Previse vs Tookitaki

Neither company has publicly disclosed a valuation, making a definitive size comparison difficult. Previse employs 1-50 people, while Tookitaki has 100-500 employees.

Tookitaki has raised more in total funding at $20M, compared to Previse's $18M — a gap of $2M.

Tookitaki holds the higher Awaira Score at 45/100, compared to Previse's 40/100. The Awaira Score is a composite metric factoring in valuation, funding, stage, team size, and market presence — a 5-point gap that reflects meaningful differences in scale or traction.

Previse was founded by Paul Christensen in 2016. Tookitaki was founded by Abhishek Chatterjee in 2014. Visit each company's profile on Awaira for a full founder biography.

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. Tookitaki: Tookitaki builds AI anti-money laundering and financial crime compliance technology for banks and financial institutions, providing the Anti-Money Laundering Suite that uses machine learning to improve transaction monitoring accuracy, reduce false positive alert rates, and enhance suspicious activity detection across financial transaction data. The Singapore company also operates the Typology Repository, a community knowledge base of money laundering typologies that informs its ML model training.\n\nThe company raised approximately $20 million in venture funding from investors including Illuminate Financial, Jungle Ventures, and SBI Investment. Tookitaki serves regulated financial institutions in Asia-Pacific and the Middle East that face increasing AML regulatory pressure and high false positive rates from traditional rule-based transaction monitoring systems that generate large volumes of alerts requiring manual review. Reducing false positive rates is the primary commercial value proposition, as financial institutions spend billions annually on compliance analyst time reviewing unproductive alerts.\n\nTookitaki competes in the AML technology market against NICE Actimize, SAS AML, and Oracle Financial Services, as well as newer AI-native AML vendors including Quantexa and Featurespace. Its community typology repository approach differentiates it by incorporating human expert knowledge about money laundering methods into its ML training process, rather than relying exclusively on historical transaction labels that may miss novel laundering patterns. The Singapore base gives Tookitaki access to the concentrated regional banking hub and the MAS regulatory framework that supports fintech innovation.

Tookitaki was founded first in 2014, giving it 2 years of additional market experience. Previse was founded later in 2016. In AI, even a year or two of head start can translate into significantly more training data, customer relationships, and institutional knowledge.

Previse has approximately 1-50 employees, while Tookitaki has approximately 100-500. A larger team often signals higher revenue or venture backing, but in AI, smaller teams are increasingly capable of building at scale.

Yes, Previse and Tookitaki 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.