Geospatial Logistics & Fleet Orchestration Platform
Shoreline Waste AI Route Optimization
A full stack logistics & telematics platform that modernizes dispatching, proves service completion, and optimizes routes in real time without disrupting daily operations.
The Challenge
A regional waste management provider relied on static, legacy route sheets. Drivers frequently deviated from optimal paths, fuel costs were unmonitored, and missed pick up disputes were unresolvable due to lack of visual evidence.
The goal was to modernize fleet operations without disrupting daily proof of service requirements. “Close enough” routing doesn’t work at scale: exceptions are constant (blocked bins, on call adds), and without GPS verified proof of work, customer claims turn into refunds and operational churn.
Quick Stats
- Mapping: Fleet Engine + PostGIS
- Proof: Geofence verified photo logs
- Automation: Dispatch → Billing triggers
- Impact: Lower fuel waste and fewer disputes through proof of service automation
The Solution
We developed a full stack logistics & telematics platform that uses AI to solve route sequencing in real time, paired with a ruggedized mobile app for field execution.
Dispatchers get an AI route planner and live fleet visibility; drivers get a low friction workflow that captures proof of work at the right moment. Proof of service is built into the operational loop, and completion events can trigger billing automatically, keeping dispatch, finance, and customer support synchronized.
Technical Approach
- Heuristic routing: Google Maps Fleet Engine recalculates routes based on real time traffic, vehicle constraints, and service window SLAs.
- Geofence verification: Photo capture triggers only when GPS intersects the customer’s service polygon to produce defensible proof of work.
Technical Details
Architecture
React Native (Mobile) → Node.js (Microservices) → PostgreSQL + PostGIS
Integrations
Samsara/Geotab for engine diagnostics + Stripe for automated “pay per lift” billing.
Security
RBAC for dispatchers vs drivers; encrypted media storage for service photos.
AI Features
Predictive maintenance: analyzes telematics signals to detect likely failures before they happen mid route.
Engineering Deep Dive
Operational realities we designed for
- Missed stops, blocked bins, and “on call” pickups added mid route
- Connectivity gaps in the field (offline first driver workflows)
- Proof of service requirements for dispute resolution and refunds
- Dispatch changes that must reach drivers instantly and safely
Reliability patterns
- Event driven updates (route optimized → push to device)
- Idempotent “job completion” events to avoid duplicate invoices
- Queue backed processing for photos/uploads and billing triggers
- Role based access and audit trails for admin/dispatcher actions
Geospatial correctness
- PostGIS stop geometry + geofence thresholds to reduce false prompts
- GPS drift handling near dense neighborhoods and alleys
- Route re optimization with hard constraints (overtime cutoffs)
- Driver UX tuned for speed: minimal taps, safe prompts
Observability & rollout
- Route health dashboards (late stops, missed pickups, exceptions)
- Photo capture analytics + dispute resolution tracking
- Staged rollout by route/team with reversible feature flags
- Runbooks for driver support, device issues, and billing exceptions
Results & Impact
- Lower fuel waste: achieved through optimized stop sequencing, reduced idling, and better constraint aware routing.
- Automated dispatching: reduced manual route planning through reliable route generation and exception handling.
- Fewer disputes: disputes resolved faster via GPS verified photos and audit ready proof of service.
Ready to build something similar?
We’ll map the operations workflow, real time data flows, and reliability requirements, then ship in phases without disruption.
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