StoriBoard is a UK-based social networking platform where creators upload and share video content. As the platform grew, the volume of video created a new challenge: valuable information was locked inside media that was difficult to process, search, and organize. Manual transcription was time-consuming, editing workflows were slow and inconsistent, and the platform lacked a scalable way to understand the content within each video. StoriBoard needed automation that could keep pace with growing creator activity without increasing operational overhead.

The challenge was not simply processing video. It was understanding the information inside it and making that information useful across the platform. They needed to reduce manual processing while making growing volumes of content easier to search, categorize, and discover. Three challenges stood in the way.

• Manual Transcription – Video required significant processing before its content could be searched or analyzed.
• Slow Editing Workflows – Removing pauses, silent sections, and unnecessary content required manual effort.
• Limited Content Discovery – Without structured transcripts and metadata, valuable information remained difficult to find and categorize.

Adople AI designed a unified video processing pipeline combining speech recognition, automated editing, and NLP-powered content analysis. OpenAI Whisper Large v2 and Stable Whisper generate multi-language transcripts with word-level timestamps. Automated video processing identifies silent and inactive sections, while transcript-based editing helps creators modify content through text. NLP models then analyze transcripts for topics and sentiment, creating structured signals that support search, categorization, and recommendations.