Work With Us
BOOK A
FREE
AUDIT.
We map your operations, find where software creates real leverage, and tell you exactly what to build — before you commit to anything.
An OTT client wanted to know if promotional poster generation could be automated using their own film as the source — without a design team. I built an MVP to prove it was possible.
An OTT company producing short films asked a direct question — could AI replace the designer in their poster workflow?
Not stock images. Not generic output. A poster built from their actual film, automatically.
Upload a short film, provide details about it — genre, mood, title — and get a promotional poster out. No manual design work. No stock images. The poster should reflect the actual film.
The MVP was built on open-source models to validate the pipeline before any production investment. Prove it works. Define the upgrade path. Then scale.
“Work within the constraint. Prove the concept. Let the result speak for next steps.”
The goal was never production quality. It was a working proof that the pipeline could exist.
I designed the pipeline entirely around open-source tooling. Hugging Face's diffusion models handled generation. FFmpeg handled frame extraction. The MVP demonstrated the full pipeline end to end — input to output — without requiring any paid API infrastructure.
Premium models like Gemini would have produced higher quality output — but the goal of an MVP is to prove feasibility, not achieve production quality. The upgrade path was clear once feasibility was confirmed.
“A complete end-to-end pipeline. Film in, poster out.”
Every stage was designed and connected — from the moment a film is uploaded to the moment a promotional poster is generated. No manual steps. No external tools.
Client uploads their short film and fills a form — title, genre, mood, description. This context passes through the pipeline alongside the extracted frames.
File UploadFFmpeg extracts frames from the uploaded film at regular intervals — capturing visual samples from across the content to inform the generation model.
FFmpegExtracted frames and form input are passed to a Hugging Face diffusion model. The model generates a poster informed by both the film's visual content and the client's description.
Hugging FaceA generated promotional poster — built from the film's own content and context. No stock images. No designer. Pipeline to output in one automated flow.
Generated PosterMVP built and delivered. Code handed off to the client. Feasibility of the pipeline was demonstrated end to end.
We map your operations, find where software creates real leverage, and tell you exactly what to build — before you commit to anything.