Amine AYARI

Principal-level individual contributor with 15+ years of experience designing and scaling production-grade AI systems across NLP, computer vision, 3D, embedded, and cloud platforms.

2 minute read

Published on January 04, 2026

I’m excited to announce the launch of LatentVideo — an AI-powered video SaaS available at latentvideo.com.

LatentVideo is a pilot project designed to cover the full lifecycle of a product, from initial idea and design to production deployment and go-to-market strategy. It is an end-to-end AI video product, developed entirely by myself, with a strong focus on real-world performance and production readiness.

Product Vision

The goal of LatentVideo is to make high-quality video background replacement accessible, fast, and reliable — without requiring users to deal with complex tooling or infrastructure.

From day one, the product was designed with:

  • a clear user problem,
  • a simple and focused UX,
  • and a scalable technical foundation.

LatentVideo is not a demo or a research prototype — it is built as a usable SaaS product.

End-to-End Ownership

This project covers the complete product stack:

  • Product vision & positioning
  • UX and user flows
  • System architecture
  • Model design and optimization
  • Cloud infrastructure and deployment
  • Operational considerations

Everything — from model training decisions to API design — was implemented end-to-end by me.

AI Video Technology

At the core of LatentVideo is a deep learning–based background subtraction model for video.

Key characteristics:

  • state-of-the-art approach for foreground/background separation,
  • temporally stable results,
  • optimized for fast inference,
  • designed for production constraints.

A strong emphasis was put on model efficiency, not just accuracy:

  • reduced latency,
  • controlled memory usage,
  • predictable runtime behavior.

This makes the system suitable for real-world video workflows, not just offline processing.

System Architecture & Cloud Deployment

LatentVideo is deployed on Google Cloud Platform (GCP).

The infrastructure is designed to:

  • scale with demand,
  • isolate compute-heavy workloads,
  • keep operational complexity under control.

The model is served as a production inference service, integrated into a clean application layer that exposes the functionality as a simple SaaS experience.

Cloud choices were driven by:

  • reliability,
  • scalability,
  • and fast iteration cycles.

A Pilot for Go-To-Market Execution

Beyond the technical aspects, LatentVideo is also a go-to-market pilot.

The project is used to:

  • validate product positioning,
  • test pricing and packaging,
  • experiment with user acquisition channels,
  • and iterate quickly based on real feedback.

It serves as a concrete exercise in turning AI technology into a market-ready product.

Closing Thoughts

LatentVideo represents a complete, hands-on exploration of what it takes to:

  • design an AI product,
  • build it end-to-end,
  • deploy it in the cloud,
  • and bring it to market.

It reflects a core belief:

strong AI systems only matter if they are packaged into products people can actually use.

I’m excited to continue iterating on LatentVideo and to share learnings from this journey.

👉 Visit the product: latentvideo.com

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