How to change what AI models say about you

When someone wants to know who you are, they increasingly don't open Google and scan ten blue links. They ask ChatGPT, Claude, or Perplexity — and they read the one synthesized answer that comes back. That answer is now your first impression, and unlike your LinkedIn or your homepage, you didn't write it and there's no link to correct it.

Gartner projects a 25% drop in traditional search volume by the end of 2026 as people shift these questions to AI assistants. ChatGPT alone crossed ~900M weekly users in early 2026. The reputation you carefully curate on the pages you own is no longer the one most people see first.

So the question stops being "where do I rank on Google?" and becomes "what do the models actually say when someone asks about me — and how do I change it?"

Why AI gets people wrong

AI models don't look you up live. They answer from patterns baked into their training data, which creates three predictable failure modes:

• Lag. Training cutoffs mean a model can describe a role you left two years ago, miss the company you just founded, or omit your most recent work entirely.
• No canonical source. When the web disagrees about you — an outdated bio here, a stale title there — the model hedges or averages, and you get a vague, watered-down answer instead of a sharp one.
• Hallucinated specifics. Asked for detail it doesn't have, a model will sometimes invent a plausible-sounding title, school, or claim. Confidently.

None of this is malice. It's just statistics over whatever the model absorbed — which is exactly why it's changeable if you understand what moves the needle.

What a reading actually measures

Before you can fix how a model describes you, you need to measure it. A useful reading breaks "how good is my answer" into four concrete dimensions:

• Presence — does your name come up at all, across direct and recommendation-style questions?
• Prominence — when you're mentioned, how early and how central are you?
• Accuracy — does the model cite real specifics (years, titles, companies), or hedge?
• Sentiment — is the framing around your name positive, neutral, or negative?

Treat the resulting score as a signal, not a verdict — but it tells you which of the four is dragging you down, which is what makes the fixes specific instead of generic. (Our full methodology lays out exactly how each is computed.)

The moves that actually work

Improving how AI describes you is really about giving the models a clean, consistent, recent signal to learn from. In rough order of leverage:

1. Lead with your current work everywhere you own. Your site, LinkedIn, GitHub, company about-page. Models weight recent, frequently-repeated facts — so if your three most-visible bios all open with what you do now, that's what gets learned.
2. Publish dated milestones. "Founded X in 2024," "Raised Y in March 2025." Concrete, dated facts give the model specifics to cite instead of hedging.
3. Earn mentions in recent third-party writing. A model trusts a roundup, interview, or article more than your own bio. One credible recent citation often moves the needle more than ten self-published pages.
4. Make your facts agree across sources. Conflicting titles and dates across the web are what produce vague answers. Pick one canonical framing and align your profiles to it.
5. Get structured where it counts. A Wikipedia entry, a well-formed Crunchbase or Wikidata record, schema.org markup on your site — these are high-signal sources models lean on heavily.

What doesn't work

• Keyword stuffing. Models aren't fooled by repetition the way old search algorithms were. It reads as noise.
• One-and-done edits. Training data refreshes over time; a single update won't show up overnight and can fade. This is maintenance, not a one-time fix.
• Ignoring the negative. If the framing around your name skews negative, adding more positive bios rarely outweighs it. You have to address the source.

How to actually track it

The only way to know whether any of this is working is to measure before and after, across multiple models, on a schedule — because each model updates on its own cadence. That's the whole reason IndexMe exists: run a free reading to see what the models say about you today, get a prioritized fix list, and watch the score move as your changes propagate.

See where notable names in your field already land on the Global AI Index, then run a reading on yourself and start closing the gap.