Veriff vs Datagen

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.

Datagen was a data science platform for generating synthetic visual datasets used to train computer vision models. The Tel Aviv company built a self-serve simulator that rendered photorealistic 2D and 3D imagery with granular control over scenes, lighting, and human-centric scenarios. Fortune 500 teams used it for AR, VR, in-cabin vehicle safety, robotics, and IoT security workflows.

Where most annotation vendors sell labeled real-world photos, Datagen focused on procedurally generated 3D scenes you could tune without collecting new camera footage. That trade-off cut dataset build time from days to hours for teams that needed rare poses, lighting, or edge cases real cameras rarely capture.

Datagen ceased operations in 2024 after generative AI reduced demand for its rule-based synthetic data stack. The datagen.tech domain now returns Cloudflare DNS resolution errors, and public reporting confirms the shutdown despite about $20 million remaining in the bank after $72 million in total funding.