Video content is king, but producing it at scale is expensive and time-consuming. What if you could just type a company name and get a marketing video in minutes?
In this project, I built an end-to-end automated pipeline that researches a company, writes a script, generates AI video scenes, and assembles them into a professional ad—completely autonomously.
The Tech Stack
I chose a modern, high-performance stack for this tool:
- Orchestration: Python (Modular, easy to debug)
- LLM (Scripting): Google Gemini 2.0 Flash (Fast, cheap, and handles structured JSON output perfectly)
- Video Generation: Kling v2 via Fal.ai (State-of-the-art AI video generation)
- Market Research: Tavily API (Real-time web search for company context)
- Assembly: FFmpeg (The Swiss Army knife of video processing)
How It Works
1. Research & Context
The pipeline starts by taking a simple input: a brand name (e.g., "Supabase"). It uses Tavily to scrape the web for the brand's tagline, core features, and visual style. This ensures the ad actually feels like the brand.
2. Script Generation
We feed this research into Gemini 2.0 Flash with a strict system prompt. The goal isn't just "write a script"—it's "write a script with Visual descriptions, Voiceover text, and estimated duration per scene."
Gemini returns a JSON object containing a list of scenes.
3. Asset Generation
This is where the magic happens. We iterate through the scenes:
- Visuals: We send the visual description to Fal.ai's Kling v2 api to generate 5-second video clips.
- Audio: We use Edge TTS to generate a natural-sounding voiceover for each line.
4. The Final Assembly
Finally, Python and FFmpeg take over. We concatenate the video clips, overlay the audio track, and even add background music and subtitles automatically.
Challenges & Optimizations
One of the biggest hurdles was consistency. AI video models tend to hallucinate. To fix this, I implemented a strict prompt engineering strategy that enforces a specific visual style (e.g., "cinematic, 4k, minimalist") across all generated clips.
This project demonstrated the immense power of chaining specific AI tools together. It's not just about one model; it's about the pipeline.