AI that listens — a stethoscope for machines
Small sensors listen 24/7 while our models detect sound anomalies early. Plan maintenance proactively, reduce unplanned downtime, and extend equipment life.
Problem: Unplanned downtime
Unexpected failures strike without warning and cost millions in lost production. Traditional calendar-based maintenance misses many early warnings hidden in sound.
Solution: Acoustic monitoring + AI
Discreet microphones listen 24/7. Our models learn the normal “sound signature” of each asset and flag anomalies in real time via SMS, dashboard, or API.
How it works
- Learn normal sound patterns per machine
- Detect deviations instantly on edge or in cloud
- Trigger alerts and integrate with CMMS via API
From reactive to proactive — real impact
Predictive, condition‑based maintenance reduces risk and raises OEE.
Built for the energy sector
Early pilots focus on high‑value assets where downtime is costly.
AI Toolkit
Unsupervised learning across multiple detectors: VAE (reconstruction error), CNNs on spectrograms, wavelet analysis, Isolation Forest, and sequence models (Transformers/LSTM).
Stack & Deployment
Python + PyTorch, streaming or batch. Easy install with non‑intrusive mics. Run on Raspberry Pi at the edge or in the cloud. Expose insights via dashboard and API.
Pilot in Stavanger & beyond
We’re ready for pilot deployments with energy operators who want to co‑shape the product and prove ROI on critical assets.
- Rapid installation on priority equipment
- Shared success metrics and reporting
- Preferential pricing for early partners
Get in touch
Tell us about your assets and maintenance goals. We’ll follow up with a short call and a pilot proposal.
Privacy & Data
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Company
SoundOps • Stavanger, Norway
API & dashboards available on request.