Voice AI Customer Support for Financial Services
We started Trace because we believe AI should wow customers, not frustrate them. As a result, we prioritize reliability and seamless customer interactions. Trace AI agents resolve calls end-to-end, securely integrating with bank systems and external APIs. So far, our agents handle quoting, application, and transaction tracking calls for our partners — but we’re still searching for new exciting use cases. Question to all fintechs/banks: Which customer requests keep coming back to you? We’d love to learn how you handle them now and if they are ripe for automation.
This company has completed 8 funding rounds. Current round is highlighted.
Total Raised
$65.8M
Top Valuation
-
Trace raised $3M in a Seed Stage Venture round.
High-Level Overview
Trace is a startup that builds voice AI customer support agents specifically for financial services, including banks, fintechs, and insurance companies. Their AI agents go beyond answering generic questions by securely integrating with core systems to perform complex, account-specific actions such as replacing cards, disputing charges, and tracking transactions, all while maintaining human-like conversations. This technology addresses the high operational costs and inconsistent service quality typical of traditional call centers, achieving around 95% end-to-end call resolution with sub-0.2 second response times optimized for voice. Trace’s solution reduces customer frustration by providing instant, 24/7 support that complies with security and privacy policies[1][2][3][4].
Origin Story
Trace was founded by Saner Cakir (CEO), Kayla Lee (COO), and Yash Pansari, all ex-researchers from UC Berkeley with backgrounds in AI and natural language processing. Kayla led foundational NLP research at Berkeley, while Saner developed an AI copilot that assisted cardiothoracic surgeons at UCSF. The idea emerged from recognizing the limitations of existing AI customer support, which could not securely access account-specific data or take actions, leading to poor user experiences in financial services. Early traction includes successful integration with fintech and banking systems and achieving high call resolution rates[1][2][4].
Core Differentiators
Role in the Broader Tech Landscape
Trace rides the growing trend of AI-driven automation in financial customer service, where operational costs are high and customer expectations for instant, personalized support are increasing. The timing is critical as financial institutions seek to modernize legacy call centers, reduce operational expenses (15-40% of opex), and improve customer satisfaction. Market forces such as the rise of fintech, regulatory demands for security, and advances in voice AI technology favor solutions like Trace. By enabling secure, voice-optimized AI agents that can handle complex, multi-step financial tasks, Trace influences the broader ecosystem by setting new standards for automation, security, and user experience in financial services customer support[1][5][6][8][9].
Quick Take & Future Outlook
Trace is well-positioned to expand its footprint as financial institutions increasingly adopt AI for customer support. Future trends shaping its journey include advances in generative AI, growing demand for multilingual and inclusive voice support, and integration with broader financial advisory and fraud detection services. As AI voice agents become more sophisticated, Trace’s influence may evolve from customer support automation to becoming a core interface for financial services, enhancing engagement and operational efficiency. Continued innovation in security and seamless integration will be key to maintaining trust and expanding adoption in this highly regulated sector[1][5][6].
Trace’s mission to revolutionize financial customer support with secure, intelligent voice AI aligns with the broader industry push toward automation and enhanced customer experience, making it a notable player in the fintech AI landscape.