For sensor-network operators, predictive maintenance vendors, and asset-telemetry platforms. Score every site against actual cost-to-deploy and predicted savings, route field crews, and price recurring against runtime data — not a slide deck.
Industrial IoT is sold on case-study ROI, deployed on field reality. The cost of laying sensors, network, and integration in a real plant rarely matches the pilot — and the savings model rarely makes it past the first quarter of live data.
Plant floor layout, network coverage, and PLC compatibility set true cost. Allometry scores deployment cost against site survey data, BIM, and prior install records.
Savings modeled against ideal runtime collapse against real fault patterns. Allometry runs continuous reconciliation between projected and observed savings — by asset, by site.
Sensor maintenance, gateway replacement, and analytics-tier upgrades hit margin. Allometry surfaces accounts where recurring is unprofitable — with pricing recommendations.
An industrial IoT rollout is 60 sites with 60 different IRRs — and most operators can only see the average. Allometry scores each site against sensor coverage, data uptime, predictive-shift ratio, and 24-month ROI — so the rollout sequence matches where the margin actually is.
The pilot landed because pilot economics are forgiving. The rollout fails because rollout economics are not. Sensor BOM costs drift, integration days stretch, predictive-savings models hit reality.
Allometry pulls live sensor pricing (Mouser, Digi-Key), integration runtimes, predictive-vs-reactive maintenance ratios, and 24-month asset uptime curves — into every site's deployment IRR. The 6 levers on the right are what the rollout-vs-pause decision actually hinges on.
Predictive-maintenance vendor selling into discrete manufacturing. Allometry runs cost-to-deploy and savings reconciliation across 4,200 deployed sensors in 18 plants, with quarterly customer reviews driven by the model.
Connect to your CRM, sensor inventory, and PI / Ignition / AWS IoT data feed. We'll score a sample of accounts, surface the savings drift, and walk you through the model — with your data.