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As of 13 August 2026, AI can only partly track mentions of your brand online.
This still needs a person who signs their name to it.
Can you do it?
15 minutesto a draft.
30 minutesto something you’d act on.
Cost, all in£0
Skill neededpower-user
Who has to check ityou
What the alternative costsThe alternative is manual searching and spreadsheet work; the supplied tool data gives no price for a specialist monitoring service.
If this goes wrong: you miss a complaint, copy a false positive into a report or make a brand decision from incomplete coverage.
What to actually do
Hand it to a person
The route this page recommends
A person who owns the outcome does this end to end, worth it when the failure is dear.
Use a tool built for this
Second choiceDo it yourself
The distant thirdA chat interface, power-user skill, and roughly 30 minutes until you can act on the result.
How to actually do it
- Write down your exact brand name, common misspellings, product names, senior staff names, competitor names and words that should exclude unrelated results.
- Open the online channels and search or listening tools you already use, run those searches for the same period, and export the results with the original text, URL, source and date where available.
- Combine the exports into one spreadsheet, keep a source column for every row, remove only exact duplicate rows, and do not delete ambiguous results.
- Upload the spreadsheet to an AI chat or Polymer and paste the prompt, replacing [BRAND NAME] with your brand name.
- Open the original URL for every item marked as a complaint, legal threat, safety concern, privacy issue or unclear result, and correct the classification in the spreadsheet where necessary.
- Compare the report's themes and examples with the complete spreadsheet and record any channels, search terms or dates that were not included.
- Save the checked report with its coverage limitations, then repeat the same searches and upload process on your chosen schedule so new results are compared consistently.
Prompt
Analyse the attached brand-mention data for [BRAND NAME]. Treat the data as a sample, not as a complete record of everything online. First remove duplicates, then label each item as: genuine mention, false positive, competitor mention, unrelated use of the term, or unclear. Preserve the original URL, date if supplied, source, author or account if supplied, and the exact text needed to support the classification. Separate praise, complaint, question, product feedback, purchase intent and other relevant themes. Flag possible urgent complaints, legal threats, safety concerns and privacy issues without giving legal advice or diagnosing anything. Do not invent missing facts, sentiment, reach, dates or sources. Explain every unclear classification in one short sentence. Produce: 1) a table of classified mentions, 2) a summary of recurring themes, 3) notable changes compared with any earlier file supplied, 4) five representative examples with URLs, and 5) a coverage and limitations section listing which sources, dates and search terms are absent. Finish with a short list of manual checks I should make before sharing the report.
Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.
What it gets wrong
- AI cannot guarantee that it has found every mention across public sites, private groups, closed platforms or changing search indexes.
- It confuses brand names with ordinary words, unrelated businesses and sarcastic or indirect references.
- It cannot reliably infer the importance of a post without context such as the author's influence, customer history or your current business priorities.
- It cannot take responsibility for deciding whether a complaint needs a public reply, escalation or legal review.
- It does not provide continuous monitoring unless a separate system collects and sends it new data.
What caps this at PARTLY: real time truth, verification cost and context depth.
How we scored this
Five axes, each scored nought to two by hand: ten means AI carries the task cleanly, and the thresholds that turn a total into YES, PARTLY or NO are published in the methodology. Each axis name links to its definition.
| Axis | Score (0–2) |
|---|---|
| Output | 1 |
| Inputs | 1 |
| Verification | 1 |
| Liability | 1 |
| Effort delta | 1 |
| Total | 5 / 10 |
The methodology and its thresholds are published in full.
FAQ
- Can ChatGPT monitor my brand mentions?
- Not continuously or comprehensively on its own. It can analyse searches, platform exports and links that you provide, then classify and summarise the mentions while showing where coverage is incomplete.
- Can AI find every mention of my business online?
- No. AI cannot see private groups, closed platforms or every page indexed online, and it can miss indirect references or misunderstand ordinary uses of your brand name. Treat its report as a monitored sample, not proof that no other mentions exist.
- What is the best AI tool for tracking brand mentions?
- Polymer is a practical fit when you can export mention data into a spreadsheet because it can turn business data into dashboards and insights. It does not remove the need for a separate way to collect the online mentions.
- Can AI tell me whether a brand mention is positive or negative?
- Usually, it can make a useful first classification and group recurring themes. Sarcasm, mixed opinions, indirect references and industry context still need you to check the original post before acting on the result.
Nearby answers
Assessed by gpt-5.6-luna (gpt-5.6-luna) on 2026-08-13, second-checked by an independent model. Wrong somewhere? Email [email protected] and it gets re-checked.
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