AI-powered field service management platform for frontline operations and asset maintenance
Zinier operates a field service management platform built on Java + Spring Boot + React, with ML inference (PyTorch, TensorFlow, Hugging Face) embedded for workflow automation. The company is sales-driven — 11 of 32 open roles are in sales, and project focus spans partner enablement, strategic account expansion, and playbook development — suggesting a channel-led motion targeting enterprises with long sales cycles. Pain points cluster around deal velocity and adoption, with active work on shortening cycles and reducing acquisition costs.
Notable leadership hires: Product Lead
Zinier provides an AI-native field service management platform for enterprises managing distributed technician workforces and critical infrastructure. The platform combines workflow orchestration, real-time mobile dispatch, scheduling automation, and maintenance task management, serving utilities, telecommunications, energy, and EV infrastructure operators. Founded in 2015 and headquartered in San Mateo, the company operates with 51–200 employees and serves customers across the US, UK, Europe, and India. Revenue model centers on enterprise SaaS licensing, with go-to-market activities including direct sales, partner programs, and professional services delivery.
Backend: Java, Spring Boot, MySQL, AWS (RDS, Lambda, DynamoDB). Frontend: React, Angular, Redux. Observability: Prometheus, Grafana, Datadog, Kibana. ML: PyTorch, TensorFlow, Hugging Face. CI/CD and container orchestration via Kubernetes.
Active projects include partner ecosystem activation and enablement, backend development for an ecosystem platform, strategic account expansion, enterprise delivery (especially UK), and improved sales playbook and onboarding for partner teams.
Zinier'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.