Auquan is revolutionizing financial services by automating the labor-intensive, data-heavy workflows that consume valuable time for investment professionals. With $8 million in funding, Auquan is building an AI-powered intelligence platform that transforms how financial firms extract insights from unstructured data. In this episode of Category Visionaries, we spoke with Chandini Jain about her journey from running arbitrage trading strategies at a Chicago trading firm to founding a company that's reinventing financial workflows through AI.

Topics Discussed:

The data overload problem in financial services that led to Auquan's creation
Auquan's pivot from serving early adopters to developing a more scalable SaaS model
The evolution of AI adoption in financial services from skepticism to cautious optimism
The distinction between "shallow work" and "deep work" AI tools
Why financial services has seen little innovation since Bloomberg and how AI is changing that
The need for AI tools that understand domain-specific nuances for specialized knowledge workers

GTM Lessons For B2B Founders:

Focus on co-development with early customers: Chandini emphasized the importance of working closely with early customers to shape the product. Rather than rushing to sign many customers initially, they focused intensely on one customer: "Customer one. Will you help us co-develop this product? We will give it to you for very cheap. But in exchange, we want lots of product feedback, we want access to all the users, we want access to your data." This approach helped them build a product truly aligned with market needs.
Pivot when you find problem-market fit but not product-market fit: Auquan initially signed deals with major asset managers but hit a wall after customer #3. Chandini recognized they had "problem market fit" but not product-market fit: "There was a problem that the market was facing that needed to be solved. Just the way we were solving it was not going to work." This realization led to a substantial pivot that eventually unlocked growth.
Create new roles to bridge the technology-adoption gap: Auquan created "outcome engineers" to ensure successful implementation of their AI solution. These specialists "take an underlying product and then they sit with the customer and they make sure that product is able to deliver outcomes exactly in the manner that the customer wants." This approach accelerates time-to-value and helps educate users about AI capabilities, addressing a major barrier to adoption.
Differentiate between horizontal and vertical AI applications: Chandini articulated a clear framework distinguishing "shallow work" (horizontal) from "deep work" (vertical) AI tools. B2B founders should recognize that "if you try to use a shallow work tool to achieve a deep work outcome... your time to value is going to be so large because you will require so much setup and training and customization." Understanding where your AI solution fits in this framework helps position it correctly in the market.
Raise funding from investors who share your vision of the problem: When fundraising, Chandini found success by identifying investors who already understood the problem space: "It is so much easier to raise money if you find the people who see the world as you see it." This alignment allows conversations to focus on your specific approach and execution plan rather than convincing investors the problem exists in the first place.

Sponsors:

Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership.

www.FrontLines.io

The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe.

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