The only way to know ChatGPT sent them is to ask.

• The answer is rendered in a native app or a desktop client, which is not a web page and has no referrer to pass on.
• Most often, there is no click at all. The person reads a name in an answer, searches it, and lands on you from Google. The visit genuinely came from search; the discovery happened somewhere with no log.

A perfect referrer header would still book this person against Google, because Google is where they clicked. Only the person knows the assistant was the reason.

“AI assistant” is a category. “ChatGPT” is a memory.

• Use the product name, never the category. A category asks them to classify their own experience first, which is exactly the step that ends in I don't remember.
• Put it beside Google, not under it. The catalog's default channel list ships chatgpt as its own row, next to google.
• One row per assistant, not one row for all of them. Collapse them into one and you can never answer “is this ChatGPT or is this all four”.
• Order rotates per respondent by default, so no assistant sits above Google for everybody and the raw share is unbiased by construction. If you pin the order with "order": "fixed", you inherit the position bias that comes with it.

Lead with the payment or upgrade flow. The respondent has just paid, so the answer is joined to revenue with no conversion tracking at all: “ChatGPT produced this much revenue last month” is the sentence a budget holder acts on.

Then run a second form in the signup flow. It is the only way to see the people an assistant sends who never pay — and with both running, you can divide a channel's share of the paying population by its share of the signup population. Above 1 it converts better than your average, below 1 worse.

Ask early inside each flow. Memory decays, and asking late means asking only the people who stayed. A small sample is visibly small. A biased one is not.

Picking a candidate can expand a follow-up in place, no page transition. Point the assistant rows at a node that asks what they were asking about, and what comes back is a real question from a real person who then converted — a different artifact from the prompt sets an AI-visibility tool guesses at and scores you against.

The assistant catalog entries carry no follow-up by default, and the topic list is yours. This is you spending one extra tap on the channel you care about most this month:

{
  "nodes": [
    {
      "id": "channel",
      "prompt": "Where did you first hear about us?",
      "order": "rotate",
      "allow_free_text": true,
      "candidates": [
        { "id": "google",     "catalog_slug": "google" },
        { "id": "chatgpt",    "catalog_slug": "chatgpt",    "expands": "ai_topic" },
        { "id": "perplexity", "catalog_slug": "perplexity", "expands": "ai_topic" },
        { "id": "claude",     "catalog_slug": "claude",     "expands": "ai_topic" },
        { "id": "gemini",     "catalog_slug": "gemini",     "expands": "ai_topic" },
        { "id": "reddit",     "catalog_slug": "reddit" },
        { "id": "friend",     "catalog_slug": "friend" },
        { "id": "dunno", "label": "I don't remember",
          "pinned": "end", "dont_remember": true }
      ]
    },
    {
      "id": "ai_topic",
      "prompt": "What were you asking about?",
      "allow_free_text": true,
      "candidates": [
        { "id": "topic_alternatives", "label": "Alternatives to a tool I already use" },
        { "id": "topic_howto",        "label": "How to do a specific thing" },
        { "id": "topic_pricing",      "label": "Comparing prices" },
        { "id": "topic_dunno", "label": "I don't remember",
          "pinned": "end", "dont_remember": true }
      ]
    }
  ]
}

• allow_free_text is where the value actually is. Your three topic buckets are a guess; what someone types is the phrasing. It is stored verbatim, capped at 500 characters, and never truncated silently.
• All four assistants expanding into one ai_topic node pools the topic counts across them. Give each its own node if you need topics per assistant — pooling is a decision you make in the config, not one you can undo at read time.
• A follow-up costs a tap, so it belongs on the channels carrying spend. The respondent who answers the first question and walks away has still told you the channel, and that response counts.

There is no dashboard. Your agent reads the aggregate over HTTP and writes the note:

curl "https://www.humansurvey.co/api/attribution/rollup\
?form_id=abc123efgh45&by=candidate&metric=revenue&from=2026-07-01&to=2026-08-01" \
  -H "Authorization: Bearer hs_sk_..."

// ILLUSTRATIVE — invented figures, one month of a payment-flow placement
"denominator": { "completed_responses": 1204, "per_node": { "channel": 1204, "ai_topic": 118 } },
"rows": [
  { "node_id": "channel", "candidate_id": "google",     "responses": 388, "share": 0.32, "revenue_cents": 1610000 },
  { "node_id": "channel", "candidate_id": "chatgpt",    "responses":  96, "share": 0.08, "revenue_cents":  486000 },
  { "node_id": "channel", "candidate_id": "perplexity", "responses":  21, "share": 0.02, "revenue_cents":   92000 },
  { "node_id": "channel", "candidate_id": "claude",     "responses":  14, "share": 0.01, "revenue_cents":   71000 },
  { "node_id": "channel", "candidate_id": "gemini",     "responses":   9, "share": 0.01, "revenue_cents":   38000 },
  { "node_id": "ai_topic", "candidate_id": "topic_alternatives", "responses": 51, "share": 0.43, "revenue_cents": null }
],
"unresolved": { "raw": 61, "dont_remember": 143, "skipped": 88, "per_node": { … } }

The denominator ships next to the shares, so 8% is 96 of 1,204 and not a percentage of some population you have to infer. And the people who did not give you an answer stay visible in unresolved instead of being dropped from the base — dropping them would inflate every channel on the page, ChatGPT included.

Free text arrives unresolved on purpose. Once a month, read what piled up and map the recurring phrasings onto a topic — retroactively, across every window that already went out:

curl "https://www.humansurvey.co/api/attribution/forms/abc123efgh45/unresolved?node_id=ai_topic" \
  -H "Authorization: Bearer hs_sk_..."
# → the phrasings people typed, grouped and counted, most frequent first:
#   "best invoicing tool for freelancers"        7
#   "alternative to <competitor>"                5
#   "how to send a quote that gets signed"       4

Nothing about the stored response changes; the rollup resolves against the live remap table on every read.

The product's job is not to hand you a confident percentage:

• It is self-report, and self-report is imperfect. People misremember, and some of them will have met your name twice. The base is always there, and I don't remember is a first-class row rather than a rounding error.
• It measures where they first heard, not what closed them. Last touch is near-constant — people search your brand name — and carries no budget decision.
• “ChatGPT” is not proof the assistant recommended you. It could have cited an article about you, and the respondent cannot tell the difference. Read the row as this person's discovery ran through ChatGPT.
• share_corrected, position_effect and calibration come back as explicit nulls today, not as computed numbers. Rotation already makes the raw share unbiased, and an absent number is better than a smoothed guess.
• Small channels are small samples. Nine Gemini responses is nine responses, and the payload gives you the count so you can decline to draw a conclusion from it.

Sign in, copy a key, hand it to your agent. From there it reads the catalog, creates one form per placement, writes the candidate lists, and gives you a URL to embed early in checkout and in signup. A month later you ask it how the month went.

The endpoints, the embed contract and the full rollup shape are in the docs, with llms-full.txt for the agent doing the work.