Deploy edge-native predictive maintenance on rotating equipment without cloud connectivity or retraining. RailMind self-organizes under bio-inspired competitive pressure to detect emerging faults in real time — validated on CWRU bearing datasets with AUC 0.985.

• Real-time fault detection at microsecond speeds
• No labeled training data required
• 40.4KB model footprint — runs on MCU-class hardware
• Cross-validated on industrial bearing datasets

A single engine architecture validated across 10 benchmark datasets spanning 6 signal domains — vibration, electrochemistry, video, satellite, motion, and audio. No retraining, no domain-specific tuning.

• 10 benchmark datasets validated without architecture changes
• 6 signal domains: vibration, electrochemistry, video, satellite, motion, audio
• No feature engineering or domain expertise needed
• Continuous adaptation to changing conditions

Provide large language models with a persistent regime-aware state substrate. RailMind continuously tracks operating context and injects structured state summaries into the LLM, producing significant measured improvements in response relevance and specificity.

• Regime-aware context injection into LLM reasoning
• Microsecond state retrieval and update
• Significant measured improvement in LLM relevance and specificity
• Integration with major LLM frameworks

Real-time quality-of-experience monitoring deployed in streaming video decoding pipelines — detecting encoding anomalies, bitrate degradation, and quality shifts directly on edge devices. The same self-organizing engine validated across industrial fault detection applies without architecture changes.

• Validated in streaming video decoding pipeline — no cloud required
• Validated AUC 0.959 across video content types
• Detects encoding anomalies and bitrate degradation in real time
• Same engine, no architecture changes from industrial to video

Anomaly detection on satellite telemetry streams — exceeding published benchmarks on the ESA-ADB SMAP dataset. Zero dynamic memory allocation ensures satellite-grade reliability with deterministic, O(1) per-sample execution.

• ESA-ADB benchmark: exceeds published reference
• 37x unsupervised advantage over raw baseline
• Zero dynamic memory allocation
• No architecture changes from ground to space

Continuous structural integrity monitoring for bridges and civil infrastructure — validated on the Z24 Bridge dataset. The same engine detecting bearing faults in factories monitors structural anomalies in large-scale infrastructure without any architecture changes.

• Validated on Z24 Bridge real-world structural dataset
• Continuous on-device monitoring — no cloud dependency
• No architecture changes from industrial PdM to civil SHM
• Detects structural regime shifts and anomalous load patterns

Multi-joint predictive maintenance and anomaly detection for industrial and collaborative robots — validated on CASPER UR3e 6-axis robot data (1.76 million sensor rows). Handles high-dimensional, correlated multi-axis signals natively.

• Validated: CASPER UR3e 6-axis collaborative robot (AUC 0.948)
• 1.76 million rows of real joint-sensor data processed
• Multi-axis correlated signal processing without feature engineering
• Deployable on robot controller hardware — no cloud required

Regime-aware quality monitoring across the full video processing pipeline — from encoding and transcoding to streaming delivery. Detects encoding anomalies, bitrate shifts, and delivery degradation in real time on edge hardware. Validated on diverse streaming content at AUC 0.959.

• End-to-end coverage: encoding → transcoding → delivery
• Validated AUC 0.959 on diverse streaming content
• Real-time regime detection with microsecond latency
• No retraining across different codec profiles or content types

Continuous behavioral regime detection from wearable and embedded motion sensors — validated across UCI HAR (6 activities) and WISDM v2 (18 activities, 160K samples). Operates entirely on-device with zero labeled training data.

• Validated: UCI HAR K=6 and WISDM v2 K=18 (160K real samples)
• Up to 18 concurrent activity regimes detected simultaneously
• Zero labeled training data required
• Runs on wearable and embedded sensor hardware

Acoustic regime detection for environmental monitoring, industrial audio analysis, and acoustic scene classification — validated on ESC-50 and DCASE benchmark datasets. The engine self-organizes to distinguish acoustic environments without sound-specific feature engineering.

• Validated on ESC-50 and DCASE benchmark datasets
• No audio-specific feature engineering required
• Same engine architecture as industrial and video applications
• Deployable on low-power edge microphones and IoT devices