Unplanned downtime is a solved problem. Most plants just don't have the data yet.
Daniel Osei spent years as a reliability engineer in manufacturing and process plants, watching centrifugal pumps, rotary compressors, and motors fail. The breakdowns that cost $50K+ per hour weren't surprises to the machines — the bearing defect frequencies were there in the data weeks earlier. They were surprises to the people operating without a condition monitoring program. Fleetpio was founded in Denver in 2023 because the tools that existed were either too expensive for a 50-200 asset plant (legacy vibration analysis firms with per-analysis billing) or too generic to be useful without a data science team to interpret the output.
We are not a generic IoT platform. We don't monitor water meters or HVAC dampers. Fleetpio is built for rotating equipment — pumps, compressors, motors, fans, turbines — because each of those equipment classes has distinct fault signatures that require specific frequency band analysis. The health score is the distillation of that analysis into a number a maintenance director can act on without learning to read a vibration spectrum.
From plant floor to product.
Spent years as a reliability engineer in manufacturing and process plants — running condition monitoring programs, interpreting vibration spectra, and writing failure reports that said the same thing every time: we had enough warning in the data, we just weren't looking. Founded Fleetpio in Denver in 2023 to put that failure prediction capability into the hands of maintenance directors who don't have a dedicated vibration analyst on staff.
Background in industrial vibration analysis at a rotating equipment OEM, where she developed fault signature libraries for centrifugal pumps and multi-stage compressors. Leads the scoring engine development and per-equipment-type calibration methodology at Fleetpio.
Spent five years building CMMS integrations and maintenance workflow automation at a maintenance software company. Leads the CMMS integration layer at Fleetpio — the part of the product most customers say they were most worried about before they saw it work.
Three principles that shaped the product.
We built for rotating equipment — pumps, compressors, motors, fans, turbines. Not all industrial IoT. Not static equipment, not electrical switchgear, not utilities monitoring. The fault signatures we detect, the frequency bands we monitor, the P-F windows we estimate: all derived from rotating equipment failure physics. BPFO and BPFI calculations require knowing ball pass frequencies. Cavitation detection requires broadband high-frequency monitoring. These aren't generic anomaly detection algorithms — they were designed around the physics of how rotating machinery actually degrades. That specificity is why the scores are actionable to a reliability engineer who's spent their career reading P-F curves.
Every recommendation Fleetpio makes links to the sensor reading that triggered it. When the dashboard says "bearing outer race wear — 87% confidence," the vibration spectrum showing the BPFO elevation is one click away. Maintenance directors shouldn't have to trust a black box. They should be able to look at the data and decide if they agree. We built the product to support that conversation, not replace it.
We have no interest in replacing your maintenance workflow. Your technicians are trained on SAP, Maximo, or Fiix. Your KPIs are reported from those systems. Fleetpio feeds those systems — it doesn't compete with them. The work order lands in the place your team already looks. No new app to check, no separate inbox to maintain. The goal is to make your existing workflow smarter, not to become the workflow.