# GEEKY AIQ

Case study by Sumaia Aktar · https://sumaia.xyz · 2026-03-01
Measuring a brand's visibility inside AI assistants

> An answer-engine optimisation platform: track the prompts people ask AI assistants, measure how often a brand is cited against competitors, and report on it over time.

- Period: 2026
- Role: Backend and integration engineer
- Stack: Laravel · Vue.js · MySQL · Redis · OpenAI · Tailwind
- Live: https://growthcrm.devcoreio.com

## The problem

Search engine optimisation has thirty years of tooling. Answer-engine optimisation, meaning how often ChatGPT, Gemini or Claude mention your product when someone asks a question in your category, has almost none. GEEKY AIQ gives a marketing team that measurement: which prompts matter, who gets cited, and how the share moves week to week.

## How it works

A customer defines the prompts and competitors that matter to them. On a schedule, the platform runs those prompts across models, extracts citations and mentions, and stores the results as a time series. Dashboards show citation share against competitors, and reports highlight what changed since the last scan.

## The interesting part

Running the same prompt across several providers on a schedule is exactly the failover, usage-accounting and error-classification problem I worked on inside [laravel/ai](/work/laravel-ai). One provider rate-limits, another runs out of credit, a third returns a different usage object. Building the scanner on those patterns is what made it tractable for a small team; the SDK work and the product work fed each other.