Track your brand in AI answers

AI visibility (also called GEO, generative engine optimisation) asks a simple question: when someone asks an AI assistant about your category, does the answer name you, cite your site, or show your products? The AI assistant endpoints answer it with data. Each call asks one assistant one prompt as a logged-out visitor, and track checks the answer for your brands and domains.

The workflow:

1. Write a prompt set: the questions your buyers ask.
2. Run it across assistants and countries with batches, several times each.
3. Read mentions and the sources to measure share of answers and which pages get cited.
4. Save the prompts that matter as monitors to follow visibility over time.

Write a prompt set

Use the questions people actually ask, in their words, not your product name:

• Category questions: “What are the best CRMs for a 10-person startup?”
• Comparisons: “HubSpot vs Pipedrive for a small sales team”
• Problems: “How do I stop deals slipping through the cracks in a spreadsheet?”
• Local and shopping questions, which bring back places and products: “best espresso machine under $500”

Keep the set stable once you start measuring, so runs stay comparable. A prompt can be up to 4,000 characters.

Track brands and domains

Send up to 20 terms in track. A term with a dot (hubspot.com) is a domain: it matches sources and product links on that domain and its subdomains, and the answer text when it names the domain. Any other term (Pipedrive) is a brand: it matches whole words in the answer text, source titles and domains, and product titles, brands and merchants, ignoring case. Track a brand and its domain separately when you want both signals.

curl https://api.serpkite.com/v1/chatgpt \
  -H "Authorization: Bearer $SERPKITE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"prompt":"What are the best CRMs for a 10-person startup?","track":["hubspot.com","HubSpot","Pipedrive","pipedrive.com"]}'

Each term comes back in mentions, in the order you sent them:

"mentions": [
  { "term": "hubspot.com", "mentioned": false, "cited": true, "sources": [2], "text_position": -1 },
  { "term": "HubSpot", "mentioned": true, "cited": true, "sources": [2], "text_position": 0.041 },
  { "term": "Pipedrive", "mentioned": true, "cited": false, "sources": [], "text_position": 0.071 },
  { "term": "pipedrive.com", "mentioned": false, "cited": false, "sources": [], "text_position": -1 }
]

Named but not cited means the assistant knows you but grounded its answer elsewhere; cited but not named means your page informed the answer without your brand getting credit. The sources list tells you which pages do get cited, which is often the most useful part: those are the pages to earn a place on.

With format: "markdown", the answer ends with a Mentions section in plain words (“named and cited”, “listed as a source”, “not mentioned”), which is handy for an agent or a report.

Run the prompt set with batches

Queue up to 100 prompts per call with POST /v1/batches. Every request in a batch goes to one endpoint, so send one batch per assistant. AI answers keep their full price in a batch (ChatGPT 4, Gemini 3, Copilot Search 3, Google AI Mode 2 and Brave AI 2 credits); the batch lane spares you from holding 100 requests open for a minute each, and delivers every answer to a signed webhook.

curl https://api.serpkite.com/v1/batches \
  -H "Authorization: Bearer $SERPKITE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "endpoint": "chatgpt",
    "requests": [
      { "prompt": "What are the best CRMs for a 10-person startup?", "track": ["hubspot.com", "Pipedrive"], "country": "us" },
      { "prompt": "What are the best CRMs for a 10-person startup?", "track": ["hubspot.com", "Pipedrive"], "country": "gb" },
      { "prompt": "HubSpot vs Pipedrive for a small sales team", "track": ["hubspot.com", "Pipedrive"], "country": "us" }
    ],
    "webhook_url": "https://example.com/hooks/serpkite"
  }'

Repeat the same batch for gemini, copilot, ai_mode and brave_ai. Each finished job arrives as a batch.completed webhook (or poll GET /v1/batches/{id}), with the answer in the job’s result, shaped like the realtime response. See Webhooks.

A batch reruns a prompt only if you send it again: results are cached for at most an hour, and only a request with max_age reads the cache, so repeated prompts are asked live.

Read the variance

Assistants are not deterministic. The same prompt can name three brands in one run and five in the next, cite different pages, or skip web search. Answers also differ by country: ChatGPT and Gemini localise to the visitor’s location, Copilot Search and Brave answer for the market you set, and AI Mode for the country it is asked from (we prefer the country you send). So measure rates, not single answers:

• Sample each prompt several times (five or more per assistant and country is a reasonable start) and at different times of day.
• Share of answers: the fraction of runs where mentioned is true. Compute it per prompt, per assistant and per country.
• Citation share: the fraction of runs where cited is true, and which of your URLs appear in sources.
• Prominence: the median text_position over the runs that name you.
• Competitors: track them in the same call and compare their rates to yours.

Treat a change of a few points between two small samples as noise. Trends over weeks, from monitors, are more reliable than any single day.

Follow it over time with monitors

A monitor with an AI assistant endpoint asks its prompt every run and reports every answer, not only changes, so each run is one sample in your time series.

curl https://api.serpkite.com/v1/monitors \
  -H "Authorization: Bearer $SERPKITE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "endpoint": "chatgpt",
    "prompt": "What are the best CRMs for a 10-person startup?",
    "track": ["hubspot.com", "Pipedrive"],
    "country": "us",
    "interval": "daily",
    "name": "CRM shortlist · ChatGPT US",
    "webhook_url": "https://example.com/hooks/serpkite"
  }'

AI assistant monitors take prompt, country, language (not chatgpt), web_search (chatgpt) and track; search options such as q, num or engine are rejected. Each run’s result is one answer object: text, markdown, model, sources, new_sources (links not cited or listed in earlier runs), search_queries, products and, with track, mentions. Read it from GET /v1/monitors/{id}/runs, or take the signed monitor.results webhook, which carries prompt instead of q and the run’s answer as the single item of new_results. See Scheduled monitoring.

Each run costs the endpoint’s price, and failed runs are free. A daily ChatGPT monitor costs 4 × 30 = 120 credits a month; a daily Brave AI monitor 60. Monitor history keeps result bodies for 24 hours, so store the answers you need.

Budget

A prompt set of 50 prompts, asked 5 times on each of the five assistants in one country, is 250 answers per assistant: 3500 credits in all. Start with one or two assistants and a handful of prompts, check the answers look the way you expect, then scale up. Set a spend cap or a dedicated API key with a monthly limit for recurring runs.

Next steps