Service · 22

Computer Vision & Multimodal AI

Apps that see — document scanning, object recognition and multimodal pipelines, cloud or on-device.

How it's built

(1) Sample data — real-world image set with edge cases listed. (2) Model select — cloud (Gemini) vs on-device (ML Kit) against accuracy targets. (3) Pipeline build — capture → infer → act with fallback paths. (4) Eval — precision/recall on the set, field-condition tests. (5) Ship — on-device optimization, telemetry, retraining loop.

Core fundamentals

tested on real-world messy inputs, not stock photos
accuracy targets agreed up front
on-device option for privacy/offline
telemetry to catch drift

Build blueprint

Deliverables

vision pipeline
accuracy report
integrated feature
telemetry

Stack

GeminiML KitVisionOn-device