The Tool Desk
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Report three outcomes separately: a brand mention, a citation to a relevant URL, and a visit from an AI tool. A mention can happen without a citation, and a citation does not guarantee a click. Keeping those outcomes separate prevents an apparently strong visibility score from hiding weak traffic.
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What “AI visibility” actually measures
AI search visibility is not a single ranking position. An engine may name your company, cite one of your pages, recommend a competitor instead, or provide an answer with no source links. Measure the parts independently:
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- Mention: whether the answer names your brand at all.
- Mention position: where your brand first appears in the answer, counted by ranked recommendation, list position or paragraph order.
- Citation: whether the answer links to a brand-owned or otherwise brand-relevant URL.
- Recommendation strength: whether the wording is a strong recommendation, a neutral mention, a qualified option or a warning.
- Share of voice: your brand’s mentions divided by mentions of all named brands in the same answer set.
- Referral click: a session attributed to an AI product in your analytics.
TechRadar describes these as different outcomes and recommends a fixed prompt log rather than relying on one manual query. See its practical method at TechRadar’s guide to tracking visibility in AI search.
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Build a controlled prompt set
Your prompt corpus is the measuring instrument. Use buyer language rather than only branded searches, and keep the wording stable between runs.
Cover the questions people ask before they know your brand
A useful starting set is 10–30 prompts divided among five intents:
- Discovery: “What tools are best for [category or job]?”
- Comparison: “Compare the leading [category] products for a small team.”
- Alternatives: “What are alternatives to [known competitor]?”
- Problem-solving: “How can I solve [specific customer problem], and which products help?”
- Category definition: “What should I look for in a [category]?”
Include some prompts that name your brand and others that do not. Branded prompts test whether the system can describe you when prompted; unbranded prompts test whether it recommends you without being led.
Freeze the variables that can change an answer
For every run, record the exact prompt text, language, locale, account state, model, search or grounding setting and tool version. A change in any of these is a new test condition, not a continuation of the old series. If you edit a prompt, assign it a new prompt_id and retain the previous version.
Choose a cadence and keep the raw evidence
Run the corpus weekly or monthly, using the same schedule for every engine. A single response is noisy because retrieval results and model wording change. Trends across repeated runs are more informative. Store the complete answer, not only a score, so another person can audit why a metric moved.
Use a measurement record that can be audited
A spreadsheet works for a small programme; a database or object store is better once you have many engines, prompts and runs. One row should represent one prompt on one engine in one run.
| Field | What to save |
|---|---|
run_at |
UTC timestamp when the request completed |
engine |
ChatGPT, Gemini, Perplexity or Claude |
model |
Exact model identifier returned or configured |
tool_version |
Search or grounding tool version, when exposed |
prompt_id |
Stable identifier for the frozen question |
prompt_text |
Exact text sent to the provider |
answer_text |
Complete generated answer |
brand_mentioned |
Boolean, with an annotation or reviewer note where possible |
mention_position |
First list or recommendation position; use “not mentioned” when absent |
recommendation_strength |
Your defined category, such as strong, qualified, neutral or negative |
competitors |
All named competing brands |
cited_urls |
Every URL returned as a citation |
cited_domains |
Normalized domains for source-mix analysis |
citation_count |
Number of citation annotations or links |
referral_clicks |
Matched analytics sessions for the run period |
Keep a separate change log for prompt edits, model changes, tool versions, locale, account and search settings. Without it, a genuine product or content change can be confused with a provider update.
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Calculate metrics that answer different questions
Calculate metrics within the same engine and prompt cohort before comparing engines. Retrieval designs differ, so an apparent gap between providers may reflect how they search and cite rather than a difference in your content.
Mention rate
mention rate = runs with a brand mention ÷ eligible runs. Define the eligible set first; for example, include only unbranded discovery prompts if you want an unprompted-recommendation measure.
Citation rate
citation rate = runs citing a brand-owned or brand-relevant URL ÷ eligible runs. Decide in advance which domains count as brand-owned and whether third-party reviews count as relevant.
Position and share of voice
Report the median first-mention position, because a mean can be distorted by an occasional long list. For share of voice, divide your brand’s mentions by all named brands in the same answer set; publish the denominator and prompt cohort with the result.
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Source and recommendation mix
Group cited URLs by domain to show which publishers and first-party pages engines rely on. Publish the distribution of strong, qualified, neutral and negative recommendations rather than collapsing every mention into a positive score.
AI referral sessions
Use your analytics data to count sessions attributed to AI products and compare them with mention and citation rates. This is the traffic outcome; it should not be substituted for visibility itself.
Automate the baseline with a scheduled prompt log
A simple reproducible pattern reads prompts from a controlled sheet or database, sends each prompt to each provider, extracts answer and citation metadata, and writes one record per result. Keep the orchestration layer provider-neutral so a schema change in one API does not rewrite your reporting logic.
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from datetime import datetime, timezone
FIELDS = [
"run_at", "engine", "model", "tool_version", "prompt_id",
"prompt_text", "answer_text", "brand_mentioned", "mention_position",
"recommendation_strength", "competitors", "cited_urls",
"cited_domains", "citation_count", "referral_clicks"
]
def make_record(engine, model, tool_version, prompt_id, prompt_text,
answer_text, cited_urls, brand):
return {
"run_at": datetime.now(timezone.utc).isoformat(),
"engine": engine,
"model": model,
"tool_version": tool_version,
"prompt_id": prompt_id,
"prompt_text": prompt_text,
"answer_text": answer_text,
"brand_mentioned": brand.lower() in answer_text.lower(),
"mention_position": None, # classify from the saved answer
"recommendation_strength": None, # apply your documented rubric
"competitors": [],
"cited_urls": cited_urls,
"cited_domains": [],
"citation_count": len(cited_urls),
"referral_clicks": None
}
# For each frozen prompt, call a provider adapter and persist the record.
# Never overwrite an earlier run; append a new timestamped record.The boolean check is only a first pass. Brand aliases, spelling variants and product names need a maintained dictionary, and a human review sample is useful for checking false positives. Store the original answer so classifications can be corrected without rerunning an old condition.
ChatGPT: capture inline URL citations
OpenAI documents two relevant patterns. Chat Completions search uses gpt-5-search-api; the Responses API can invoke the web_search tool when search should be optional. Responses include inline citations and url_citation annotations containing a URL, title and character offsets. Save both the rendered answer and every annotation.
Minimal Python shape:
from openai import OpenAI
client = OpenAI()
resp = client.responses.create(
model="gpt-5-search-api",
tools=[{"type": "web_search"}],
input="Which brands are recommended for [category]? Include sources."
)
answer = resp.output_text
# Persist answer and each URL citation annotation from the response output.Implementation details and current request structures are documented in OpenAI’s web-search API guide. OpenAI’s help documentation warns that search results and citations can be incomplete, outdated or incorrect. Open every cited page and check its publication or update date before treating it as evidence: OpenAI’s ChatGPT search guidance.
Gemini: save grounding queries and citation spans
Enable Gemini’s google_search tool. The API can return executed queries, search-result steps and inline url_citation annotations with start and end indexes. Preserve the query list and map each cited text span to its URL; the query trail explains why a source appeared.
from google import genai
client = genai.Client()
interaction = client.interactions.create(
model="gemini-3.8-flash",
input="Which brands are recommended for [category]? Include sources.",
tools=[{"type": "google_search"}],
)
print(interaction.output_text)
# Save interaction.steps and every url_citation annotation.Follow the current request and response details in Gemini’s grounding with Google Search documentation. The current documentation says Gemini 3+ grounding includes 5,000 free search requests per month and then costs $14 per 1,000 queries. Treat those figures as date-sensitive and verify billing before budgeting or publishing a cost estimate.
Perplexity: record the complete Sonar response
Use the current Sonar API or another officially supported search endpoint. Save the complete answer, every citation, model, prompt and timestamp. Because response schemas and pricing change, inspect the current Perplexity documentation before choosing field names; do not hard-code a citation parser from an old example.
TechRadar Pro reported a Sonar search fee of roughly $5–$14 per 1,000 search requests in 2026, in addition to token charges. That is a dated planning figure, not a permanent price. Verify the provider’s current rates before running a large schedule. The implementation and cost context are covered in TechRadar Pro’s visibility guide.
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Claude: preserve web-search result locations
Anthropic’s web-search tool gives Claude current web content and cited sources. The documented result locations include url, title, encrypted_index and up to 150 characters of cited_text. Save those fields with the generated answer, along with the tool version.
import anthropic
client = anthropic.Anthropic()
message = client.messages.create(
model="claude-opus-5-5",
max_tokens=4096,
messages=[{
"role": "user",
"content": "Which brands are recommended for [category]? Include sources."
}],
tools=[{"type": "web_search_20260318", "name": "web_search"}],
)
# Persist text blocks and web_search_result_location citations.Anthropic lists these tool versions: web_search_20250305, web_search_20260209 with dynamic filtering, and web_search_20260318 with response inclusion. Record the exact version used in each run. See the Claude web-search tool documentation.
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Use the same prompt cohort and report the conditions alongside every result. A useful comparison table contains:
| Axis | What to compare |
|---|---|
| Retrieval trigger | Whether search or grounding was enabled and when it executed |
| Citation fields | URL, title, offsets, query steps or cited text exposed by the provider |
| Source mix | First-party pages, editorial sources, directories and other domains |
| Stability | Variation in mentions, positions and citations across repeated runs |
| Latency | Elapsed request time under the same schedule and network conditions |
| Cost | Current search and token charges for your actual request volume |
| Localization | Language, country, account state and regional search differences |
| Referral behaviour | AI-attributed sessions and landing pages in your analytics |
Do not declare one engine universally better from one sample. Compare each engine with its own prior runs first, then compare equivalent cohorts.
Use Google’s first-party measurement where it applies
Google Search Central says crawlable, unique and helpful content remains foundational for generative AI search. Search Console includes a Generative AI performance report for Google’s own generative Search surfaces. Use that report for Google Search visibility, and use controlled API prompt runs to study answer-engine behaviour in ChatGPT, Gemini, Perplexity and Claude.
Google explicitly says: “You don’t need to create new machine readable files, AI text files, markup, or Markdown to appear in Google Search (including its generative AI capabilities).” It also says Google Search ignores llms.txt; do not treat that file as a proven Google ranking requirement. The full guidance is at Google Search Central’s AI features documentation.
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Audit answers before publishing a trend
Automated parsing makes the schedule scalable, but it cannot reliably judge every recommendation. For a review sample from each run:
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- Open every cited URL and confirm that it exists, is relevant and supports the nearby claim.
- Check the page’s publication or update date, especially when the answer makes a current-pricing or compatibility claim.
- Verify that the detected brand mention refers to your company rather than a similarly named product.
- Check the recommendation category against the answer’s actual wording, including qualifications and warnings.
- Confirm that competitor names and citation domains were captured completely.
Keep representative raw answers and linked citations in the report. Readers and stakeholders should be able to trace a percentage back to the exact response that produced it.
Common failure modes and fixes
A score jumps after a model or tool update
Inspect the change log before attributing the movement to content. Split the chart at the model or tool-version change and label the two series separately.
The brand appears often but receives no traffic
Separate mention rate from citation rate and referral sessions. Review whether mentions are buried, unlinked, qualified or attached to pages that do not satisfy the user’s next step.
Citations point to outdated pages
Record the cited URL and page date, then update or replace the underlying content where appropriate. Do not assume that adding a special AI file will redirect retrieval; Google’s guidance does not endorse llms.txt as a ranking requirement.
Results differ between countries or accounts
Do not merge them into one average. Store locale and account state, then report separate cohorts so geographic and personalization effects remain visible.
Brand detection misses aliases
Maintain a versioned alias list for company names, product names and common spellings. Use it for an automated first pass, and periodically compare it with human labels to identify false positives and negatives.
Provider billing is higher than expected
Count grounded search calls, token usage and retries separately. Recheck current provider pricing, especially where a free allowance or a per-search fee applies, before increasing cadence.
A practical reporting format
For each engine, publish the prompt cohort and run dates beside:
- mention rate and citation rate;
- median first-mention position;
- share of voice and competitor overlap;
- top cited domains and first-party URL share;
- recommendation or sentiment distribution;
- AI referral sessions and landing pages;
- model, tool, locale and account conditions; and
- a change log explaining any break in the series.
Include a small set of representative raw answers with their linked citations. This makes the report useful for content teams, search specialists and product owners rather than reducing a changing retrieval system to one unsupported score.
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