recycling collection, run like a real operation.
Plutou is the AI-native operations platform for recycling collection operators — an agentic dispatcher that plans the week from fill prediction, adapts when reality shifts, and learns from every pickup.
Who this is for
Recycling collection operators managing residential, commercial, and drop-off schemes — often across multiple material streams and compliance frameworks.
Where operations break
Material streams change week to week.
Tonnage shifts. Contamination spikes. Processing contracts impose cut-offs that don't align with collection calendars. Static routes don't accommodate dynamic material flows.
Compliance and contract SLAs require accurate records.
Municipal contracts and processor agreements depend on documented service. Paper manifests and end-of-day recalls aren't the audit trail they require.
Plan. Adapt. Learn.
How Plutou works for recycling collection operators.
- P
Plan — The agentic dispatcher generates the week.
Routes are built to match actual material availability — not a fixed schedule that ignores how full the bins are or what the processor can take this week.
- A
Adapt — When reality breaks, the system replans.
Contamination incidents, vehicle issues, and site access problems are reflected in replanning in real time.
- L
Learn — Every pickup makes the system smarter.
Tonnage patterns, contamination history, and service time data improve future route builds. The operation gets more efficient without manual tuning.
Common questions
- Can Plutou plan routes across multiple recycling material streams?
- Yes. Different vehicle types, material categories, and processing destinations can be modelled per route and per location.
- How does Plutou support compliance reporting for municipal recycling contracts?
- Service records are captured in real time at each stop. The operational record reflects what actually ran — not what was planned — which is what auditors and contract managers typically need.
- Does Plutou work with IoT fill-level sensors on recycling bins?
- Yes. Fill prediction can be powered by sensor data, driver observation, or historical patterns — or a combination. The planning engine adapts to whatever signal quality is available.