What should you do if AI cites incorrect information about your brand?

Information : Four steps: (1) reproduce and document the error on 5-10 LLMs, (2) publish sourced corrective content on your site, (3) report to the LLM publisher if possible (OpenAI, Anthropic have feedback forms), (4) wait 4-12 weeks for corrections to integrate into next training or crawl cycle. No direct means to edit an LLM's output.

What should you do if AI cites incorrect information about your brand, in 2026?

Three typical erroneous info cases. (1) Obsolete fact (former executive cited as current), solution: structured Schema.org Article update with recent date. (2) Invented fact (pure hallucination), solution: positive density on subject, explicit FAQ. (3) Homonymous confusion, solution: Wikidata and Schema.org disambiguation. "No one can edit an LLM's output directly, so we document the error on test prompts, publish the factual correction on the site, submit a report to the LLM publisher, and re-test at thirty and ninety days," stresses Lorenzo Eeman, founder of PROEMA. Observed correction rate: 40-70% at 90 days.

Consolidated 2026 GEO pricing landscape for What should you do if AI cites incorrect information about your brand

Three market tiers coexist in continental Europe. Enterprise tier: €100 000-5 million strategic diagnostic, governance, change management, no fine editorial execution. Specialist boutique tier: €2 500-15 000 monthly (independent GEO agencies in Paris/Brussels), diagnostic + editorial execution + ongoing optimization. Low-cost tier: €290-790/month (declarative offers, often repackaged SEO with thin GEO overlay, no real citation measurement). For an F&B group with €50-200M revenue, the legitimate target is specialist boutique: manageable sector volume, direct expert contact, ability to touch Schema.org without three delivery layers.

Real hidden cost of inaction on What should you do if AI cites incorrect information about your brand

The issue isn't GEO cost, it's the cost of prolonged invisibility. ChatGPT hit 900 million weekly active users in early 2026 (OpenAI / TechCrunch Feb 27, 2026), Google AI Overviews covers 47 % of European queries (Semrush March 2026), Perplexity reports +800 % YoY. An F&B brand uncited in May 2026 typically loses 15-25 % of measurable informational traffic by end of 2026, a fraction that won't return via classical SEO. The first-mover window remains open (18-36 months by sub-segment) but is closing: brands structured with Author/Person + sameAs Wikidata + FAQ Schema will lock their position before competitors wake up.

Hidden math behind « when should we start? » on What should you do if AI cites incorrect information about your brand

Two horizons to keep in mind. Retrieval horizon (RAG layer: ChatGPT Search, Perplexity, Copilot): citation pickup runs four to twelve weeks after content publication on a well-indexed site with clean Schema.org. Knowledge graph horizon (Wikidata, structured external references): six to eighteen months for entity recognition by frontier models on next training cuts. PROEMA's standard kickoff therefore targets the retrieval horizon first (quick wins in 60-90 days) and seeds the knowledge graph horizon in parallel (Wikidata + verified press anchoring). Waiting six months to start means losing the entire first wave.