Singapore-based Pints AI has raised US$5.6 million in a pre-Series A round to help regulated financial institutions deploy auditable artificial intelligence for core business functions. The funding addresses a critical barrier in the sector, allowing banks and insurers to move AI beyond experimental pilots and into traceable, production-ready systems.
Regional venture capital firm Tin Men Capital led the round, with SBI Ven Capital co-leading alongside SEEDS, NTUitive, SUTD Venture Fund and Tenity. Pints AI will direct the capital toward growing its engineering team, building new regulatory capabilities, and developing Autothought Studio. This suite of tools allows institutions to build and manage AI applications internally, a key focus for teams working on AI for Product Development within highly regulated environments.
Moving beyond edge applications
The startup reports that 12 financial institutions across four countries have saved over US$10 million in under two years using its platform, Autothought. The system connects directly to core banking and insurance infrastructure to automate underwriting, claims processing and onboarding. Crucially, it generates a complete audit trail for every AI-assisted decision.
Partha Rao, CEO and co-founder of Pints AI, said the company was built to address the industry's core adoption barrier. "Most AI deployments in financial services today live at the edges - marketing, chatbots and customer calls," Rao said. "The institutions that wire it into their core processes, such as underwriting, credit decisioning, claims and compliance, will pull ahead in ways others will not catch."
Orchestrating models for compliance
AI initiatives in regulated sectors often stall because decisions must remain explainable and compliant. Pints AI solves this through an agent orchestration framework that assigns each task to the most suitable model. This ranges from purpose-built small language models to frontier models such as Claude or open-source alternatives, aiming for enterprise-grade accuracy at a lower cost.
The company also embeds engineers directly within client organizations to integrate the software with existing systems. This approach helps institutions transition from concept to production on a single platform rather than juggling disconnected tools. The resulting outputs meet the audit requirements of regulators like the Monetary Authority of Singapore, the Reserve Bank of India and the Hong Kong Monetary Authority. Uncertain outputs are automatically flagged for human review, a necessary safeguard for AI for Finance operations.
Why this matters for product developers
Product developers building enterprise software must prioritize traceability from day one. The Pints AI model demonstrates that successful adoption requires matching specific tasks to the right model architecture while embedding human review loops for uncertain outputs. Teams that design systems with built-in audit trails and regulatory compliance as core features will secure faster deployment cycles in regulated industries.
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