Welcome to Manot

Manot is a model observability platform specialized for computer vision
Meet Manot, the model observability platform designed to improve your computer vision model's performance and reliability. By identifying and analyzing edge cases, limitations, and hidden biases in your model's training data, Manot provides you with essential insights about where your model's weak points are, allowing you to conduct targeted model improvement. By using Manot, you will improve the efficiency of the model improvement process by 10x while cutting your costs in half.
Demo of Manot in action

The Problem

Computer vision models used for image recognition, object detection, and other tasks can have hidden biases that could result in incorrect classifications. This bias can be unintentionally learned during the training process due to the dataset not capturing all the different scenarios that the model will have to interact with in the real world. This issue could have serious consequences such as an autonomous vehicle failing to correctly operate in new environments, or have biased outcomes when dealing with diverse populations. Despite attempts to eliminate bias through increasing training data, there is no guarantee that hidden biases will be discovered or fixed until after the model fails.

How Our Solution Works

How our technology works
We offer a solution to this through our product, Manot. Manot is a model refinement platform that can quickly identify potential biases that might cause a product to fail. By recognizing, diagnosing, and mitigating biases in real-time, Manot can help improve model accuracy, reduce workload for engineers, and save businesses significant amounts of time and money.
Manot works in several phases, beginning with an initial learning phase where it learns how your computer vision model was trained and what it recognizes. It then observes and monitors real-world images to detect unusual targets or scenarios that are challenging for your model to interpret correctly. Manot compares the targets learned from your model with real-world data to detect hidden biases in your model's training dataset, analyzing the images based on impact scores to identify which ones could have the most significant impact on your model's performance. Manot prioritizes and classifies this data, providing insights and alerts for you to investigate, before providing all the information, insights, and data for your review and decision-making to refine your model and improve its performance and reliability.

Our Value Proposal

Manot is the only comprehensive observability platform you will need. Using our platform, you will deploy models faster, drastically reduce the feedback loop, and increase your model's accuracy, all while cutting down on the costs involved with model refinement.