Latency, reliability, and economics. Cloud vision calls take at least 1 second end-to-end from a real phone on LTE — Captur takes 30ms. Cloud APIs need a signal your users don't always have. And you'll never run into LLM capacity constraints or unsustainable token costs.

LLMs are fine upstream — for content moderation or rich multimodal reasoning. They're the wrong primitive for real-time mobile validation: 1–5 seconds per call, no offline, non-deterministic outputs, per-call cost that scales with your business. An LLM can describe a photo. Captur decides whether it's the right photo.

Core ML is iOS-only; ML Kit is Android-focused. Captur is cross-platform from one policy — Swift, Kotlin, React Native, Flutter — and ships model updates over the air rather than through an app-store release. You define policy in business terms, not against raw model output.

Every false positive and false negative is captured and triggers an automated improvement cycle. The model improves with your edge cases. Updates ship over the air. Policy versions are auditable. You can override the model from your app at any time.

On-device means most data never leaves the user's phone. SOC 2 Type 2 and GDPR compliant. PII can be detected and rejected before any image leaves the device.

Committed consumption-based, with a paid pilot for new customers. We size against your expected volume and use case. Talk to us →