Device intelligence platform for real-time fraud detection and bot identification
Fingerprint identifies over 1 billion unique devices monthly and serves 6,000+ companies detecting fraud and bot activity in real time. The stack reveals a modern, JavaScript-forward architecture (FingerprintJS, TypeScript, React, Vite, Webpack) paired with enterprise sales tooling (Salesforce, HubSpot, Gong, Plantar) and AWS infrastructure—indicating a product-first fraud-detection platform backed by sales-driven GTM. Active hiring is heavily skewed toward engineering, with talent acquisition and scaling challenges (recruiting impersonations, fast scaling, operational maturation) suggesting founder-heavy early growth transitioning into more structured operations.
Fingerprint is a device intelligence platform built to detect fraud, bot activity, and high-risk behavior across web and mobile environments. The company serves mid-to-enterprise customers in fintech, e-commerce, and identity verification—including publicly named references like Dropbox and checkout.com. Fingerprint operates as a fully remote, globally dispersed company with a strong open-source presence (FingerprintJS with 26,000 GitHub stars). Founded in 2020 and backed by $77M in funding, the company identifies visitor intent through behavioral and device signals, processing over 1 billion unique device fingerprints monthly. Core pain points center on operational maturation (system performance, product reliability, talent gaps) as the company scales.
JavaScript, TypeScript, React, AWS (EKS, RDS, IAM, VPC), Kubernetes, Go, Python, Terraform, ArgoCD, Cloudflare, and proprietary FingerprintJS library. Sales stack includes Salesforce, HubSpot, Gong, and Planhat.
Over 1 billion unique devices every month, serving 6,000+ companies including Dropbox and checkout.com for fraud detection and bot identification.
Fingerprint'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.