Ralph Gootee, CTO and Co-Founder at TigerEye, leads the event of a business simulation platform designed to boost strategic decision-making, planning, and execution. By leveraging advanced time-aware AI technology, TigerEye enables organizations to streamline planning processes, simulate various scenarios, and make data-driven decisions more efficiently.
Founded by Gootee and former PlanGrid executives, TigerEye addresses common challenges in business planning, corresponding to outdated spreadsheets and prolonged planning cycles, with a deal with adaptability and predictable growth. The platform integrates principles from industries like construction and software QA to offer dynamic solutions that help businesses optimize operations and scale effectively.
What inspired you to start out TigerEye, and the way did your previous experiences with PlanGrid influence your vision for the corporate?
I’ve at all times found data to be a challenge. Back once we built my last company, PlanGrid, tools like Looker and Redshift were just coming out. The concept of insights was recent. Mixpanel and Amplitude were still of their early days. These products were so fresh that you just had to construct your individual data engineering team to handle any kind of knowledge insights.
At PlanGrid, we assembled an incredible team with PhDs and talented leaders who did impressive work: identifying hot leads, analyzing customer connections, and calculating ARR. But it surely took a 10-person team, was expensive, and left analysts feeling like ticket crunchers, running SQL queries to reply segmentation and growth questions. After they eventually moved on to guide data science teams elsewhere, the remaining team was often left struggling to make sense of the dashboards they left behind, resulting in significant wasted time. Moreover, our CFO manually verified those numbers to make sure accuracy.
As a board member at other firms, I saw the identical pattern: disconnected dashboards that were hard to piece together into actionable insights. In the course of the Autodesk acquisition of PlanGrid, these challenges became even clearer. Managing two Salesforce environments and coordinating basic back-office tasks like CRM, ERP, and marketing was a struggle. Even determining which campaigns were working was a mystery. These frustrations inspired the vision for TigerEye: a method to make data seamless, actionable, quick and accessible.
TigerEye offers a versatile AI solution for go-to-market teams. What challenges out there did you discover that led you to design a conversational AI for business intelligence?
Go-to-market analytics often feel overwhelming because it is full of numbers, stats, and heavy math. The means of asking creative, investigative questions is clunky. You would possibly create a ticket for the info team, asking for something like a win rate graph. There’s back-and-forth clarification, delays, and sometimes you realize you asked the fallacious query. For most individuals, it’s neither an enjoyable nor a quick process especially for those without the authority of a C-Suite executive to fast-track responses.
Conversational AI changes that. Imagine just saying, “Show me win rates for the West Coast in pink versus the East Coast in brown, over the past 4 quarters, in a bar chart.” A conversation like that takes seconds and so does the output. We designed TigerEye to offer users an intuitive “junior analyst” they will discuss with — at all times available to create insights without the necessity for a clunky interface.
What were essentially the most significant hurdles you faced through the early stages of TigerEye’s development, and the way did you overcome them?
One major surprise was the sheer scale of knowledge we encountered, no matter company size. Even mid-market firms often have vast amounts of knowledge that change often. Existing tools like Looker couldn’t handle these workloads efficiently; we saw load times of 10–12 seconds for a single graph. That’s unacceptable for today’s fast-paced business environment.
To deal with this, we needed to innovate. We integrated DuckDB for faster query execution and selected Flutter for constructing a light-weight, efficient interface. Moreover, we contributed back to the open-source community by developing and maintaining DuckDB.Dart, enabling seamless integration with Dart and Flutter environments. These technologies allowed us to optimize for speed, flexibility and scalability.
As a co-founder, how did you and your team prioritize features and capabilities for TigerEye’s launch?
We began by putting all the company’s resources behind the AI Analyst vision. This meant every front-end and back-end engineer contributed. The character of an AI analyst required a full-company effort since it’s not nearly text output; it’s about providing interactive widgets, configuring simulators, and enabling analysts to take meaningful motion. For instance, one feature lets users configure a future plan so as to add 10 reps to the West Coast seamlessly, which involves designing a highly interactive and intuitive system.
The event process had its ups and downs, however the technical backbone was built on rigorous evaluation. This became the core of our prioritization. Evaluation is where the true work happens. We’re continuously asking, “Did this alteration make the system higher or worse?” We began with our engineering team and our domain experts and eventually evolved to capturing customer inquiries to refine our system further.
We introduced an automatic test suite where the AI evaluates itself and assigns a rating to find out if changes are improvements. To make sure accuracy, we still conduct human evaluations weekly to forestall biases like an LLM giving itself top marks. This dual-layer approach has been crucial to getting TigerEye to a “1.0” state and continually raising the bar.
Finally, achieving domain-specific alignment was a serious focus. Sales and go-to-market operations demand precise, specialized answers, and alignment across stakeholders isn’t at all times straightforward. This is the reason domain expertise and real-world customer feedback were critical in shaping TigerEye into the platform it’s today.
How does TigerEye’s approach differ from traditional BI tools, and what impact has this had on adoption rates amongst businesses?
TigerEye was built from the bottom up with AI and mobile, offering an answer that’s inherently portable and designed to reply questions quickly. Unlike traditional BI tools, that are slow and infrequently require extensive configuration, TigerEye prioritizes speed and ease of use through conversational AI.
Our graphs and widgets are highly flexible, with interactive visuals that allow users to explore data intuitively. The AI doesn’t depend on generic, surface-level information that may result in inaccurate responses; as an alternative, it’s specialized to deliver precise, structured metrics tailored to every business.
Whether for startups, midmarket, or enterprise firms, TigerEye ensures consistency by grounding all calculations in SQL, enabling each front-end and AI-driven queries to deliver the identical reliable numbers. We also provide transparency by showing customers the mathematics behind our evaluation, ensuring they understand exactly how the TigerEye platform arrived at its responses. This commitment to clarity helps construct trust and confidence within the insights delivered.
The result’s an AI platform that delivers strong customizability while empowering teams to access actionable insights independently, allowing data teams to deal with more strategic tasks. This approach has accelerated adoption amongst businesses searching for intuitive, scalable, and precise tools to boost their decision-making.
How does TigerEye leverage AI to adapt and learn from CRM, ERP, and marketing automation changes in real time?
TigerEye uses AI, including Retrieval-Augmented Generation (RAG) and integrations with real-time APIs, to adapt dynamically to changes in CRM, ERP, and marketing automation platforms. We also mix GenAI with more traditional machine learning and simulation theory to offer our AI the power to predict the long run. By connecting directly to those systems, our company repeatedly monitors updates, corresponding to recent customer records, changes in deal stages, or campaign performance metrics, ensuring insights remain current and actionable.
Our AI Analyst doesn’t just passively report data; it learns and evolves with customer workflows. For instance, if a sales team modifies its pipeline structure, TigerEye quickly identifies the changes and adjusts its calculations, forecasts, and proposals accordingly. This real-time adaptability eliminates manual updates and ensures leadership and teams at all times have an accurate, up-to-date view of their go-to-market performance.
Also, TigerEye’s flexibility allows it to work across multiple systems, ensuring seamless integration and alignment. Whether it’s Salesforce, HubSpot, NetSuite, or other platforms, TigerEye’s AI enables teams to chop through complexity, delivering timely, reliable insights that drive smarter, faster decision-making.
With increasing complexity in go-to-market operations, how does TigerEye simplify decision-making for leadership and teams?
Actionable insights through conversational AI. Traditional BI tools often require teams to navigate cumbersome dashboards, wait for data teams to generate reports, or manually piece together metrics across siloed systems. TigerEye eliminates these bottlenecks by providing easy, AI-driven answers tailored to leadership and teams’ needs.
Our AI Analyst functions like a proactive, junior team member, able to responding to questions corresponding to, “What’s my win rate in Q4 across regions?” or “How would adding five reps to the East Coast impact ARR?” The platform delivers insights in seconds without the necessity for data modeling or extensive setup.
By integrating AI with tailored business intelligence, TigerEye ensures that each one metrics are accurate, consistent, and aligned across the organization. Leadership gains clarity on strategic decisions, while teams profit from tools that surface trends, predict outcomes, and reduce the noise of operational complexity. TigerEye helps business leaders make faster, smarter decisions without the heavy lift.
How do you see conversational AI transforming business intelligence over the following five years?
Business intelligence is currently at a crossroads. Many tools remain stuck in an older or acquired state. They’re slow to innovate, lacking recent products, and overly generalist of their approach. These legacy solutions weren’t built from the bottom as much as integrate with large language models or to supply AI interoperability. Most often, they’re attempting to retrofit outdated systems with unproven AI solutions, which isn’t moving the needle.
Conversational AI will drive a brand new breed of specialised BI applications. These tools won’t require teams to spend countless hours customizing and constructing solutions — they’ll be tailored from the outset to deal with specific needs in finance, sales, marketing, construction, oil and gas, and other industries. Each market is evolving in a different way, and specialization is vital.
Foundational AI models like OpenAI, Anthropic, and Mistral will proceed to handle broad, generic applications, but the long run of BI lies in specialized vertical solutions that address unique problems. Specialized AI tools for BI will replace the present one-size-fits-all approach, enabling businesses to extract insights faster and more accurately. It may possibly deliver precision and actionable insights inside its domain. This shift will redefine BI as we realize it.
After serving as a visiting partner at Y Combinator, how has mentoring startups influenced your leadership style or approach to innovation?
YC taught me the importance of prioritizing people. I learned to focus my energy on founders who were hungry, open to feedback, and relentlessly tenacious. Those traits — grit and adaptableness — are hallmarks of successful teams, and I’ve carried that into TigerEye.
One other lesson was recognizing the worth of diversity, each in thought and background. At YC, I saw firsthand how founders from underrepresented groups often brought incredible resilience and creativity to the table. It’s a perspective that’s shaped how we construct and lead at TigerEye today. Diversity strengthens teams and drives innovation.
What’s your vision for the long run of TigerEye, and the way do you propose to expand its impact across industries?
TigerEye is at the start an AI company. Our goal is to bring the innovations we see in consumer AI, just like the seamless interaction in tools like Perplexity and Cursor, into the enterprise. Imagine a private assistant you can ask for insights anywhere, on any device. Must know why deals stalled in Q2 or what could be required so that you can double your sales headcount in a certain region when you’re on the move? You ask, and it’s there immediately, accurate and consistent across the corporate.
The longer term of TigerEye is about simplifying access to data and making insights ubiquitous, whether you’re using a mobile app, wearing a smartwatch, or asking for a report in Slack. We’re focused on creating tools that make data-driven decision-making effortless.
