How TigerEye is redefining AI-powered business intelligence

At the Generative AI Summit in Silicon Valley, Ralph Gootee, Co-founder of TigerEye, joined Tim Mitchell, Business Line Lead, Technology at the AI Accelerator Institute, to discuss how AI is transforming business intelligence for go-to-market teams.

In this interview, Ralph shares lessons learned from building two companies and explores how TigerEye is rethinking business intelligence from the ground up with AI, helping organizations unlock reliable, actionable insights without wasting resources on bespoke analytics.

Tim Mitchell: Ralph, it’s a pleasure to have you here. We’re on day two of the Generative AI Summit, part of AI Silicon Valley. You’re a huge part of the industry in Silicon Valley, so it’s amazing to have you join us. TigerEye is here as part of the event. Maybe for folks that aren’t familiar with the brand, you can just give a quick rundown of who you are and what you’re doing.

Ralph: I’m the co-founder of TigerEye – my second company. It’s exciting to be solving some of the problems we had with our first company, PlanGrid, in this one. We sold PlanGrid to Autodesk. I had a really good time building it. But when you’re building a company, you end up having many internal metrics to track, and a lot of things that happen with sales. So, we built a data team.

With TigerEye, we’re using AI to help build that data team for other companies, so they can learn from our past mistakes. We’re helping them build business intelligence that’s meant for go-to-market, so sales, marketing, and finance all together in one package.

Lessons learned from PlanGrid

Tim: What were some of those mistakes that you’re now helping others avoid?

Ralph: The biggest one was using highly skilled resources to build internal analytics, time that could’ve gone into building customer-facing features. We had talented data engineers figuring out sales metrics instead of enhancing our product. That’s a key learning we bring to TigerEye.

What makes TigerEye unique

Tim: If I can describe TigerEye in short as an AI analyst for business intelligence, what’s unique about TigerEye in that space?

Ralph: One of the things that’s unique is we were built from the ground up for AI. Where a lot of other companies are trying to tack on or figure out how they’re going to work with AI, TigerEye was built in generative AI as a world. Rather than relying on text or trying to gather up metrics that could cause hallucination, we actually write SQL from the bottom up. Our platform is built on SQL, so we can give answers that show your math. You can see why the win rate is that, and it will decrease over time.

Why Generative AI Summit matters

Tim: And what’s interesting about this conference for you?

Ralph: The conference brings together both big companies and startups. It’s really nice to have conversations with companies that have more mature data issues, versus startups that are just figuring out how their sales motions work.

The challenges of roadmapping in AI

Tim: You’re the co-founder, but as CTO, in what kind of capacity does the roadmapping cause you headaches? What does that process look like for a solution like this?

Ralph: In the AI world, roadmapping is challenging because it keeps getting so much better so quickly. The only thing you know for sure is you’re going to have a new model drop that really moves things forward. Thankfully for us, we solve what we see as the hardest part of AI, giving 100% accurate answers. We still haven’t seen foundational models do that on their own, but they get much better at writing code.

So the way we’ve taught to write SQL, and how we work with foundational models, both go into the roadmap. Another part is what foundational models we support. Right now, we work with OpenAI, Gemini, and Anthropic. Every time there’s a new model drop, we evaluate it and think about whether we want to bring that in.

Evaluating and choosing models

Tim: How do you choose which model to use?

Ralph: There are two major things. One, we have a full evaluation framework. Since we specialize in sales questions, we’ve seen thousands of sales questions, and we know what the answer should be and how to write the code for them. We run new models through that and see how they look.

The other is speed. Latency really matters; people want instant responses. Sometimes, even within the same vendor, the speed will vary model by model, but that latency is important.

The future of AI-powered business intelligence

Tim: What’s next for you guys? Any AI-powered revelations we can expect?

Ralph: We think AI is going to be solved first in business intelligence in deep vertical sections. It’s hard to imagine AI solving a Shopify company’s challenge and also a supply chain challenge for an enterprise. We’re going deep into verticals to see what new features AI has to understand.

For example, in sales, territory management is a big challenge: splitting up accounts, segmenting business. We’re teaching AI how to optimize territory distribution and have those conversations with our customers. That’s where a lot of our roadmap is right now.

Who’s adopting AI business intelligence?

Tim: With these new products, who are you seeing the biggest wins with?

Ralph: Startups and mid-market have a good risk tolerance for AI products. Enterprises, we can have deep conversations, but it’s a slower process. They’re forming their strategic AI teams but not getting deep into it yet. Startups and mid-market, especially AI companies themselves, are going full-bore.

Tim: And what are the risks or doubts that enterprises might have?

Ralph: Most enterprises have multiple AI teams, and they don’t even know it. It happened out of nowhere. Then they realize they need an AI visionary to lead those teams. The AI visionary is figuring out their job, and the enterprise is going through that process.

The best enterprises focus on delivering more value to their customers with fewer resources. We’re seeing that trend – how do I get my margins up and lower my costs?

Final thoughts

As AI continues to reshape business intelligence, it’s clear that success will come to those who focus on practical, reliable solutions that serve real go-to-market needs. 

TigerEye’s approach, combining AI’s power with transparent, verifiable analytics, offers a glimpse into the future of business intelligence: one where teams spend less time wrestling with data and more time acting on insights. 

As the technology evolves, the companies that go deep into vertical challenges and stay laser-focused on customer value will be the ones leading the charge.