What Is AI Presence Management (AIPM)? A Plain-English Definition
- Patrick Duggan
- 26 minutes ago
- 3 min read
AI Presence Management (AIPM) is the practice of measuring and improving how accurately large language models describe your company, product, or brand when a person asks about it. It covers four things you can actually measure: whether the models are aware you exist, whether they get your facts right, whether they speak about you with positive or negative sentiment, and whether they would recommend you. If SEO was about ranking on a page of blue links, AIPM is about what the machine says out loud when there are no blue links at all.
That distinction matters more every month. People increasingly ask ChatGPT, Claude, Gemini, and Microsoft Copilot a question and act on the answer without ever visiting a website. If those models do not know you exist, or describe you with stale or wrong facts, you have a problem that no amount of traditional search ranking fixes. You are invisible or misrepresented at the exact moment a buyer is deciding.
The four dimensions AIPM measures
Awareness is whether the models can say anything substantive about you at all. A brand-new company scores low here by default — this is the cold-start problem, and it is normal. Accuracy is whether the facts the models recite — founding year, founders, headquarters, flagship product — are correct. Sentiment is the emotional coloring of how they describe you. Recommendation is whether, asked point-blank, a model would steer a buyer toward you or toward a competitor.
We roll the recommendation signal up into a single number we call AIPM-NPS, modeled on the classic Net Promoter Score: promoters minus detractors, on a scale from minus 100 to plus 100. A negative AIPM-NPS means the models either do not know you well enough to vouch for you or actively point elsewhere. It is the most honest gut-punch in the whole report, and most companies have never seen theirs.
Why structured data is the lever, not the goal
Here is the part nobody selling AI hype will tell you: the single most effective way to improve how a model describes you is not to publish more marketing copy. It is to make your own website machine-legible. Schema.org structured data, an llms.txt file, a clean robots.txt that actually welcomes the AI crawlers, semantic HTML — these are the format the models use to ground themselves in who you really are. We have measured companies with hundred-million-dollar marketing budgets scoring five out of ninety-five on structured data while a two-person startup scores eighty-five. Machine-legibility is a weekend of work. Awareness is years. The leverage is wildly asymmetric, and almost everyone ignores it.
How AIPM is measured at DugganUSA
Our AIPMSEC auditor at aipmsec.com runs a five-model council — GPT-4o, Claude, Gemini, Mistral, and DeepSeek — and asks each one what it actually knows about a given company. It checks those answers against the verifiable facts, scores awareness, accuracy, sentiment, and recommendation per model, and then separately scans the company's own website for the machine-readability signals above. Every score is capped at 95, on principle: we guarantee that five percent of any confident-sounding number is nonsense, including our own.
We even publish our own failing marks. Microsoft's Bing AI Performance report says Copilot has cited our main site eleven times, ever — proof that we run the test on ourselves before we run it on anyone else. You cannot manage what you refuse to measure. That sentence is the entire discipline of AI Presence Management, and the field is so new that simply looking puts you ahead of almost everyone.
How do AI models see YOUR brand?
AIPM has audited 250+ domains. 15 seconds. Free while still in beta.