Tive operates a hardware-software platform for supply chain monitoring, combining embedded sensors (BLE, I2C, UART) with a cloud backbone of Kafka, Kinesis, and PostgreSQL. The tech stack is skewed toward data infrastructure and backend languages (Python, Go, Java, C++) rather than frontend, and the active project list centers on a large-scale real-time data platform and predictive shipment analytics—indicating engineering focus on throughput and accuracy over consumer-facing UX. Sales hiring (15 roles) outpaces engineering (8), pointing to a company scaling go-to-market and customer expansion alongside product maturity.
Tive provides real-time visibility into shipments for shippers, logistics providers, and retailers worldwide. The platform combines proprietary sensor hardware with cloud-based condition monitoring and analytics to reduce loss, delay, and damage in transit. Tive operates a global footprint, hiring across eight countries including the United States, Mexico, Colombia, Germany, and South Africa. The company serves over 500 enterprise customers and operates a 24/7 monitoring service alongside its self-service cloud platform. Current scaling efforts span go-to-market strategy, customer onboarding, account management, and internal process improvement to support expansion.
Tive runs Kafka and AWS Kinesis for data streaming, PostgreSQL and SQL Server for storage, Elasticsearch for search, Kubernetes and Docker for orchestration, and Go, Python, Java, and C++ for backend services. Frontend uses Angular. Salesforce handles CRM.
Tive is headquartered in Boston, Massachusetts and was founded in 2015. The company has 201–500 employees and is privately held.
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Tive'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 →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.