Powering vision AI applications for Retail

Weaving human and machine intelligence to help deep learning teams reach production at scale

retail main img

Expanding AI capabilities in retail

Building computer vision applications for the retail industry means dealing with chronic change and inconsistency, as products and brands get constantly replaced, reorganized and redesigned. To make matters more complex, mistakes or delays in production environments are likely to be customer-facing, so models are required to perform at peak accuracy.

To answer such demands, Dataloop’s platform integrates automatic active learning with human-in-the-loop functions for data validation. This way, deployed models are continuously fed with new, authentic and validated data that comes directly from your production environment, ensuring that your models produce outputs with maximum accuracy and reach production-grade performance at the fastest time to market.

Dataloop's real world applications in retail
Select use cases that Dataloop advances

smart checkout
Cashierless checkouts
shelf management fixed e1568291000860
Shelf management
Product recognition 1
Product recognition
warehouse management
Retail logistics
waste management
waste management
shoplifting prevention
shoplifting prevention

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Dataloop’s Use-cases

Enterprise-grade data engine

Ensuring highest data standards that serve your entire data organization, allowing cross-functional collaboration while keeping your data access internal

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Custom authentication and role permission enforcement. Using advanced Kubernetes-powered infrastructure for on-premises or private storage deployments

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Full encryption to keep your data constantly safe, compliant with military-grade security standards and certified by comprehensive system audits

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High performance for large volumes of data with 99.9% uptime SLA, providing auto-scaling infrastructure that supports your ever-growing data operations

Yeshiaya Odysight
Yishaia Zabary
Senior Data Scientist
david lempert
David Lempert
Head of R&D
Ido ariav
Ido Ariav
Deep Learning Lead