AI-assisted decision support for the final moments before delivery
Delivery Intelligence Platform
Explored how multimodal AI can help drivers navigate the most failure-prone stage of delivery: the final 100 meters before reaching the customer.
Problem
Maps are optimized for navigation to an address, not necessarily to the exact customer handoff location. Drivers still face incomplete addresses, large complexes, ambiguous entrances, informal references, and missing wayfinding context — leading to delays and failed deliveries.
Solution
Designed a multimodal intelligence layer aggregating GPS telemetry, scan events, delivery history, customer media, building information, route traces, computer vision outputs, and LLM summarization. The system converts fragmented operational signals into concise driver guidance.
Outcome
Demonstrated how operational AI can bridge the gap between map navigation and real-world delivery execution, and established a reusable framework for multimodal AI in high-friction workflows.
Architecture
A placeholder implementation path that can be expanded with screenshots, data contracts, system diagrams, and measurable results as the project matures.
01
GPS telemetry ingestion
02
Route history analysis
03
Building intelligence layer
04
Media and image processing
05
Computer vision extraction
06
Signal aggregation
07
LLM reasoning
08
Driver guidance generation
09
Human feedback loop
Product Artifacts
Sanitized examples to demonstrate product thinking and execution style when proprietary materials cannot be shared.
- PRD outline (problem framing, success metrics, rollout plan)
- Workflow wireframe / journey snapshot
- Evaluation rubric or quality checklist
- Operational metrics dashboard mock
Metrics to Track
- First-attempt success
- Driver contact rate
- Address confidence
- Instruction usefulness
Product Role
- Framed system strategy
- Mapped data sources to features
- Designed output loops for ops teams