Fuses vibration, temperature, and throughput sensor data across every connected plant into an ML-based predictive maintenance agent — the same architecture that gave one manufacturer 18 hours of advance warning.
Pulse is in active design with early-access manufacturing partners. The savings calculator below scales a real deployment ratio from our own case study — a 3-plant IIoT rollout that predicted failures 18 hours in advance.
A single unplanned stoppage on a production line can cost more than a full quarter of routine maintenance spend.
Most plants still service equipment on a fixed schedule regardless of actual wear — replacing healthy parts early and missing early failure signs in between.
Vibration, temperature, and throughput sensors already exist on most modern equipment — the data is being collected but rarely turned into a prediction.
Each plant runs its own local monitoring, with no unified view for a corporate reliability or operations team.
Ingests vibration, temperature, throughput, and acoustic sensor data from every connected machine into a single IIoT data lake.
Learns the normal operating signature of each machine and flags deviations that historically precede failure — not generic threshold alarms.
Surfaces a predicted failure window — in our own deployment, an average of 18 hours of advance warning — giving maintenance teams time to act before a stoppage.
Converts a predicted failure into a scheduled maintenance work order automatically, routed to the right technician and parts inventory.
One dashboard across every connected plant, so a corporate reliability team sees equipment health enterprise-wide, not site by site.
Own uptime targets and need to move from reactive to predictive maintenance without a multi-year IIoT platform build.
Already have sensor data flowing but no consistent way to turn it into an actionable, ranked list of at-risk equipment.
Managing the bridge between operational technology (OT) on the plant floor and cloud-based analytics platforms.
Scaled directly from our real 3-plant deployment ratio.
Illustrative estimate scaling our real case study rate of $73,333/plant/year (from $220K saved across 3 plants). Actual savings depend on equipment type, failure modes, and existing sensor coverage — this is a planning estimate, not a guarantee.
Case study: a manufacturer operating 3 plants needed to reduce unplanned equipment downtime. We deployed an IIoT data lake with ML-based anomaly detection that predicted failures about 18 hours in advance, helping the team avoid roughly $220K in annual maintenance disruption costs.
Pulse's architecture is the same IIoT data lake and anomaly-detection pipeline we built for a real multi-plant manufacturer — not a new concept, a productized version of working infrastructure.
Fusing high-volume sensor streams into a queryable, ML-ready pipeline is exactly what our data engineering team builds for clients across industries.
Our AI engineering practice is explicit about shipping production-grade anomaly detection with monitoring and retraining built in, not a one-off model that degrades silently.
Become an early access partner and let's connect your first plant.