James O'Brien, Co-Founder & COO of Ducky: $2.7 Million Raised to Build Internal Data Search for LLMs

Ducky AI operates as a pre-LLM compute layer, enabling businesses to make their proprietary data accessible to large language models without compromising security or privacy. In this episode of Category Visionaries, James O'Brien shares the journey of pivoting from a customer support solution to becoming a machine learning infrastructure tool that helps developers find and transform internal business data for optimal LLM consumption. With $2.7 million in funding, Ducky is positioning itself as the essential bridge between enterprise data and AI systems, making advanced AI capabilities accessible to technical teams without requiring extensive ML expertise.

 

Topics Discussed:

• Ducky's evolution from a customer support solution to a developer-focused ML infrastructure tool
• The validation process that led to identifying knowledge accessibility as a core market problem
• How and why the team executed their pivot in just three months
• The challenges of defining an ideal customer profile in the rapidly expanding AI space
• Building a go-to-market strategy in Nashville's emerging tech ecosystem
• Fundraising lessons learned during the SVB collapse

 

GTM Lessons For B2B Founders:

1. Listen when developers ask for your infrastructure: James discovered their true product-market fit when developers started requesting access to Ducky's knowledge retrieval infrastructure rather than their customer-facing application. "We had a revelatory moment where we realized that a bunch of developers had asked for access to our infrastructure, our knowledge retrieval infrastructure. And that's kind of what we're good at." This insight led to their pivot toward becoming an API-first tool that matched their technical strengths with the right audience.
2. Validation is a superpower: The Ducky team excels at gathering unbiased feedback from potential customers. When considering their pivot, they embraced this strength: "I think one of the things that we're best at as a team is validation. I think we're really good at drawing relatively unbiased... input and feedback from people that we're interviewing or talking to." For B2B founders, this emphasis on rigorous customer validation before building can be the difference between success and wasted engineering resources.
3. Make pivot decisions with data, not emotion: When considering a change in direction, Ducky time-boxed their exploration to three weeks, built multiple prototypes, and showed them to potential customers. "It was pretty clear after three weeks that one was not only a better use of our skills and time, but also a better market fit." B2B founders should approach pivots methodically, setting clear timelines and success criteria for validation.
4. Design pricing that aligns with value creation: James emphasizes usage-based pricing that fundamentally connects revenue to customer value: "If you use it and it works, you will use it more. And that means that we're doing our job. And that's awesome. That's all I ever want to do, quite frankly, is get paid for actually bringing value to people." This approach creates natural incentives for both the vendor and customer, unlike the multi-year contracts that often create misaligned incentives.
5. Look beyond AI hype to focus on business problems: James discovered that many companies have been tasked to "do something with AI" without clear objectives. "People are like, 'hey, we got to do something with AI,' but we don't know what that is. And then they think so deeply about, 'hey, how are we going to construct this?'" B2B founders should help customers cut through the hype by focusing on the underlying business value and specific problems to solve, rather than getting lost in technical details.

 

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