To find partners with specialized utility expertise, ask technical and focused questions. Use this list during your evaluation to connect with teams that align with your requirements:
1. How do you handle GPS signal loss or 'urban canyons' in your algorithms?
This reveals if they use Dead Reckoning or sensor fusion (accelerometers/gyroscopes) to maintain accuracy when satellites are blocked.
2. Can you explain your approach to 'Edge AI' versus Cloud processing for this project?
GPS apps often need instant reactions. Relying solely on the cloud can introduce dangerous latency; they should know when to process data locally.
3. What specific datasets do you use to train your routing models?
A model is only as good as its training data. You want to ensure they use diverse, high-quality, and legally compliant geospatial datasets.
4. How do you integrate with legacy fleet management hardware (OBD-II/ELD)?
You likely have existing hardware. They must demonstrate the ability to pull data from older systems, not just build for new devices.
5. What is your strategy for minimizing battery drain while running continuous background GPS tracking?
Artificial intelligence solutions with GPS are battery hogs. Capable developers use adaptive tracking intervals to save power.
6. How do you secure location data to comply with GDPR and CCPA?
Location data is sensitive PII (Personally Identifiable Information). Lax security can lead to massive legal fines.
7. Do you have experience with map rendering engines like Mapbox, Google Maps Platform, or OpenStreetMap?
This confirms they know the specific SDKs and APIs required for visualization, not just the backend logic.
8. Can your system scale to handle telemetry from 10,000+ simultaneous vehicles?
This tests their backend architecture knowledge. High-frequency GPS data requires robust databases (like time-series databases).
9. How do you approach 'Map Matching' to ensure the vehicle icon snaps to the correct road?
Raw GPS data is noisy. Good developers use Hidden Markov Models or similar methods to accurately "snap" raw points to the road network.
10. What is your post-deployment support model for model retraining?
AI models drift over time. You need to know if they offer services to retrain the AI as road networks and traffic patterns change.
