Improving packing quality using video analytics

With Lightly's help, Rabot scales its business and onboards customers 2x faster.


Rabot uses AI-powered cameras to improve the packing quality of e-commerce orders. Their vision AI packing solution, Rabot Pack is installed above packing stations in e-commerce warehouses. Through machine learning, Rabot identifies errors and verifies the contents of every single order. This allows customers to continuously improve their current processes and helps floor managers discover potential problems easily. This unique approach to augmenting the workers’ abilities is implemented seamlessly in various environments.


Rabot’s customers have different layouts for their packing stations. Therefore, each customer’s environment differs from previous locations. As a result, each customer onboarding requires a retraining of the models for perfect integration into the new environment.

For Rabot, it was crucial to find a solution to address the following issues:

  • Scalability: Retraining the model requires a lot of data from the customers. Those images/videos then need to be analyzed to determine which images should be annotated and used for retraining.
  • Selection: Selecting the right data for training is essential for seamless integration. Existing selection processes were neither efficient nor could they provide an intelligent data selection.

Subsequently, Rabot could only onboard a certain number of customers at a time. They needed to find an efficient way to select the most interesting data to annotate the most relevant images.

Scalable and Efficient Data Curation using Lightly

Rabot and Lightly have worked together closely to tackle the above challenges.

Lightly selects the most relevant data to help Rabot retrain its model for each customer as part of its machine learning pipeline. It uses the self-supervised learning feature to find the most diverse images.

Thanks to Lightly, Rabot reduced the amount of data required for retraining, resulting in a higher speed for retraining. In practice, Rabot could identify the 10% relevant training data while keeping model performance high. Thus, shortening the retraining process time by 50% significantly resulted in a 2x faster customer onboarding for Rabot.


In a nutshell, Rabot was able to (1) gain relevant insights, (2) select the best data based on those insights, and (3) increase their model performance. This enabled them to:

  • process over 50M images within 6 months using Lightly.
  • reduce the retraining process time by 50%.
  •  double their customer onboarding speed. 
  • improve their model accuracy by 10%.

Client review

"Lightly is hyper-focused on finding thousands of relevant images from millions of video frames to improve deep learning models. The Lightly platform enabled us to build models and deploy features more than 2x faster and unlock completely new development workflows. I can recommend every MLOps team with a lot of data to integrate Lightly."

Isura Ranatunga

Co-Founder and CTO







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