearable — Activity Recognition | UCI HAR | 95.4% |
| Robotics — Arm Anomaly | voraus-ad | Validated |
| Wearable — Fall Detection | Digit_Fall | Validated |
Industry Handbook
Thirteen chapters. LaTeX source. Open download.
IoT, Medical, Energy, Geophysics, Finance, Environment, Space, Cybersecurity, plus five additional industrial domains. Each chapter ships methodology, datasets, validation results, and a reproducibility recipe.
Full Industry Handbook (13 chapters · LaTeX-built) ships v1.1. Sector-specific verticals available on request via the demo flow.
Deployment
Deploy where the data lives.
On-Premise
Air-gapped server deployment. Customer-controlled.
Private Cloud
VPC deployment. Single-tenant.
SaaS
Managed CRONOS endpoint (planned).