B2B infrastructure for global ground mobility and delivery via API and SDK
Elife Transfer runs a white-label mobility and delivery network that partners embed through API, SDK and agentic integrations, backed by a Java, Go, Kafka and PostGIS stack across AWS, GCP and Azure. RAG, Firecracker and an LLM orchestration build point to an AI-native transaction engine meant to replace manual operations. Hiring is accelerating and ops-led, aimed at supplier expansion and coverage reliability.
Elife Transfer, founded in 2019 and headquartered in Burlingame, California, provides B2B infrastructure that lets enterprises offer ground transportation and delivery under their own brand. Services span airport transfers, private rides, shuttles, charter buses, ride hailing and food delivery, sourced from aggregated ride suppliers, local fleets and delivery partners. Partners connect via API, SDK, AI agent or white-label integration. The privately held company has 201–500 employees and hires across the United States, India, Vietnam and the Philippines.
Elife Transfer builds on Java, Go, Python and Scala with MySQL, PostGIS and Kafka, runs on AWS, GCP and Azure, uses Docker and Firecracker, and integrates Google Maps, Apple Maps, Alipay, Paytm and Apple Pay.
Yes, hiring is accelerating, led by operations with additional engineering, product and sales roles, mostly at manager level and above, across the United States, India, Vietnam and the Philippines.
Current initiatives include expanding and onboarding its supplier network, building LLM orchestration infrastructure, a low-code agent-building framework, an AI-native transaction engine, and bidirectional API/SDK partnerships.
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Elife Transfer'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.