Nexite operates a computer vision and IoT platform that captures real-time customer interactions in physical retail environments—what merchandise gets picked up, tried on, or abandoned. The tech stack (Go, Python, AWS/Azure/GCP, BigQuery, Bluetooth Low Energy) reveals a hardware-to-cloud pipeline built for high-volume sensor data ingestion and analysis. Active projects around GenAI interaction layers and wireless IoT systems, combined with acute pain points in network congestion and high-throughput data flow, show the company is scaling beyond basic computer vision toward AI-driven store recommendations and expanding sensor networks across store chains.
Nexite provides a Live Retail Optimization platform designed for brick-and-mortar retailers. The system uses in-store sensors and computer vision to track customer behavior—product touches, try-ons, abandonment—and feeds that data through a cloud analytics backend (BigQuery, SQL) to generate recommendations for merchandise placement, inventory allocation, and employee guidance. Founded in 2017 and based in Tel Aviv, the company serves mid-market and enterprise retail chains. The engineering-heavy hiring profile (7 senior engineers, 1 product hire) and focus on hardware bring-up, firmware development, and network integration suggest an organization deepening hardware capabilities and IoT rollout rather than expanding sales or customer success infrastructure.
Computer vision sensors with Bluetooth Low Energy and wireless IoT connectivity, integrated with a cloud backend (AWS/Azure/GCP, BigQuery, Python/Go) that processes and analyzes the captured data in real time.
Yes. 7 senior engineering roles are open (mostly hardware, firmware, and IoT infrastructure), with 1 product hire. Hiring is currently limited to Israel.
AWS, Azure, and GCP. BigQuery is used for data warehousing and analytics on customer interaction data.
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