Active.ai vs Pagaya

Neither company has publicly disclosed a valuation, making a definitive size comparison difficult. Active.ai employs 50-200 people, while Pagaya has 500-1000 employees.

Pagaya has raised more in total funding at $600M, compared to Active.ai's $11M — a gap of $589M.

Pagaya holds the higher Awaira Score at 70/100, compared to Active.ai's 50/100. The Awaira Score is a composite metric factoring in valuation, funding, stage, team size, and market presence — a 20-point gap that reflects meaningful differences in scale or traction.

Active.ai was founded by Ravi Shankar in 2016. Pagaya was founded by Gal Krubiner in 2016. Visit each company's profile on Awaira for a full founder biography.

Active.ai: Active.ai builds conversational AI solutions specifically for retail banking and financial services, enabling banks to deploy intelligent virtual assistants for account inquiries, transaction analysis, loan servicing, and customer onboarding through mobile and messaging channels. The platform is designed to integrate with core banking systems and comply with financial services regulations across multiple jurisdictions.\n\nThe company raised approximately $11M in Series A funding and counts regional banks, cooperative financial institutions, and digital neobanks among its customers in India and Southeast Asia. Active.ai's banking-specific NLP models are trained on financial domain terminology, reducing hallucination risk in regulated customer-facing interactions.\n\nThe digital banking transformation in India, accelerated by UPI and the JAM trinity, has created strong demand for AI-assisted banking interfaces that can serve the next 300 million users entering the formal financial system. Active.ai's banking-native design positions it as a credible alternative to expensive custom development or generic chatbot platforms that require extensive financial domain customization. Pagaya: Pagaya operates an AI financial underwriting network that processes consumer loan applications on behalf of lenders, using machine learning models that evaluate creditworthiness across a broader set of data signals than traditional credit bureau scores, enabling lenders to approve more applicants while maintaining or improving default rates. The Tel Aviv and New York company monetises by taking a network fee on loan volume processed through its AI underwriting system, funded by institutional investors who purchase the approved loan pools.\n\nThe company went public on NASDAQ via SPAC merger, having raised over $600 million in combined public and private funding from investors including Oak HC/FT and Viola Growth. Pagaya reports processing hundreds of billions of dollars in loan applications annually across personal loans, auto loans, and mortgage products, with network partners including SoFi, Ally Financial, and US Bank embedded in its origination technology. The business model operates as an AI network sitting between lenders who originate applications and institutional investors who fund approved loans.\n\nPageya competes in the AI credit underwriting market against ZestFinance, Upstart, and traditional credit bureau scoring models from Fair Isaac. Its network model, where multiple lenders access the same AI infrastructure and their collective data improves model performance over time, creates compounding advantages compared to single-lender AI implementations. The company has navigated regulatory scrutiny around AI lending decisions and disparate impact as financial regulators increase oversight of alternative data use in credit decisions.

Both Active.ai and Pagaya were founded in the same year — 2016. Despite sharing a founding year, they may have launched at different times within that year, which can matter in fast-moving AI markets.

Active.ai has approximately 50-200 employees, while Pagaya has approximately 500-1000. A larger team often signals higher revenue or venture backing, but in AI, smaller teams are increasingly capable of building at scale.

Yes, Active.ai and Pagaya are direct competitors — both operate in the AI Finance space and likely target overlapping customer segments. This comparison is especially relevant for buyers evaluating both platforms.