Verify Field Teams Are Actually Working — AI activity analytics for mobile crews

Field service managers, last-mile logistics operators and contractors at roughly 3.0M businesses increasingly struggle to verify that mobile crews are working during billed hours, which produces payroll leakage, invoice disputes and weak performance coaching. Many of these organizations already spend about $6K per year on workforce software and analytics yet still rely on GPS breadcrumbs, manual checklists or post-hoc audits that deliver low-confidence evidence. You could build a solution combining a lightweight mobile SDK for on-device activity inference (photos, GPS, motion), edge ML that emits confidence-scored activity events, and a cloud dashboard for audits, exception workflows and payroll/invoice reconciliation. Core features would include industry-specific classifiers, human-in-the-loop review, offline capture and turnkey integrations to payroll, field service management and ERP systems so customers realize value quickly. The market is attractive now because smartphone-first field workforces, advances in sparse-signal ML and intense cost pressure on labor converge to create demand; the addressable market is roughly $18.0B (3.0M businesses x $6K ACV) and the opportunity scores 92/100 for market and 90/100 for revenue potential in preliminary assessments. Early high-value verticals would be HVAC, pest control, plumbing, cable installers and last-mile delivery where episodic tasks and billing disputes are common. You can stand out by prioritizing privacy-first on-device inference, configurable confidence thresholds, verticalized models and seamless WFM/payroll integrations, but expect real challenges from labor and regulatory pushback, the need for labeled data and model generalization across 10–20 verticals, and the operational requirement to keep false positives below roughly 5% or risk creating more administrative overhead than savings.