How to Check What ChatGPT Says About Your Business

How to Check What ChatGPT Says About Your Business
How to Check What ChatGPT Says About Your Business
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How to Check What ChatGPT Says About Your Business

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Why you need to check — not assume

Why you need to check — not assume

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Step 1: Write buyer-intent prompts

Step 1: Write buyer-intent prompts

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Step 2: Run prompts in ChatGPT

Step 2: Run prompts in ChatGPT

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Step 3: Repeat on other platforms

Step 3: Repeat on other platforms

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Step 4: Compare to competitors

Step 4: Compare to competitors

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Step 5: Diagnose why

Step 5: Diagnose why

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Step 6: Map fixes to modules

Step 6: Map fixes to modules

To check what ChatGPT says about your business, run five to ten buyer-intent prompts in your category and city, log which competitors get named and why, then repeat across Gemini and Perplexity. AIrecommend.ai's free scan automates six-platform sampling and maps gaps to Growth Engine fixes.

Why you need to check — not assume

Founders assume AI "probably knows" their business. Then a prospect says, "ChatGPT told me to call someone else."

You need a repeatable audit, not a one-off curiosity query. This guide covers manual methods and the structured scan we built at AIrecommend.ai.

Step 1: Write buyer-intent prompts

Do not ask "Is [Your Business Name] good?" Real customers ask category + intent + geography:

  • "Who's the best emergency plumber in [city]?"
  • "Recommend a dentist for anxious adults near [neighborhood]"
  • "Top-rated HVAC company for same-day AC repair in [metro]"

Write 5–10 prompts per location you serve. Avoid brand-leading questions unless testing reputation repair.

Step 2: Run prompts in ChatGPT

For each prompt:

  1. Open a fresh chat (reduces carryover bias)
  2. Note whether browsing/search is active — record the mode
  3. Copy the full answer
  4. List every business named, in order
  5. Capture stated reasons ("highly rated," "fast response," "popular locally")

Create a simple spreadsheet:

| Prompt | You mentioned? | Competitors named | Reasons cited | Browsing on? | Date |

Step 3: Repeat on other platforms

ChatGPT alone is insufficient. Industry overlap analyses suggest ~11% shared citation domains across major assistants — different engines, different winners.

Minimum additional checks:

  • Gemini — Google ecosystem signals
  • Perplexity — explicit citations
  • Claudegrounding behavior

Or run all six in one pass: free AI visibility scan.

Step 4: Compare to competitors

Pick 3–5 competitors you lose to in real life. Run the same prompt set. Calculate:

If competitors appear on 8/10 prompts and you appear on 1/10, you have a systematic gap — not bad luck.

Deeper framework: AI visibility tracking.

Step 5: Diagnose why

When models name competitors, reasons usually cluster:

Cited reason Likely signal gap
"4.9 stars with 500 reviews" Review velocity / volume
"Highly rated on Google" GBP + review themes
"Known for emergency service" Review text + service labels
"Located in [area]" Service area clarity in listings/schema
Specific wrong facts about you Listing conflict — accuracy repair

Step 6: Map fixes to modules

At AIrecommend.ai, scan results map to Growth Engine modules:

You approve every draft before publishing.

The faster path: automated scan

Manual audits teach intuition. Automation produces shareable baselines.

Our free scan at /#scan:

  1. Enter business category and market
  2. Sample buyer-intent prompts across six platforms (ChatGPT included)
  3. Return mention rate, competitor table, platform blind spots
  4. Map prioritized fixes to modules
  5. Generate a report link for your team

No credit card. Same methodology our paid programs resample monthly.

Related service: ChatGPT optimization.

Common mistakes

Leading prompts — "Why is [Your Name] the best?" trains nothing useful.

Single-session conclusions — one answer is anecdote; ten prompts are data.

Ignoring browsing mode — document settings or results are not reproducible.

ChatGPT-onlyplatform overlap makes multi-engine checks mandatory.

Panic without fix plan — measurement should end in NAP, reviews, entity work — not content spam.

Reviews: the fastest lever after listings

Once NAP is clean, review themes often move mention rates over 60–90 days. Do it ethically: Google reviews the right way — no gating, same link for every customer.

Ongoing monitoring

Cadence Action
Weekly Spot-check 2 prompts if market is hyper-competitive
Monthly Full rescan or dashboard scorecard (paid tiers)
Quarterly Expand prompt library for new services/locations

Paid programs include continuous tracking, Super Pixel attribution on AI traffic, and approval-queue delivery.

Growth $4,997/mo · Dominance $9,999/mopricing.

What checking cannot do

Audits do not control ChatGPT. They inform work on signals models may read. No ethical vendor guarantees placement after a scan.

Next steps

  1. Run 10 manual prompts today — log results
  2. Run the free scan for six-platform baseline
  3. Fix NAP and entity facts this week
  4. Plan review velocity for next 90 days
  5. Resample and compare mention rate trend

Also read: How AI assistants choose businesses · AEO services.

Frequently asked questions

Yes, but single queries are noisy. Use multiple buyer-intent prompts without leading the model toward your name — measure whether you appear when customers ask who to hire.

Test both if your product tier allows browsing. Live retrieval can differ from memory-only answers. Document which mode you used for repeatability.

Monthly at minimum for active markets. Model updates and competitor changes move mention rates quickly.

Trace likely sources — conflicting listings, outdated directories, thin schema — and fix upstream. See our accuracy repair guide.

Manual checks help; structured multi-platform scans prevent single-engine blind spots and produce shareable baselines for your team.

See what AI says about your business

Free six-platform scan · shareable report · ~15 seconds