Persona Studio AI

Project Overview & Vision: PersonaStudio AI introduces an efficient "Write Once, Run Everywhere" paradigm for digital media assets. Traditional content creation workflows are heavily fragmented; adapting a single master video for diverse platforms and audiences usually demands repetitive human effort or computationally expensive AI re-parsing. PersonaStudio AI addresses this bottleneck by decoupling heavy video ingestion from the generation layer. Built for the AMD Developer Hackathon (Unicorn Track), the platform analyzes a raw video exactly once to extract its semantic essence into a highly compressed, persistent JSON payload called Content DNA. Dual-Path Ingestion & Multi-Model Architecture: The core innovation lies in its flexible Understanding Engine, which maps video data via two selectable methods: Whisper Transcript Path: Leverages ffmpeg and Groq (Whisper Large v3) to extract audio and generate precision timestamped transcripts, which are compiled into the Content DNA using Fireworks AI LLMs. Gemma 4 Vision Path: Samples evenly-spaced frames using ffmpeg and passes them directly to a vision-capable Gemma 4 model via OpenRouter, mapping visual context without requiring any audio transcription. Decoupled Transformation at Scale: Both ingestion channels converge on the identical ContentDNA schema persisted as a JSONB object in Supabase Postgres. Once saved, users can instantly trigger /generate requests to transform that single structural blueprint into platform-specific LinkedIn posts, blog articles, short-form scripts, or captions customized by tone and persona. Because the Transformation Engine queries the static database payload instead of re-processing the source media, the system minimizes API latency, avoids redundant AI compute overhead, and optimizes throughput on scale.