A bilingual prototype for garbage, recycling, compost, bulky items, ecocentre guidance, collection alerts, missed pickup reports, and AI-assisted sorting.
Collection information is often spread across PDFs, calendar pages, call-centre scripts, contractor updates, and resident memory. A dedicated app can turn those fragments into one reliable resident experience while giving staff clearer signals about recurring issues.
The concept combines address-based scheduling, next collection cards, a missed-pickup report flow, ecocentre guidance, and a guarded AI assistant that answers sorting questions using approved municipal rules.
Fewer calls about collection dates, holidays, and item sorting
Cleaner reporting loop for missed pickups and route exceptions
Higher use of ecocentre guidance for hazardous or special materials
Better visibility into recurring street, sector, or contractor issues
The page shows the kind of planning material Auvant would produce before and during delivery.
A simple path from address lookup to collection reminder, sorting question, and missed-pickup report.
Core records needed to connect addresses, sectors, materials, exceptions, and contractor confirmations.
Examples of bilingual alerts for schedule changes, delayed routes, ecocentre redirects, and special handling.
Map current schedules, contractor handoffs, ecocentre rules, and resident call drivers.
Build a working address lookup, reminder flow, missed-pickup form, and AI sorting proof of concept.
Launch with one or two collection sectors and staff review before expanding city-wide.
Add analytics, documentation, support procedures, and long-term ownership handoff.
For a collection app, the first deliverable should help leadership, operations, communications, and IT decide without committing to a large project too early.
Resident screens and municipal dashboard with calendar, reminders, reporting, ecocentre guidance, and sorting assistant.
Full path between resident, municipal team, contractor, route exception, and return service.
Required fields, roles, retention to discuss, AI limits, and human validation points.
Limited scope, success criteria, risks, dependencies, and next implementation decisions.
We can adapt this concept to a real context, with scope, risks, data, and delivery steps made clear.