RankBee — Visibility tooling for ChatGPT, Gemini, content quality, and generative search

Working directly with the founder on product design for RankBee, an AI visibility platform helping brands understand how they appear inside ChatGPT, Claude, and Google AI Overviews.

The work spans dashboards, prompt tracking, competitor monitoring, sentiment analysis, and content optimization — all inside a category that barely existed a year ago. A mix of strategic bets, rapid iteration, and constant adaptation as the team explores what "SEO" means in a world increasingly shaped by AI-generated answers.

RankBee platform interface
RankBee platform interface
RankBee attribute analysis view
RankBee platform interface
RankBee platform interface
RankBee platform interface
RankBee platform interface
RankBee prompt deep dive view
RankBee platform interface
RankBee platform interface
RankBee platform interface
RankBee platform interface

The Category

AI visibility is the idea that when someone asks ChatGPT or Google's AI Overview for a recommendation, the answer your brand receives matters as much as search ranking once did. It's a new surface for brand performance — and it behaves nothing like traditional SEO.

The models have their own opinions, change unpredictably, and operate without a clear optimization playbook. RankBee exists because brands needed a way to understand how AI systems were representing them across an increasingly fragmented search landscape.

The Company

Founded in London in 2024. Angel-backed with a team of 5 people.

How We Worked

The founder brought deep expertise in the AI visibility space and strong opinions about where the product needed to go. My role was translating that domain knowledge into product decisions — how information should be structured, and which interactions would make the data understandable and actionable.

Some concepts moved directly into production. Others served as directional explorations, and some were abandoned entirely as the market shifted and the category evolved. The process was intentionally iterative, shaped by a rapidly changing space where both the product and the problem definition were still emerging.

The Hard Part

The core challenge was presenting data that most marketers have never encountered before: brand sentiment across multiple AI models, prompt-level performance, content quality, and competitive positioning in a world where "ranking" no longer means a fixed position on a search results page.