AI-powered platform that tracks how ChatGPT, Claude, and Gemini see a brand, benchmarks visibility against competitors, and turns findings into prioritized fixes.
- Discipline
- B2B SaaS
- Our role
- Product + Design + Engineering
- Platform
- Web application
- Year
- 2026
01The idea
Making AI visibility understandable.
Our contribution
- Product Strategy
- UX/UI
- Frontend
- Backend
- AI Integration
Search behavior is expanding beyond traditional search engines. Buyers now ask ChatGPT, Claude, and Gemini directly for recommendations, and brands need to know whether those models mention them, describe them accurately, and recommend them over competitors.
The challenge
As AI assistants become a growing entry point for discovery, businesses had no reliable way to see whether these models even knew about them, how they were described, or which competitors got recommended instead. There was no dashboard for 'how does AI see us' the way there is for search rankings.
What we built
GeoSurfaced tests relevant, real-world questions against multiple AI engines and analyzes how a brand appears in the answers — tracking mentions, ranking, sentiment, and the competitors that show up instead. Complex AI answers are turned into a small set of understandable metrics: AI Visibility, Answers Mentioning You, Average Position, Competitors, Questions & Answers, and Recommended Actions.
02The experience
Complex answers. Clear next steps.
The interface is deliberately built like a familiar SaaS analytics tool rather than a research report — a visibility score with history, a competitor comparison view, and a question-and-answer log an agency or marketing lead can read in minutes. Underneath, the platform runs prompts across supported AI engines on a schedule, parses the responses for brand mentions and ranking position, and turns repeated gaps into prioritized, ready-to-implement recommendations.
Product capabilities
The details that make it useful.
- 01AI Visibility score, tracked over time
- 02Answers Mentioning You — mention rate across tested questions
- 03Average Position across AI engines
- 04Competitors — who gets recommended instead, and why
- 05Questions & Answers log across ChatGPT, Claude, and Gemini
- 06Recommended Actions, prioritized and ready to implement
- 07White-label reports for agencies monitoring multiple client brands
03Under the hood
Good design.
Solid foundations.
Full-stack product build: product definition, UX/UI, frontend, backend, and the AI-engine integration layer, end to end.
- React
- Vite
- JavaScript
- Tailwind CSS
- Node.js
- LLM APIs
- 01React frontend
- 02Node.js API
- 03AI providers — ChatGPT, Claude, Gemini
Engineering considerations
- AI engines don't expose a ranking API — visibility has to be measured empirically, by running the same questions repeatedly and parsing free-form answers consistently.
- Answers vary between runs and models, so the scoring layer has to separate a real trend from normal noise before it reaches a recommendation.
- 01
Clear visibility benchmark across ChatGPT, Claude, and Gemini
- 02
Actionable roadmap to improve AI-driven discovery
- 03
Scalable SaaS foundation ready for agency and multi-brand use
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