AI and robotics platform for retail shelf intelligence and inventory accuracy
Simbe operates a robotics-driven shelf intelligence platform built on C++, Python, ROS, and cloud infrastructure (GCP, AWS, Azure, BigQuery). The tech stack reveals a hardware-robotics core (Nvidia Jetson, ROS 2) paired with data-pipeline maturity (dbt, Airflow, BigQuery, Looker), suggesting the company has moved beyond single-store pilots into chain-wide deployments. The engineering-heavy hiring skew (10 eng roles open, half mid-level) and active DevOps/CI-CD development indicate they're scaling robotics operations and observability—consistent with stated pain points around robot uptime and end-to-end service visibility.
Notable leadership hires: IT Security Lead
Simbe builds an autonomous retail platform that uses robotics, AI, and RFID sensors to audit shelf availability, pricing accuracy, and inventory in physical stores. The platform integrates with existing retail systems (Salesforce present in stack) and targets large retailers across multiple sectors globally. Founded in 2014 and headquartered in South San Francisco, the company operates at 51–200 employees. Active projects include chain-wide deployments, client pilots, and expansion playbooks—reflecting a go-to-market motion focused on moving customers from pilot phase to strategic chain rollout. Pain-point tracking shows significant internal focus on scaling robotics reliability and converting single-location proofs-of-concept into enterprise-scale operations.
Simbe uses C++, Python, ROS/ROS 2, Nvidia Jetson, and cloud platforms (GCP, AWS, Azure). Data infrastructure includes BigQuery, dbt, Apache Airflow, Looker, and Tableau. Sales and ops tools: Salesforce, 6sense, Apollo, LinkedIn Sales Navigator.
Simbe is headquartered in South San Francisco, California. The company has 51–200 employees and was founded in 2014.
Simbe's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
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