Veriff vs Signzy

Veriff has a disclosed valuation of $1.5B, while Signzy's valuation is not publicly available, making a direct size comparison difficult. Veriff employs 500-1000 people.

Veriff has raised more in total funding at $192M, compared to Signzy's $26M — a gap of $166M. Combined, the two companies have completed 4 known funding rounds.

Veriff leads with an Awaira Score of 83/100, while Signzy sits at 63/100. That 20-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Veriff was founded by Kaarel Kotkas in 2015. Signzy was founded by Nikhil Idnani in 2015. Visit each company's profile on Awaira for a full founder biography.

Veriff: Veriff provides AI-powered identity verification software, using computer vision and machine learning to authenticate identity documents and match biometric selfies in real-time during remote digital onboarding flows for financial services, mobility, gaming, and sharing economy companies. The Tallinn-founded company platform processes identity verifications across more than 230 countries and supports over 10,000 document types in its classification and fraud detection models.\n\nThe company raised approximately $192 million including a $100 million Series C round led by Tiger Global, reaching a valuation of $1.5 billion. Veriff counts Bolt, Monzo, Wise, and numerous regulated enterprise clients among its customers, processing millions of identity verifications monthly as a critical compliance infrastructure component for digital-first businesses. The company has expanded from Tallinn with offices in Tallinn, London, New York, and Barcelona, reflecting both its European origin and its growing US market penetration.\n\nVeriff competes in the global identity verification market alongside Onfido (acquired by Entrust), Jumio, and Persona, with the market expanding rapidly as digital financial services, crypto exchanges, and sharing economy platforms face increasing KYC regulatory requirements. Its differentiation includes unusually broad document type coverage, high automation rates on complex and degraded document images, and fraud detection models trained on a large global dataset of identity fraud attempts. Veriff represents one of the most successful technology exits from the Estonian startup ecosystem and a flagship example of Baltic technology entrepreneurship. Signzy: Signzy is a digital identity verification and KYC automation platform that uses AI-powered document analysis, liveness detection, and database integration to enable financial institutions and regulated businesses to onboard customers remotely with regulatory compliance. The platform processes identity documents, conducts video KYC, and provides risk scoring through APIs that integrate with banking and fintech onboarding workflows.\n\nThe company raised approximately $26M in Series B funding from investors including Vertex Ventures and Arkam Ventures, and serves a large base of banks, NBFCs, insurance companies, and fintech platforms in India. Signzy processed tens of millions of KYC verifications and has been recognized by the Reserve Bank of India for its video KYC framework contributions.\n\nVideo KYC and digital identity verification have become mandatory for regulated financial services in India, creating a large and recurring market for compliant identity infrastructure. Signzy's deep regulatory expertise and production track record across India's financial system give it a significant advantage over generic identity verification platforms that lack India-specific compliance depth.

Both Veriff and Signzy launched in 2015. Same year, but even a few months' head start matters in AI — early movers lock in data, talent, and customer relationships fast.

Veriff has about 500-1000 employees; Signzy has about 200-500. A bigger team usually means more revenue or heavier VC backing, but in AI, small teams can build at massive scale.

Yes — they're direct rivals. Both Veriff and Signzy compete in AI Security, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.