Inspiration
The idea for Anchor came from a conversation with one of my mentors, Will Rush, who recently exited his startup to found another one. He shared how draining the process was—not just operationally, but emotionally. It wasn’t just about finding buyers; it was about knowing when to exit, how to package the company, and whether the decision was right. That conversation stuck with me. I realized there’s a gap in tools that help founders navigate exits, especially in the early-stage and micro-acquisition space. Anchor was built to bridge that gap.
What it does
Anchor helps startup founders explore strategic exit opportunities by generating AI-powered acquisition bundles that private equity firms might be interested in. Founders enter key details like industry, ARR, and team size, and the platform returns 2–3 curated bundles that include similarly profiled startups. Each bundle includes individual valuations, estimated ROI, and a short rationale for why the group has synergy—giving both founders and PE firms a solid starting point for acquisition conversations.
How we built it
We used Next.js and TypeScript for the full-stack web app. The frontend is styled with Tailwind CSS, with components from Radix UI and icons from Lucide React. For data visualization, we used Recharts. AI functionality—like bundle generation and synergy descriptions— was implemented through the Gemini API. Everything was built and deployed in Firebase. This was also my first time using HTML, CSS, and integrating an API in code at all.
Challenges we ran into
Firstly implementing and then making the AI useful without being generic was a major hurdle. Tuning the prompts and logic for bundle generation took several iterations. Deployment through Firebase also had issues, especially around routing. And as someone new to web development, getting comfortable with HTML, CSS, and layout was a hard learning curve, but I have learned a lot from the experience.
Accomplishments that we're proud of
We’re proud of creating a working, end-to-end product that doesn’t just showcase LLM capabilities but that creates a new 'blue ocean' space. It feels especially meaningful to have built something inspired by a mentor’s experience—and to have done it while learning the full web stack almost completely from scratch (with some help from AI tools of course :)) .
What we learned
Aside from learning frontend fundamentals and API integration for the first time, we gained insight into how AI can be practically embedded into workflows without overwhelming users. We also deepened our understanding of acquisition mechanics and how bundling startups might help unlock previously untapped PE opportunities.
What's next for Anchor
During the hackathon, one of the judges mentioned that Anchor could be a strong application of the OSINT (Open Source Intelligence) framework. While we didn’t fully grasp the framework when we first came across it, we’re excited to explore how we might build a model based on OSINT principles that can achieve similar results to our current API-based approach. We’re both in the ECB Honors Program (ECE Honors + Business Honors) at UT Austin, and we plan to revisit and revamp Anchor after we take our Algorithms course next year.
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