AI citation monitoring for brand stats

What to build

A GEO monitoring dashboard that tracks what ChatGPT, Perplexity, Gemini, and Google AI Overviews say about a brand, flags fabricated or outdated stats being cited as fact, and suggests corrected content to publish so AI engines cite accurate data instead.

LLMs routinely cite fabricated or stale stats about brands as fact, and the tactic of planting fake stats to get cited is spreading, leaving brands and agencies with no visibility into or control over their AI narrative.

Demand

SEO agencies, PR teams, and content marketers want this now because AI answer engines are becoming a primary discovery surface, and a viral HN post just showed how easily fake stats get absorbed and repeated as fact.

• Hacker NewsSocial post

  Publish fake stats, get cited in LLMs — 324 points, 191 comments on HN, showing strong engagement with the underlying tactic and the problem it exposes.

• Peec AIAnalysis

  Existing GEO-monitoring vendor (peec.ai) publishing on this exact tactic indicates a commercial market already forming around brand visibility in LLM answers.

Stack

• OpenAI API
• Perplexity API
• Google Gemini API
• Playwright for scraping AI Overviews
• Postgres
• Next.js

Solo + AI difficulty

Easy to prototype: querying each LLM/API with brand-related prompts and diffing citations against known facts is straightforward with AI-assisted coding. Hard part is AI Overviews lack a public API, so scraping is brittle and rate-limited. MVP in 2-3 weeks covering 3 brands and a handful of query templates.

Entry threshold

Requires scraping or querying multiple LLM/answer-engine outputs on a schedule plus a simple alerting dashboard; a solo builder can ship a working version in a few weeks with AI coding tools, but several funded startups (Profound, Evertune, otterly.ai) already occupy this space.

Window

6-12 months

Where to find first users

• r/SEO and r/marketing
• Product Hunt launch
• SEO agency Slack/Discord communities
• Cold outreach to boutique PR agencies

Competitors

• Peec AI
• Profound
• Otterly.AI

Counter-signals & risks

• No historical match or external corroboration exists yet, so the scale and frequency of fake-stat citation in LLMs is unverified rather than demonstrated.

• Major AI providers are actively improving citation accuracy and grounding, which could shrink the problem before a monitoring tool gains traction.

• Several GEO-monitoring tools (Peec AI, Profound, Otterly.AI) already exist, which could commoditize this capability quickly and compress differentiation for a new entrant.