Radar operates a location-intelligence platform built on Android, iOS, Google Maps, and a modern data stack (Airflow, Spark, Athena, Redis, Kubernetes). The company handles 1 billion API calls per day and maintains 10,000 queries per second, revealing a data-intensive infrastructure challenge that's driving their next-generation data platform project and multi-region deployment work. Engineering and security hiring dominate the active role mix, paired with Rust and TypeScript adoption, suggesting both scale and performance are bottlenecks.
Radar provides geofencing SDKs, location APIs, and AI-driven geolocation solutions to mid-market and enterprise customers. Founded in 2016 and based in New York, the company serves use cases spanning customer engagement, operational optimization, and fraud protection. Their platform processes billions of location events daily across mobile (iOS, Android) and backend systems, with customers in gaming, retail, and logistics sectors. Post-sales scaling and high-volume data handling are active operational focuses.
Radar runs on Android, iOS, Node.js, React, TypeScript, Rust, Kotlin, and Objective-C for client-side work. Backend: AWS (EKS, Athena), MongoDB Atlas, Redis, Kubernetes, Apache Airflow, Spark. Data pipelines use OpenStreetMap and MapLibre. Infrastructure: Terraform, CircleCI, Cloudflare, PagerDuty monitoring.
Active projects include mobile SDK feature development, multi-region deployment, a next-generation data platform, fraud detection heuristics, gaming customer alerting, and full-stack location infrastructure. The company is also planning fundraising models and scaling post-sales implementations.
Radar'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.