Active.ai vs Previse

Neither company has publicly disclosed a valuation, making a definitive size comparison difficult. Active.ai employs 50-200 people, while Previse has 1-50 employees.

Previse has raised more in total funding at $18M, compared to Active.ai's $11M — a gap of $7M.

Active.ai holds the higher Awaira Score at 50/100, compared to Previse's 40/100. The Awaira Score is a composite metric factoring in valuation, funding, stage, team size, and market presence — a 10-point gap that reflects meaningful differences in scale or traction.

Active.ai was founded by Ravi Shankar in 2016. Previse was founded by Paul Christensen 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. Previse: Previse builds AI systems that enable large enterprise buyers to offer instant payment to their suppliers, using machine learning models that predict invoice approval probability in real time and allow financial institutions to fund approved invoices immediately. The London company addresses the working capital problem for small suppliers in large enterprise supply chains, where payment terms of 60 to 120 days create cash flow constraints that disproportionately affect smaller vendors.\n\nThe company raised approximately $18 million in venture funding and has partnered with global banks and financial institutions to deploy its instant payment infrastructure within existing accounts payable workflows. Previse technology sits between the buyer ERP system and the bank payment infrastructure, enabling approved-probability scoring that allows funders to advance payment on invoices before formal buyer approval while managing default risk at the portfolio level.\n\nPrevise competes in the supply chain finance and accounts payable automation market alongside Taulia, C2FO, and Greensill (now in wind-down), as well as bank-operated reverse factoring programs. The instant payment use case is differentiated from traditional reverse factoring by requiring no buyer enrollment or confirmation step, reducing friction for both suppliers seeking early payment and buyers whose procurement processes are not designed to accelerate payment approval. The company addresses a global trade finance gap estimated at $5 trillion, with small suppliers in enterprise supply chains representing the segment most underserved by traditional trade finance products.

Both Active.ai and Previse 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 Previse has approximately 1-50. 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 Previse 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.