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: 22% fuel savings; 90% fewer claims
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
- 22% fuel savings: achieved through optimized stop sequencing and reduced idling.
- 100% automated dispatching: eliminated 4 hours of manual route planning daily.
- 90% reduction in claims: disputes resolved instantly via GPS-verified photos.
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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