Get insights into your computer vision model’s blind spots
An insight management platform for computer vision model performance. Manot pinpoints where, how, and why computer vision models fail. It accelerates model refinement and redeployment processes by 10x, boosts accuracy by 20%, and reduces costs by 32%.
👋 Hi Product Hunt! I’m Chinar, co-founder and CEO of Manot. Grab an ice cream, sit back, relax, and let me take you on our adventure! 🍦🚀 It all began back when I was a computer vision (CV) engineer working on cool projects from surveillance to high-tech drones. But here’s the catch - our AI models were awesome in development but not so awesome in the real world 🙈. I saw models with 95% accuracy during testing begin to fail in production, which causes unhappy customers and a lengthy feedback loop
🧑💻Hey Product Hunt, I’m Erik, the R&D Lead at Manot. Throughout my career in Computer Vision 💻👁️, I have trained various predictive and generative models, all of which require specific and thorough diagnostics and evaluation before being ready for production. Building these pipelines for every single task and model takes a lot of time and resources ⌛, and even doing all of this does not guarantee the same level of performance on your production data as there can be a significant gap between
🤙 Hey there Product Hunters! I’m Haig, co-founder and CPO of Manot. Let’s talk about the life of a product manager in AI 🌍💻. For the past 5 years, I’ve had one goal: ensure each AI product (and thus the underlying model) is not just good, but great for our customers. But here’s the thing, time and time again the models prove to be unpredictable. Every time a model performed poorly or failed, it was back to the whiteboard with the engineering team ✏️. Imagine this process: we detected a problem,
Love continuous feedback loop between pre and post production environments and key stakeholders. Having this process automated streamlines collection of key model behavior quality metrics in production and allows model developers to make faster changes to their ML algorithms based on empirical data in production
So excited about this!! Amazing work team Manot!
A measure of community engagement at launch. Higher means more people noticed and interacted with the product. It's a traction signal, not a quality rating.
Discussion threads divided by interest score. Above 0.30 is strong. Below 0.15 suggests the product got clicks but not conversation.
Categories come from the product's launch tags. Most products appear in 2-3 categories. The primary category is listed first.
The scores reflect launch-period engagement. Historical data is preserved and doesn't change retroactively. The build date at the bottom shows when the index was last refreshed.