Citation & Source Influence
How AI updates its description of you, and why a wrong one is worse than no mention
Most AEO advice is about whether AI cites you. The harder problem is what it says you are once it does, and a confident wrong description is worse than no mention because it sets an expectation your signup or sales call can't meet. The description is assembled at retrieval time from the sources each engine trusts, so it differs by engine, and when your canonical page is ambiguous the model fills the gap from adjacent products. A fix doesn't flip the answer instantly: engines that search climb as they re-fetch, Google's AI surfaces follow its index cadence, and answers from model memory change only when the model does. The remedy is a Measure → Diagnose → Execute → Verify loop, judged over weeks, not a single run.
By Eason Wang, Co-Founder & CPO, SolCrys
Published · Updated
Questions this guide answers
- Why does AI describe my brand wrong even when it cites me?
- Does AI describe my brand differently on each engine?
- How long does it take to fix a wrong AI citation or description?
Direct answer
AI doesn't just decide whether to cite you. It decides what you are, it describes that differently on each engine, and after you fix it the description updates on each engine's own clock, not instantly. A confident wrong description is worse than no mention, because it sets an expectation your signup or sales call can't meet.
The fix is a loop: read the description per engine (not just whether you appear), find which source carries the wrong version, make the correct claim the easiest thing to extract on a page you own, and give the correction weeks rather than days to propagate.
Why a wrong description is worse than no mention
No mention is a missed opportunity. A confident wrong description is an active mis-sale: the buyer arrives expecting a product, price, or integration you don't have, and you rarely see why the lead went cold. The 2026 evidence raises the stakes:
- Buyers notice. In G2's April 2026 survey of 1,076 B2B software buyers, 64% said they often or very often get inaccurate recommendations from AI chatbots, even as 51% now start their research in a chatbot more often than in Google.
- Answers outrun their own sources. A study of Google AI Overviews by Xu, Iqbal, and Montgomery found 11.0% of 98,020 claims unsupported by the pages cited for them, with omission the most common failure.
- Courts are starting to treat the summary as the engine's own words. In May 2026 the Regional Court of Munich held that Google's AI Overviews are Google's own statements and that Google is liable for false ones. The ruling is not final.
The description is assembled, not remembered
When an engine searches the web to answer, it doesn't recall a fixed description of your brand. It reconstructs one at retrieval time from the sources it trusts for that query. When your own canonical page is thin or ambiguous, the model fills the gap from adjacent products and third-party blurbs, and lands somewhere directionally close but wrong on the specifics.
That is why the fix starts on your own site, even though on most engines your pages are a minority of what gets cited. Your owned pages are the canonical the model reconciles everything else against (see owned, earned, and community sources). When the canonical is unambiguous, third-party mentions inherit an accurate version; when it's vague, every mention inherits the ambiguity.
The canonical matters even more on ChatGPT now. After ChatGPT all but stopped citing Reddit in August 2026, the share it dropped went mostly to vendors' official sites, documentation, and government pages in our tracking, so an ambiguous sentence on your own page travels more directly into its answers.
The same brand, described differently by each engine
How AI describes you is per-engine, because each engine assembles the description from a different source mix. Even Google Search, AI Overviews, and Gemini retrieve substantially different sources, with average Jaccard similarity below 0.2 (Grossman et al., SIGIR 2026).
Illustrative scenario only. A cloud data-warehouse vendor, call it Brand W, repositioned in early 2026 from batch analytics to streaming ingestion, but its one-sentence statement of what it does still sits below the fold. Asked "What is Brand W?" on 5 engines over 2 weeks, the answers split like this:
| Engine | How it describes Brand W | What's wrong | Likely source |
|---|---|---|---|
| ChatGPT | A batch warehouse, a smaller alternative to Snowflake | Misses the repositioning | A 2024 comparison post and the vague homepage |
| Google AI Overviews and AI Mode | A streaming platform with a free tier | The free tier was retired | An outdated pricing roundup that still ranks |
| Perplexity | A streaming lakehouse, similar to Databricks | Implies features it lacks | A community comparison thread |
| Gemini | A streaming ingestion platform | Nothing material | Brand W's current docs |
| Claude | A batch data warehouse | Old positioning | A directory listing last updated in 2024 |
Read it per engine, not as one number
Same brand, same prompt: 4 wrong descriptions and 5 sources to fix. A single "are we described well" score would average that away. Read the description per engine and trace each wrong version to the source that carries it.
How the description updates after you fix it
Fixing your canonical page does not flip the description instantly. It propagates as engines re-fetch and re-index, and the shape depends on where the answer comes from.
- Engines that search at answer time (Perplexity, ChatGPT when it searches, Claude with web search) re-fetch and progressively enrich. The description climbs rather than flips: each re-fetch pulls a more accurate passage from your corrected page. Anthropic documents separate agents for this: Claude-SearchBot for search and Claude-User for user-triggered fetches.
- Google AI Overviews and AI Mode inherit Google's Search index and ranking systems, so the description updates on Google's normal recrawl cadence, not a separate AI clock. Google says no special markup is needed; the lever is the visible page.
- Answers drawn from model memory (any engine answering without a web search, including many API calls) reflect training data. They stay wrong until the model itself is updated, on a cadence of months you cannot trigger.
Why a correct fix can look like it failed
A correct fix can look like it failed for the first week, because the engine hasn't re-fetched yet, has pulled only a partially better passage, or answered from memory on that run. Killing the fix after one bad week is a common mistake, and it is the same error as reading a single noisy run as a trend.
How to fix a wrong description (and prove it moved)
This is a Measure → Diagnose → Execute → Verify loop, and Verify is the part that matters most because of the lag.
Measure the description, not just presence
Capture the exact wrong claim on each engine, with the date, the answer text, and the cited source. Whether you appear is the easy half; what the answer says you do decides whether the citation helps or hurts. In SolCrys, Answer Accuracy grades each answer against your Corporate Context and cannot mark a claim wrong without pointing to the evidence.
Diagnose where the wrong version comes from
It is usually your own outdated page, a stale directory or app-store listing, or a third party that described you wrong and got cited. Correcting a page the engine isn't actually drawing on is why so many corrections never land; the step-by-step version is in how to fix a wrong fact in an AI answer.
Execute on the canonical first
Make the correct claim unambiguous and extractable on your own page: one clean sentence on what you actually do, near the top, not buried under hero copy. If a third party carries the wrong version, ask for a correction there too. A person reviews and approves every change before it ships.
Verify over time, against the same frozen prompts
Rerun the same frozen prompt set on the same engines and watch the description climb on engines that search. Do not re-judge after a single run; only the windowed trend is trustworthy. This is the step monitoring-only tools skip.
The honest caveats
Retrieval behavior changes, and the correction cadence varies by engine and domain authority. You cannot force a re-fetch on your schedule; you can only make the right answer the easiest thing to extract and wait out the propagation.
The per-engine table is an illustrative scenario, not client data: the direction generalizes, the specific split won't. The propagation patterns come from how each engine documents its retrieval plus practitioner observation, not a controlled study. The Munich ruling is not final, and nothing here is legal advice. For monitoring and governance, see AI hallucination risk monitoring and is AI telling the truth about your brand?.
Sources
- G2, "The Answer Economy" survey of 1,076 B2B software buyers (April 15, 2026): 64% often or very often get inaccurate AI chatbot recommendations
- Xu, Iqbal, and Montgomery, "Measuring Google AI Overviews: Activation, Source Quality, Claim Fidelity, and Publisher Impact," arXiv 2605.14021 (May 2026)
- The Decoder on the Regional Court of Munich ruling (case 26 O 869/26, May 2026, not final): AI Overviews are Google's own statements
- Grossman et al., "How Generative AI Disrupts Search: An Empirical Study of Google Search, Gemini, and AI Overviews," SIGIR 2026, arXiv 2604.27790
- Search Engine Journal (Matt G. Southern), report on Reddit's ChatGPT citation drop (August 19, 2026)
- Anthropic Help Center: ClaudeBot, Claude-User, and Claude-SearchBot
- Google Search Central, guidance on optimizing for generative AI features in Search (May 2026, updated July 10, 2026)
FAQ
Why does AI describe my brand wrong even when it cites me?
It's a source-of-truth gap. When an engine searches to answer, it assembles the description at retrieval time, and when it can't find one clear statement of what you do on a page you own, it interpolates from adjacent products and third-party blurbs. Being cited doesn't mean being described correctly: a study of Google AI Overviews found 11.0% of claims unsupported by the pages cited for them. Fix the ambiguous canonical first, then the stale third-party source.
Does AI describe my brand the same way on every engine?
No. Each engine assembles the description from a different source mix, so the same brand, on the same prompt in the same window, can be described accurately by one engine and with a retired positioning or price by another. Read the description per engine and trace each wrong version to its source.
I fixed my page but the AI answer didn't change. Why not?
The correction propagates as engines re-fetch and re-index, not instantly. Engines that search at answer time climb over days to weeks, Google AI Overviews and AI Mode follow Google's recrawl cadence, and answers drawn from model memory stay wrong until the model is updated. A correct fix can look like it failed for the first week. Don't kill it early.
How long does it take to fix a wrong AI description?
Days to weeks on engines that search the web, and longer for answers drawn from model memory, which change only when the model is updated. You can't force a re-fetch; make the right answer the easiest thing to extract and judge the change over a windowed trend, not a single run.
Is a wrong AI citation really worse than no citation?
For a brand without strong authority, often yes. A confident wrong description sets an expectation your signup or sales call can't meet, and you rarely see why the lead went cold. No mention is neutral; a wrong one actively mis-sells you. Buyers notice: 64% of B2B software buyers told G2 in April 2026 that they often or very often get inaccurate AI recommendations.
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