Pulse ingests millions of MQTT signals per second and renders them into decisions before your on-call engineer opens a ticket. Built for operations teams who can't afford a silent outage.
Can it handle your scale?
Pulse runs a distributed ingestion mesh that absorbs bursts from 1,000 to 1 million devices without queue buildup. No dropped packets. No backpressure excuses.
Can it catch problems before they cascade?
Statistical baselines, ML-assisted pattern recognition, and rule-based triggers working in parallel. If a sensor deviates, Pulse flags it in the same 11ms window.
Statistical Baseline
Z-score and IQR models trained per-device. Adapts to seasonal patterns automatically.
ML Pattern Engine
LSTM models detect multi-variate anomalies that rule-based systems miss entirely.
Rule Triggers
Write threshold rules in plain YAML. Deploy in seconds. No ML degree required.
Does it fit your stack?
Pulse connects to your existing infrastructure in under an hour. Native connectors for the protocols your devices already speak. No rip-and-replace required.
Teams that couldn't afford to be wrong.
"We were drowning in MQTT noise from 12,000 sensors across three production lines. Pulse went from zero to full ingestion in 4 hours. The anomaly feed caught a bearing failure on Line 7 before our floor supervisor even got the alert."
"Ten thousand refrigerated trailers across the US, Canada, and Mexico. Before Pulse, a silent compressor failure in Laredo would cost us $40K in spoilage before anyone noticed. Now we get a 22-minute warning window."
"After a silent outage took down our grid monitoring cluster last Q3, I needed something that could survive a partial infrastructure failure and still surface critical signals. Pulse's distributed ingestion mesh has been rock solid since day one."