Computer Vision for Startups | How to Build Real Time Intelligence

Описание: In this episode of Frame by Frame, Clueso's CTO - Prajwal sits down with Software Developer - Ashish, to break down how startups can actually build computer vision systems that work in production.

From classical pixel-based techniques to deep learning models, embeddings, and modern vision-language models (VLMs), this conversation explores how real AI systems are designed when performance, cost, and latency actually matter.

We discuss how these approaches are used inside Clueso to process videos, detect meaningful frames and build intelligent video features at scale.

In this episode, we cover:
• What traditional computer vision techniques still do better than modern AI
• When to use deep learning models vs classical CV algorithms
• How embeddings help analyze videos efficiently
• Why vision-language models changed the field of computer vision
• The hidden challenges of analyzing videos vs images
• How to design CV systems that balance cost, speed, and accuracy
• Infrastructure decisions: self-hosting models vs inference providers
• Why “bringing a nuke to a knife fight” is a common mistake in AI systems

If you’re building an AI startup, working with image/video intelligence, or trying to understand how real-world computer vision systems are structured, this episode will give you a practical engineering perspective.

🎧 Watch till the end to understand how startups should think about choosing the right vision techniques for real products.

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Learn more: https://www.clueso.io/
Apply to join Clueso: https://www.clueso.io/careers


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