Decision intelligence platform automating enterprise business decisions
Aera builds a decision intelligence agent that monitors business operations, generates real-time recommendations, and executes decisions autonomously. The tech stack reveals a mature ML infrastructure (TensorFlow, PyTorch, scikit-learn, Pandas) paired with cloud-native operations (Kubernetes, Istio, Prometheus, Grafana, OpenTelemetry), indicating heavy investment in both model performance and production reliability. Current hiring velocity is accelerating across engineering and sales, with active projects spanning real-time AI inference, multi-cloud infrastructure, and ML solutions implementation—suggesting a push to scale both the platform and GTM motion.
Notable leadership hires: Engineering Director
Aera Technology, founded in 2017 and based in Mountain View, develops a decision intelligence platform for enterprise operations. The product targets supply chain, finance, and sales functions—domains where real-time decision automation can drive efficiency and sustainability. The 201–500 person organization is distributed across the United States, India, Romania, and the United Kingdom. Core technical surface includes TensorFlow and PyTorch model training, a Kubernetes-native deployment layer, observability infrastructure (Prometheus, Grafana, OpenTelemetry), and connectors to business systems (Salesforce, Power BI, Tableau). Active pain points center on enterprise sales motion (acquiring large clients), scaling QA for AI-driven systems, and managing model performance across multi-cloud deployments.
Aera uses Python, Java, TensorFlow, PyTorch, and scikit-learn for ML; Kubernetes, Istio, and Linkerd for cloud infrastructure; Prometheus and Grafana for observability; and Salesforce, Power BI, and Tableau for business data integration.
Aera has active hiring in the United States, India, Romania, and the United Kingdom.
Aera Technology'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.