Veriff vs Teachable Machine

Veriff verifies government IDs, selfies, and business records so companies can onboard users, run KYC and AML checks, and block fraud in about six seconds. Its decision engine analyzes behavioral data across each session and is trusted by more than 3,000 companies worldwide, including Instacart, Western Union, Wise, and Monzo. Automated identity checks report roughly 99.6% accuracy.

Many verification vendors license document forensics, liveness, and face-match models from third parties and rebrand the stack. Veriff builds its decision engine, liveness matching, and document forensics in-house, so one team owns accuracy when a case fails review. AML and database checks use named specialist partners listed in its sub-processor documentation rather than hidden behind the brand.

The platform combines document checks, biometric matching, passive liveness detection, and database cross-referencing into one workflow. It supports more than 12,500 government-issued identity documents across 230+ countries and territories, with end-user flows available in 50 languages. Beyond onboarding, Veriff covers proof of address, age validation, AML screening, business verification across 300+ jurisdictions, and ongoing biometric re-authentication.

Financial services, marketplaces, iGaming, mobility, and HR teams use Veriff for KYC, KYB, age assurance, and account reverification at scale. Fraud tools like CrossLinks, FaceBlock, and session video recording help teams spot synthetic identities and repeat offenders across the shared verification network.

Teachable Machine is a browser-based machine learning trainer from Google that lets you build image, sound, and pose classifiers without writing code. You gather examples into classes, train a model in the browser, then export it as a real TensorFlow.js model for sites, apps, or hardware projects. The whole workflow runs in three steps: gather samples, train, and export.

Most ML platforms assume you know Python and cloud infrastructure. Teachable Machine deliberately targets people who want a working classifier in minutes, not a data science pipeline. Training can stay entirely on your device, so webcam and microphone data never has to leave your computer. The export path points at creative tools like p5.js, Glitch, and Arduino, not enterprise dashboards.

Classroom teachers use it for bias and ethics lessons, students prototype game controllers and assistive devices, and hobbyists build classifiers before touching TensorFlow code. It is free, requires no account to start, and the original 2017 version is still available for camera-only experiments.