Local & mobile AI execution
Local AI runs directly on your device — your phone, your laptop, even specialized hardware at your job sites — without sending data to the cloud. This matters for two reasons: privacy (sensitive customer data never leaves your building) and speed (no internet connection needed). For field service businesses, this means a technician's tablet could run AI diagnostics on-site even in a basement with no cell signal.
Go deeper
Your field technicians arrive at a hospital mechanical room three floors underground — no cell signal, no Wi-Fi. They pull up the equipment history on their tablet, and the AI diagnostic tool analyzes vibration readings, cross-references the maintenance log, and flags a bearing that's trending toward failure. All of it runs locally. No cloud round-trip, no data leaving the device, no HIPAA exposure from sending equipment data through a third-party server.
The trap most companies fall into is assuming local AI means inferior AI. Two years ago, that was true. Today, models optimized for on-device execution handle most field diagnostic and classification tasks at near-cloud quality. The real question isn't 'is it good enough?' — it's 'which tasks genuinely need cloud-scale AI and which work fine locally?' Most routine field work falls in the second category.
Questions to ask
- Which of our field workflows currently fail or degrade when connectivity drops?
- What data are we sending to cloud AI that could stay on-device for privacy or compliance reasons?
- Do our device hardware specs support local model execution, or do we need a refresh cycle?