Manot

👋 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 between product managers and CV engineers. So, myself and a few talented friends rolled up our sleeves and got to work. After endless cups of coffee ☕, along with some laughs and cries… Manot was born. We were on a mission - to make CV models smarter before and after being in the real world! 🤓 And guess what? Our little mission got some love! We raised a pre-seed round with the amazing people at Argonautic Ventures, Berkeley SkyDeck, and SmartGateVC 💜. Being the detectives we are 🕵️🕵️, we talked to over 200 product managers, CV engineers, and data scientists to make sure this problem was felt everywhere. And the response? Mind-blowing 🤯! We’ve got our MVP into the hands of pilot customers, including two Fortune 500 companies! Here is a quick glimpse into how it works 💻: We developed a scoring algorithm that takes the inference results on a given model’s test dataset, and spits out predictions about the model’s blind spots 🧠. We offer on-premise or cloud solutions along with access to our 5 billion image data lake and generative AI modules. For a deeper dive into our tech, see Erik’s comment below! Now, here’s the cherry top 🍒: Manot now has a free tier! We’re throwing open the doors so everyone can play with Manot. This isn’t just about growing the platform; it’s about learning from you. So ask questions, reach out, and let’s make something amazing together! Thank you for being a part of this adventure - your support, feedback, and ideas are what keep us going 🚀💜 Cheers, Chinar

🧑‍💻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 your test sets and the production data. Manot is the remedy to these problems. We have created a solution that readily diagnoses classification, segmentation, and detection models without even requiring the model itself, using only a small set of its predictions, ground truths, and raw images. By analyzing the weaknesses of your models we are able to pinpoint scenarios where your model will fail both from your raw data pool, huge data lakes of our partners or even generate such cases by leveraging the state-of-the-art GenAI capabilities 🧠 Leave comments below and we can discuss! 🚀 - Erik

🤙 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, reported the problem, wait for a fix, and hold our breath while we hope it works. Then we repeat this process again, and again, and again. It was a never-ending, lengthy feedback loop 👎 That is why Manot is so personal to us. It solves a problem we saw, hated, and did not conquer 🦦. By proactively addressing AI model performance in production, along with a more automated model evaluation and data curation pipeline, we are inherently solving the feedback loop problem. Excited to hear your thoughts and dive into discussions! - Haig