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Manot

Get insights into your computer vision model’s blind spots

5.0•9 reviews•

479 followers

Get insights into your computer vision model’s blind spots

5.0•9 reviews•

479 followers

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LLMs
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Testing and QA software
•
AI Metrics and Evaluation
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%.
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Company Info
manot.ai
Manot Info
Launched in 2023View 1 launch
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p/manot-2
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Launch tags:
SaaS•Artificial Intelligence•Data & Analytics
Launch Team
Chris MessinaArthur PapikyanChinar Movsisyan

What do you think? …

Chinar Movsisyan
Chinar Movsisyan

Manot

Maker
📌
👋 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
Report
1yr ago
Liana Karapetyan
Liana Karapetyan

dzook

@chinarmovsisyan nice journey! Great to see the free tier available.
Report
1yr ago
Chinar Movsisyan
Chinar Movsisyan

Manot

Maker
@liana_karapetyan7 Thanks! We worked hard to make it happen!
Report
1yr ago
Edgar Ohanyan
Edgar Ohanyan
@chinarmovsisyan Super important tool! Congrats! Wondering which use cases do you support.
Report
1yr ago
Chinar Movsisyan
Chinar Movsisyan

Manot

Maker
@edgar_ohanyan2 Thanks! We are use case agnostic! Here are some of the many use cases Manot has addressed: surveillance, drone automation, autonomous vehicles, construction, manufacturing, industrial robots, etc. Happy to walk you through it tailored to your use case!
Report
1yr ago
Elias Stråvik
Elias Stråvik
StakeWeb

StakeWeb

@chinarmovsisyan big congrats on the launch! Just shot and posted a landing page feedback vid on X 👏
Report
1yr ago
Erik Harutyunyan
Erik Harutyunyan

Manot

Maker
📌
🧑‍💻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
Report
1yr ago
Tigran Hovhannisyan
Tigran Hovhannisyan
@ero1311 thank you very much for the product and technical description❤️ love it❤️ wanted to know do you have some lower bound for custom test set or your algo works even with single test set image ?
Report
1yr ago
Erik Harutyunyan
Erik Harutyunyan

Manot

Maker
@tigran_hovhannisyan9 that's a really good question! Actually there’s no lower bound for this, but the more images you provide the better will our solution understand the weaknesses of your model. Considering our numerous experiments across different tasks and datasets we would recommend to start with at least 100 images.
Report
1yr ago
Tigran Hovhannisyan
Tigran Hovhannisyan
@ero1311 thank you very much! One more question. Does that number changes for different tasks such as classification, detection, etc. or it’s the same for all of them ?
Report
1yr ago
Ashot Martirosyan
Ashot Martirosyan
Hello @ero1311, I'm genuinely impressed by the quality of your work—it's quite remarkable. Reflecting on my past experiences, a particular question arises concerning models designed for predicting highly specific objects that might not be well-represented in public datasets. This situation naturally raises concerns about the scarcity of good data samples. How your algorithms will typically address such circumstances? Thanks!
Report
1yr ago
Erik Harutyunyan
Erik Harutyunyan

Manot

Maker
@tigran_hovhannisyan9 I would say the number doesn't depend that much on the task at hand, rather on the number of semantic classes you are planning to predict. I mean if you are trying to predict 120 classes starting with 100 images obviously will not be enough for Manot to analyze the weeknesses of your model :)
Report
1yr ago
Haig Douzdjian
Haig Douzdjian

Manot

Maker
📌
🤙 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
Report
1yr ago
Manana Hakobyan
Manana Hakobyan
@haig_douzdjian Fellow AI product manager here! - what level of technical knowledge should the average manot PM user have for getting the best out of the product?
Report
1yr ago
Haig Douzdjian
Haig Douzdjian

Manot

Maker
Great question @mananahakobyan ! Manot was built with PMs and data teams in mind. The platform requires little technical knowledge. Most of the "work" is done by connecting integrations, which then allows Manot to synergize Product KPIs and Engineering KPIs. Let me know if you have any more questions!
Report
1yr ago
Marcin Lukanus
Marcin Lukanus
Occasio

Occasio

@haig_douzdjian Long time no see, but huge congrats on the launch Haig! Best of luck with Manot!
Report
1yr ago
Haig Douzdjian
Haig Douzdjian

Manot

Maker
@marcin_lukanus Thanks man! Will reach out shortly
Report
1yr ago
Manana Hakobyan
Manana Hakobyan
•1 review
As a product manager I love the fact that I am looped in AI development and have more visibility over the blind spots! Recommend this product.
Report
1yr ago
Haig Douzdjian
Haig Douzdjian

Manot

Hey Manana! We love that you love it. We might have to steal your quote for our website ;)
Report
1yr ago
Chinar Movsisyan
Chinar Movsisyan

Manot

Hi Manana, thank you so much!
Report
1yr ago
Cher Hu
Cher Hu
•1 review
As an AI Product Manager, model quality can be a real pain point and glad to see an effective tool out there helping product managers with the issue. The founding team is amazing!!
Report
1yr ago
Haig Douzdjian
Haig Douzdjian

Manot

Hey Cher! Absolutely the pain points we noticed! We think you all are amazing too!
Report
1yr ago
Chinar Movsisyan
Chinar Movsisyan

Manot

Hi Cher! Your feedback means a lot! Thank you so much!!
Report
1yr ago
Samvel Martirosyan
Samvel Martirosyan
•3 reviews
Nice idea, good job, going to go deeper and try it
Report
1yr ago
Haig Douzdjian
Haig Douzdjian

Manot

Thanks Samvel! We'll be here if any questions arise :)
Report
1yr ago
Chinar Movsisyan
Chinar Movsisyan

Manot

Hey Samvel! Excited to have your feedback on the platform.
Report
1yr ago