Veriff vs Scale

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.

Scale AI supplies training data, model evaluations, and applied AI systems for labs, enterprises, and governments building machine learning products. The company runs the Scale Data Engine for annotation and RLHF, the GenAI Platform for full-stack generative workflows, and Donovan for defense-oriented intelligence work. Customers include Meta, TIME, Instacart, and public sector agencies that need audited, high-volume data pipelines rather than ad hoc labeling spreadsheets.

Where many labeling vendors focus on a single modality or outsource quality control entirely, Scale combines ML-assisted pre-labeling with layered human review across text, image, video, and 3D sensor fusion including LiDAR. Its Generative AI Data Engine adds prompt generation, red teaming, and benchmark evaluations in one loop, so teams can train, stress-test, and compare frontier models without stitching together separate vendors.

Scale fits ML teams shipping self-driving perception, document NLP, generative assistants, and government programs that demand traceable data provenance. Teams book a demo for custom contracts because pricing is enterprise sales only, not a self-serve checkout page.