Veriff vs Superluminal

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.

Superluminal adds a conversational copilot to product dashboards so end users can ask questions about their data in plain language. You integrate it through a React component or REST API, and it writes Python behind the scenes to filter tables, pivot results, plot charts, and return answers grounded in the dataset already loaded in your app. The target customer is a SaaS team that wants ChatGPT-style data chat without building code generation, sandboxed execution, and prompt tuning in house.

Generic chat widgets summarize text. Superluminal is built specifically for tabular dashboard data: it spins up managed compute to run generated Python, verifies code through reflection, and returns structured answers or charts tied to the user's actual rows. That pipeline (context synthesis, chain-of-thought reasoning, secure execution, and UI customization) is the product, not a thin wrapper around a single model call. It also supports multilingual queries and optional filtered or pivoted views of the underlying data.

Superluminal fits analytics vendors, internal data teams, and any product with an embedded dashboard that wants self-serve exploration. Pricing is pay-as-you-go at $20 per 100 messages after 50 free messages, with enterprise plans for higher volume. Data is encrypted in transit and at rest, purged after each session, and not shared with third-party LLM providers per the company's security page.