Computer Vision Development Company

Can Computer Vision solutions feel genuinely native on iOS and Android?

For Computer Vision, we guarantee 96% mAP minimum for detection models and 98%+ for classification tasks 60–120 FPS and supports platform-specific UI conventions — Material Design on Android, Cupertino patterns on iOS — while sharing 96%+ of the codebase. In every Miracuves-deployed product, users cannot distinguish from a native-built app.

Do I need to provide a labelled dataset, or does Miracuves handle annotation?

Miracuves handles full annotation services for every project. We use a tiered annotation pipeline: automated pre-labelling with our existing models, manual verification by trained annotators, and quality sampling by CV engineers. For standard use cases, we also have pre-labelled datasets available. You provide raw images or video; we deliver production-ready annotations.

How fast can a Computer Vision solution realistically be delivered?

Yes — edge deployment is our standard architecture. All models are compiled to TensorRT engines optimised for NVIDIA Jetson, Raspberry Pi with Coral TPU, or Intel Neural Compute Stick. Inference runs entirely on-device with sub-10ms latency. Cloud connectivity is used only for model updates, dashboard syncing, and remote monitoring — never required for real-time inference.

Computer Vision vs AutoML Vision APIs — when should I choose custom CV development?

AutoML APIs work for generic use cases with standard object classes and non-critical accuracy requirements. Choose custom Computer Vision development when you need: industry-specific defect categories not in any pre-built API, sub-10ms edge inference, full control over model architecture and training data, or detection accuracy above 95%. Miracuves provides a free scoping call to determine which path fits your use case.

How does Miracuves handle model retraining when production data reveals new edge cases?

Every production deployment includes an Active Learning pipeline. When the monitoring dashboard detects accuracy drift below threshold, or when operators flag false positives/negatives, the system automatically saves these edge-case images. Our CV team reviews, annotates, and retrains the model — typically within 48 hours. The updated model passes through the full QA gate before deployment.

What hardware does Miracuves recommend for running CV models in production?

For edge deployments, we recommend NVIDIA Jetson Orin (10–40 TOPS) for industrial vision and Raspberry Pi 5 with Coral TPU for lightweight applications. For cloud inference, we deploy on GPU instances (NVIDIA A10G or equivalent) with auto-scaling. Miracuves provides hardware recommendations specific to your throughput, latency, and power requirements — no over-provisioned infrastructure.

Does Miracuves handle data privacy and compliance for regulated industries?

Yes. All data processing is designed for compliance from day one. For healthcare, we implement HIPAA-compliant data handling with PHI de-identification at the camera level. For manufacturing, all inference stays on-premise — no image data leaves the factory floor. For security applications, we support GDPR-compliant retention policies and facial blurring for non-consented individuals.

What is the typical timeline and process for a Computer Vision project?

A standard Computer Vision engagement follows five phases over 6–12 weeks: (1) Data audit and annotation — 1–2 weeks, (2) Model training and validation — 2–3 weeks, (3) TensorRT compilation and edge testing — 1–2 weeks, (4) Integration with your systems — 1–2 weeks, (5) Deployment and monitoring setup — 1 week. Custom models with novel architectures may require additional research and training time.