Device fingerprinting and location-based identity verification for fraud prevention
Incognia builds device fingerprinting and location-based identity solutions for fraud prevention and user verification. The tech stack (Android, iOS, Python, Kubernetes, AWS, Spark, MLflow) reveals an ML-heavy backend focused on signal processing and model training, while pain points around Kubernetes and database scaling point to infrastructure strain from product growth. Active hiring in engineering and support across four countries (US, UAE, Brazil, Colombia) suggests expansion into new geos and verticals.
Incognia provides identity verification and fraud prevention through persistent device fingerprinting combined with location analysis and tamper detection. The platform serves food delivery, ride-hailing, marketplace, and financial services companies. Founded in 2020 and based in Palo Alto, the company operates with a 51–200-person team. Current focus areas include GTM workflow automation, SSO integration, digital onboarding, and geographic expansion into financial services—alongside ongoing work to reduce fraud costs and improve detection across diverse attack vectors.
Incognia's core stack includes Android and iOS for client-side signal collection, Python and JavaScript for backend logic, Kubernetes and AWS for infrastructure, and Apache Spark, Scala, and MLflow for ML model training and feature engineering.
Incognia is headquartered in Palo Alto, California, and was founded in 2020. The company currently operates across 51–200 employees.
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