Brand Hallucination Monitoring Services
AI is making things up about you.
Let’s fix that.
Large language models don’t just summarize—they invent. If ChatGPT says you raised funding you didn’t, if Claude credits your competitor’s founder as your CEO, or if Gemini lists a product you never built, that’s not just an error—it’s a hallucination.
And it’s costing you trust, leads, and reputation.
At LLM.co, our Brand Hallucination Monitoring service helps companies detect and correct AI-generated falsehoods before they damage your credibility. We test what models say about your company, people, and products, then show you how to control the narrative.
LLM.co helps you identify when large language models hallucinate facts about your brand—so you can correct errors, protect reputation, and influence future AI output.
What We Deliver
Brand hallucination happens when an AI confidently outputs false, misleading, or invented information about your company—without any real-world basis.
These errors typically result from weak structured data, ambiguous public signals, or entity collisions—especially when your brand name overlaps with another company or public figure. Unlike SEO errors, which are visible and traceable, hallucinations are buried inside LLM responses and spread invisibly through countless user interactions.
Our service makes them visible, fixable, and preventable.
Model-Wide Fact Testing Suite
We prompt test ChatGPT, Claude, Gemini, Perplexity, and other models using a battery of brand, product, and executive-level queries—capturing outputs in response to bios, summaries, timelines, financials, and org structure.
Hallucination Capture & Logging
We detect and log hallucinated content at both the statement and entity level—highlighting where facts diverge from reality and tagging them by risk severity (e.g., misleading vs reputationally damaging).
Collision & Confusion Analysis
You scale production—across platformsWe identify cases where your brand is being confused with others (especially those with similar names), or where internal data points are being merged with unrelated entities or people., channels, and personas—without sacrificing quality.
Source Attribution Inspection
We reverse-engineer which sources the model may have used (or hallucinated) to fabricate the claim—tracing to outdated bios, press coverage, or incomplete directories.
Correction Strategy Roadmap
We provide step-by-step strategies to correct hallucinations in both current and future model outputs—using structured content, schema, anchor creation, corpus injection, and public data reinforcement.
Ongoing Monitoring
Results will change as AI results change. We help to continuously monitor your brands results in AI search results, ensuring you are aware of brand hallucinations as they arise.
Use Cases for AI Brand Hallucination Monitoring
Brand hallucination risk is highest for:
Venture-Backed or Public Companies
You're already on the radar of investors and analysts—if ChatGPT misquotes your valuation or misstates your CEO, it could influence a deal.
Founders & Executives
If you're being summarized by AI tools, do they have the right backstory, role, and affiliations? Our service helps you own your digital biography across LLMs.
Overlapping Brands
If another company shares your name, domain, or industry keywords, AI may conflate your entities and fabricate hybrid answers.
Rebranded or Acquired Companies
LLMs often cling to outdated brand names, old domains, and legacy descriptions long after you’ve evolved. We help you update what they “remember.”
Agencies & Comms Teams
Whether you’re managing reputation for clients or your own brand, this gives you the LLM layer you’re likely missing in PR and brand monitoring.
How It Works (Our Process)
At LLM.co, we’ve developed a repeatable, outcome-driven process for monitoring LLM brand hallucinations via AI.
1. Discovery & Source of Truth Setup
You share your official company bios, leadership details, product facts, funding history, and any known past misstatements. We use this to define ground truth.
2. LLM Query Execution
We run real-world prompts across public LLMs, designed to simulate how users ask about you. Each output is captured, logged, and versioned by model, date, and query type.
3. Error Identification & Scoring
Our team manually tags hallucinations, rates their risk level, and notes frequency. We highlight which models repeat errors and which ones invent them independently.
4. Strategy & Recommendations
We deliver a full written audit report including screenshots, hallucination logs, severity index, and a step-by-step correction plan—including schema updates, corpus suggestions, and content targets.
Why LLM.co?
We’re not just a monitoring tool—we’re a correction engine.
LLM.co is the only agency fully focused on Large Language Model Optimization (LLMO). Our team combines expertise in prompt engineering, schema development, search behavior, and AI hallucination patterns. We know what causes model errors, and we know how to fix them.
We don’t just audit—we repair, retrain, and reinforce your presence inside AI.
Private LLM Blog
Follow our Agentic AI blog for the latest trends in private LLM set-up & governance
FAQs
Frequently asked questions about our brand hallucination monitoring services
They track media and search—not AI hallucinations. This fills the gap between what’s published and what AI believes.
Yes. We don’t just detect hallucinations—we show you how to suppress and replace them using public data, structured content, and AI-visible publishing tactics.
ChatGPT (with browsing), Claude 3, Gemini 1.5, Perplexity—and other RAG-based or open-source models upon request.
You’ll receive an initial audit within 2 weeks. Full visibility/correction strategies may take 30–90 days depending on content deployment and indexing.