Automate clapperboard detection

Manual tasks in post-production like clapperboard identification, metadata management, and asset categorization often slow down review processes, leading to inefficiencies and errors. qibb addresses these challenges with an automated 5G camera-to-cloud workflow, significantly accelerating your team's productivity.

By integrating powerful AI tools for optical character recognition (OCR), object detection, and content summarization, qibb enriches your metadata seamlessly and accurately. With automated asset categorization based on custom guidelines, your team can focus less on manual tasks and more on delivering outstanding creative content.

The qibb platform continuously monitors Sony CI Media Cloud for new video uploads, automatically initiating the post-production workflow. When a video asset is detected, the flow identifies and extracts the clapperboard segment using Shotstack and uploads it to AWS S3. Gemini AI then performs advanced optical character recognition (OCR) to accurately capture text from the clapperboard, which is saved back into Sony CI as detailed metadata.

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In parallel, OpenAI processes the main video to generate concise summaries and detect significant objects within the footage, enriching the asset with comprehensive metadata. To further optimize asset management, qibb integrates the processing of custom-provided PDFs detailing specific categorization rules for each clip. This ensures that assets are precisely categorized into 'Keep' or 'Not Keep' folders based on their metadata alignment with the provided criteria.

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The entire qibb flow runs seamlessly, offering a fully automated solution that not only saves considerable manual effort but also enhances accuracy and efficiency. The flexible nature of qibb allows easy customization of workflows, adapting quickly to evolving project needs and ensuring your team can always work smarter and faster.