PLEXOR connected the information in retail camera feeds with the transactions at the register. Zain Raza led the backend and AI platform build for Ego Eimi, turning those inputs into events a manager could review.
Retail video and point-of-sale records often sit in separate systems. Investigating a transaction means finding the matching moment in a recording, while other operational events may have no corresponding receipt.
The product needed to bring those signals together and describe a potential event clearly enough for a manager to examine it. The existing IP camera infrastructure was an input to that design.
The backend combined live video with transaction data. Vision-language analysis interpreted events and produced plain-language descriptions connected to their context.
The application supported review of potential loss, service and safety events. Linking the footage and transaction information let a manager examine the underlying evidence rather than relying only on an alert label.
Project materials showing the workflows and interfaces described in this case.
Video and point-of-sale event analysis. The delivery section explains the workflow demonstrated here.
The delivery connected retail observation and transaction data in one analysis platform. The product’s engineering contribution was the route from separate inputs to an inspectable event, with a person responsible for interpreting what it means.
A video-analysis system needs explicit access, retention and review rules. An AI alert should not become an automatic accusation or employment decision. The operating scope should cover how footage is handled, how incorrect alerts are reviewed and who can act on the information.
Project contribution: Zain Raza. The scope described above identifies the work behind this case.
Editorial review: Ego Eimi · Updated September 6, 2026 · About the team
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