Error Prediction


Error Diagnosis


Platform for Your
Computer Vision

Achieve superior model performance with The Manot Error Prediction and Diagnosis Platform for Computer Vision Models. Get more intelligent and reliable models,
all while saving both time and money.

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Trusted by Leading AI Teams

AI Assistant for Computer Vision Models

Comprehensive platform for improving computer vision models


Let algorithms do the grunt work - deploy humans only when necessary


Easy to use platform allows you to monitor and optimize model performance


Platform that helps you predict and detect edge cases and outliers

Why Manot?

The only error prediction and diagnosis platform you need – Manot not only detects biases but also provides actionable insights to mitigate them.

Better Data = Better Models

Faster model deployment

Increase in accuracy and F1 score

Lower costs

What Can Manot Do for You?

Diagnose Model Performance

Manot diagnoses the biases of computer vision models in the pre-production phase, by identifying and mitigating outliers that can cause failures.

Detect Hidden Errors

With its advanced scoring system, Manot identifies the areas where your model underperforms, detects hidden biases and suggests ready data samples that will contribute the most to rebalance the training dataset.

Get Actionable Insights

The platform provides self-service analytics and insights for AI teams to curate better data and report system risks.

Deploy Improved Models

Take your improved and bias-free models into production and monitor their performance, ensuring they’re operating reliably in the real world.

Optimize Workflow & Increase Efficiency

Manot lets you optimize the processes and workflows in your team, decreasing the feedback loop from months to hours — saving your time by 10x and reducing costs by 50%.

How Manot Works

Initial learning phase

Firstly, you need to feed a portion of your computer vision model's training data to Manot. This allows Manot to learn how your model was trained and what objects and targets it recognizes.

Observation and monitoring phase

After the initial learning phase, Manot observes and monitors real-world images to detect unusual targets or scenarios that are confusing/tricky and challenging for your AI model to detect and interpret correctly. These are called edge-cases or outliers, aka out of the distribution samples/scenarios.

Comparison phase

Then, Manot begins comparing the targets (it learned from your model) with real-world data. By doing so, Manot detects edge cases, limitations and hidden bias in your model's training dataset. This is important because these edge cases can cause your model to perform poorly in the unpredictable real world conditions.

Analysis phase

When Manot identifies such edge cases in your model's performance, it then analyzes the images based on impact scores. It understands the differences and similarities of objects or parts of the image your model recognizes, as well as which objects/parts might be confusing and cause the model to make incorrect or uncertain interpretations.

Based on this analysis, Manot provides impact scores to each image. The impact scores represent how much of an impact that particular image could have on your model's performance. Images with higher impact scores are more likely to cause your model to perform poorly in the real world.

Prioritization and classification phase

When Manot detects unusual cases or scenarios (edge cases) that could impact your model's performance, it collects, prioritizes and classifies all the data (the images or videos). This way, it shows possible scenarios and images based on real-world data where your model has hidden bias because of limited training data.

If the real-world images aren't enough, Manot can use its generative AI capabilities to create various scenarios. This way you get even more insights and data to refine your model's training data based on real-world conditions.

Provision of alerts and insights phase

When Manot detects unusual cases or scenarios (edge cases) that could impact your model's performance, it alerts you to investigate these insights. For example, if your model is trained to recognize black dogs, but struggles with brown dogs or black dogs at night, Manot will identify these scenarios and bring them to your attention.

Analysis and refinement phase

Finally, Manot provides you with all this information, insights, and data for your review and decision-making. This enables you to further refine your model based on changing and unpredictable/unstable real-world conditions, thus improving its performance and reliability.

Enabling Safe and Reliable AI in the Real-World

Manot’s Observability Platform is bridging the gap between the management, engineering and data teams

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Manot is transforming how AI teams deploy computer vision models into production.

Let’s talk about how we can do the same for you.

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