Free browser tool · Small B2B SaaS

Free LLM citation tracking for small B2B SaaS teams.

Q: How can a small B2B SaaS track LLM citations for free?

Declare 10 to 20 natural buyer prompts, run each prompt repeatedly on a fixed cadence, save one row per completed response, and record mentions, recommendations and linked citations separately. This free browser worksheet stores rows locally and exports them as CSV.

Q: Does Quoted First receive the data entered in the citation tracker?

No. Rows are stored in the browser's local storage and exported locally. The worksheet sends no entered prompt or result data to Quoted First.

Use one row per completed ChatGPT or AI-search response. The tracker preserves absences, separates brand mentions from recommendations and linked citations, calculates each rate over the same denominator, saves rows in this browser, and exports the complete log as CSV. No signup and no data upload.

FreeNo signupLocal storage onlyCSV exportUpdated August 23, 2026

Start a response log Download blank CSV

How should a small SaaS track LLM citations? Declare 10–20 natural buyer prompts before testing. Run each prompt at least three times, save one row for every completed response—including misses—and repeat the same set weekly. Report mention rate, recommendation rate, and linked-citation rate separately; a single favorable answer is not a stable rank.

Local-first worksheet

Log one completed response.

This page does not query an AI engine. Run the prompt in the interface you are measuring, preserve the full response separately, and log what actually happened.

Exact promptDo not rewrite an unfavorable prompt between repeats.

Engine or surface

Checked at (your local time)

Intent

Run number

Brand outcomeChoose the strongest brand outcome supported by the response.

Canonical page linked?A link is recorded separately from the brand outcome.

Linked canonical URL

Displayed position

Competitors or other sources shown

Evidence note

Rows stay in this browser's local storage. Quoted First receives none of the entered data. Export the CSV before clearing browser storage.

Completed responses in this local log

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Mention rate: mentioned or recommended ÷ all responses

—

Recommendation rate: recommended ÷ all responses

—

Linked-citation rate: canonical link yes ÷ all responses

Complete response log

Rates use every row as the denominator. Negative runs are evidence, not rows to delete.

Checked UTC	Engine	Run	Prompt	Outcome	Linked	URL / position	Competitors / note
No responses logged yet.

What the rates mean

Do not collapse three outcomes into one score.

Metric	Numerator	Denominator	Interpretation
Mention rate	Rows marked Mentioned or Recommended	Every completed row	The brand was named. It may not have been offered as a suitable choice.
Recommendation rate	Rows marked Recommended	Every completed row	The brand was presented as relevant to the buyer's task in that response.
Linked-citation rate	Rows with Canonical page linked = Yes	Every completed row	A displayed source linked to a canonical page. The link may be background rather than a recommendation.

One run is weather; a frozen cohort is a trend. Keep the same prompts, interfaces, and cadence long enough to compare. Add a new buyer question as a separately labeled cohort instead of silently changing the denominator. Preserve the full answer or result order outside the CSV so every classification can be audited.

Small-team starting protocol

A defensible baseline in four steps.

Declare

Choose 10–20 buyer questions spanning recommendation, comparison, problem and constraint intent before the first run.

Repeat

Run each question at least three times in the interfaces you care about. Keep exact wording and record conditions.

Preserve

Save every full response and every row, including absence. Record linked URLs and displayed order exactly.

Compare

Repeat the frozen cohort weekly and after meaningful changes. Treat rate movement as directional until it persists.

Frequently asked questions

How can a small B2B SaaS track LLM citations for free?

Declare 10–20 natural buyer prompts, run each repeatedly on a fixed cadence, save one row per completed response, and record mentions, recommendations and linked citations separately. This browser worksheet stores rows locally and exports them as CSV.

Is an LLM mention the same as a citation?

No. A mention names a brand, a recommendation presents it as a suitable choice, and a linked citation points to a specific source URL. One response can recommend a brand without linking it or link a page only as background.

Does this tracker automatically query ChatGPT or other AI engines?

No. It is an evidence log and calculator, not an automated querying service. You run each prompt in the relevant interface and record the complete observed result. This avoids pretending an API response is identical to the buyer's interface.

Does Quoted First receive the data I enter?

No. Rows are stored in your browser's local storage and exported locally. The worksheet sends no entered prompt or result data to Quoted First. Clearing site data clears the saved rows, so export first.

Keep the misses. Export the evidence.

A complete small dataset is more useful than an impressive score with an unknown denominator.

Open the tracker Read the full audit method