Before machines fail, they speak. We taught AI to listen.
The first Large Acoustic Language Model for heavy industry. Traditional heat and vibration sensors detect failure late — the acoustic signature of an asset broadcasts microscopic mechanical shifts long before the spike. SufiSonic Pulse™ decodes it, continuously.
The structural integrity of an asset is broadcast continuously through its acoustic signature. Physical sensors catch the spike — acoustics catch the whisper weeks earlier.
Hover over the machines — every asset broadcasts its acoustic signature. Mobile rounds and edge sensors capture it, the Acoustic AI Core trains on it, and work orders fire before failure does.
Bearing wear, cavitation, internal leaks, and misalignment — detected in the acoustic band before vibration shows it.
Structural weaknesses and fatigue located before failure propagates.
Loose or damaged internal components; abnormal burner and combustion noise.
Fouling and scaling identified through abnormal flow noise and mechanical vibration signatures.
Gear tooth cracks, lubrication issues, and structural looseness caught at the micro-shift stage.
Rotor bar breakages, stator issues, and winding faults identified from the electromagnetic-acoustic signature.
Deep learning built exclusively for machine audio — validated with F1-score and AUC-ROC at every release.
App and edge devices capture audio. Bandpass filters and spectral subtraction strip ambient noise; synthetic noise variations augment data for robustness.
Raw audio converts to time-frequency spectrograms and MFCCs. Statistical features — RMS, zero-crossing rate — extracted per window.
Hybrid CNN-LSTM classifies fault-specific sounds: CNNs capture spatial audio features, LSTMs track degradation over time. Unsupervised autoencoders detect unknown unknowns in low-data scenarios. TabPFN in R&D.
Lightweight models run on edge devices for real-time inference; cloud AI handles heavy retraining. Brand-specific filters fine-tune the Foundation Acoustic Model to your exact equipment's mechanical dialect.
REST APIs trigger automatic CMMS work orders (SAP, Maximo); AI alerts notify technicians. Maintenance feedback flows back, cutting false positives over time.
The Foundation Acoustic Model ships with brand-specific filters for the equipment that runs heavy industry.
Select sites and critical assets. Install edge devices. Test baseline data collection and sign off on scope.
Core spectrogram analysis and anomaly detection live. Early anomaly recommendations and system diagnostics on baseline data.
Foundation Acoustic Model deployed, fine-tuned to your specific brands. AI-driven actions and predictive alerts activate.
MVP validated. Production architecture scaled across sites; capabilities tailored to each location's asset mix.
Begin with a site assessment — we map your critical assets, plan edge placement, and define the baseline capture campaign.
Start a conversation