Material Recovery Passport for Waste Haulers

1. Institution: Municipal solid-waste contractors, subscription waste haulers, and multi-city recycling operators. 2. What consumers pay for: Weekly trash, recycling, organics, and bulky-item collection, typically $25–$80+ per household per month. 3. Why they stay/pay for years: Waste collection is a recurring household utility; a retained household produces 50–500+ collection events over its relationship with the hauler. 4. Long-running physical work: Trucks collect bins, weigh loads, process contamination, transfer material, and send material to landfill, recycling, composting, or anaerobic-digestion facilities. 5. Unsurmountable internal problem: Operators know route-level outcomes but usually cannot connect a household’s repeated behavior, local rules, collection conditions, material-processing outcomes, and personalized interventions. The data is fragmented between trucks, route systems, call centers, municipal rules, processors, and resident communications. 6. B2B SaaS: A “Material Recovery Passport” that creates a longitudinal household-level model of disposal behavior. It predicts contamination and overflow risk, recommends targeted interventions, verifies whether interventions worked, and gives operators evidence for service-plan, bin-size, and education decisions. 7. Consumer gets: Fewer missed/overflowing bins, clearer personalized disposal guidance, fewer contamination fines, automatic reminders for seasonal items, and potentially rewards or bill credits for consistently recoverable material. 8. Business gets: Lower contamination penalties, less rejected recycling, fewer costly callbacks, better bin-size allocation, lower disposal costs, defensible municipal reporting, and higher customer satisfaction. 9. Why existing software does not solve it: Waste software is mostly route optimization, fleet management, billing, or municipal reporting. It rarely forms a persistent household-level evidence graph that measures whether a behavior-change intervention actually improved downstream material recovery. 10. Why it could become very large: Residential waste is an essential recurring service with millions of households, thousands of municipal contracts, and large economic pressure from landfill costs, recycling contamination, and regulatory diversion targets. The platform could become the intelligence layer shared by haulers, processors, municipalities, and consumer recycling programs. 11. Exact recurring data/events: Scheduled pickup; actual pickup timestamp; lift count; bin weight or axle-weight delta; missed-pickup event; overflow report; service ticket; route conditions; weather; bin size; contamination-tag event; processor rejection/load-quality result; resident message sent/opened; local disposal-rule changes; bulky-item pickup; compost participation; reward redemption; and intervention outcome. 12. Why it cannot be reduced to a camera/photo/inspection: A photo may identify one contaminated bin, but cannot establish whether the household repeatedly improved, whether the route’s material was ultimately accepted by a processor, whether bin capacity is wrong, or which intervention changes behavior over months and years. 13. Who pays: VP of Operations, Director of Recycling/Zero Waste, municipal-contract leader, or COO at a regional waste hauler. 14. Realistic annual SaaS price: $40,000–$150,000 per operator annually, plus roughly $0.05–$0.25 per active household per month for high-volume deployments. AI’s role: Predicting household risk and generating understandable, local-rule-specific guidance; the actual product is the longitudinal evidence system, operational workflow engine, and outcome measurement layer.