IoT sensor platform for supply chain visibility and condition monitoring
Wiliot operates a Physical AI platform that ingests billions of monthly sensing events from battery-free Bluetooth IoT devices and converts them into actionable supply chain insights. The stack—Python, Spark, Databricks, PostgreSQL, MongoDB, DynamoDB, plus Grafana and Tableau for visualization—reveals a company built for high-volume sensor data ingestion, real-time stream processing, and multi-tenant analytics at scale. Active hiring skews heavily toward engineering (5 roles) relative to sales (3), indicating product-stage maturity and internal focus on processing infrastructure and deployment reliability.
Wiliot provides a supply chain visibility platform powered by passive Bluetooth sensors and AI-driven analytics. The platform processes sensing data from IoT Pixels (battery-free tags) to deliver real-time inventory intelligence, condition monitoring, and workflow automation for retailers and logistics operators. Customers deploy the system to track product location and condition across warehouses, transport networks, and retail environments without manual scanning. The company is headquartered in San Diego with 201–500 employees, all hiring currently based in the United States. Active project work spans customer site surveys, real-time data pipeline development, deployment automation, and internal runbook documentation—typical of a B2B infrastructure company scaling its go-to-market motion.
Python, AWS, Azure, GCP, Apache Spark, PostgreSQL, MongoDB, DynamoDB, Kubernetes, Databricks, Grafana, Tableau, and Salesforce. Also uses InfluxDB for time-series data and specialized hardware languages (Verilog, SystemVerilog, RISC-V, Cadence) for sensor integration.
Processes billions of sensing events monthly from battery-free Bluetooth IoT sensors to enable real-time supply chain visibility, inventory intelligence, and condition monitoring. Integrates into existing enterprise systems to automate workflows and reduce product waste.
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Wiliot'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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