---
title: "AI search monitoring"
description: "AI search monitoring for ChatGPT, Perplexity, Gemini and Google AI Overviews. Capture answers, brand mentions and cited sources by prompt and country."
canonical: https://www.zenrows.com/use-cases/ai-search-monitoring
updated: 2026-10-18
---

# AI search monitoring

AI search monitoring tracks how answer engines such as ChatGPT, Perplexity, Gemini and Google AI Overviews answer prompts about your brand and category: which brands they mention, in what order, and which pages they cite. Zenrows runs the chat interfaces in Browser and fetches AI Overview results pages with Fetch, so you store every answer and score it over time.

## Sample output: one AI answer

```json
{
  "prompt": "best project management software for agencies",
  "engine": "perplexity",
  "country": "us",
  "run": 2,
  "captured_at": "2026-10-16T09:41:07Z",
  "answer_text": "For agencies, the tools recommended most often are ...",
  "brands_mentioned": [
    { "brand": "Example Rival", "position": 1, "recommended": true },
    { "brand": "Your Brand", "position": 3, "recommended": true },
    { "brand": "Other Tool", "position": 4, "recommended": false }
  ],
  "citations": [
    { "position": 1, "url": "https://www.example-reviews.com/best-pm-tools", "domain": "example-reviews.com" },
    { "position": 2, "url": "https://www.yourbrand.example/agencies", "domain": "yourbrand.example" }
  ],
  "your_brand": { "mentioned": true, "position": 3, "cited": true },
  "sentiment": "positive"
}
```

An illustrative answer record: Zenrows returns the answer and its sources, and brand matching and sentiment are your own parsing step or an LLM's.

## What AI visibility tracking measures

AI visibility tracking runs a fixed set of prompts through each answer engine and records what comes back. The same raw answers serve several jobs.

| Job | Data you collect | Typical sources |
|---|---|---|
| Brand visibility | Whether your brand appears, its position in the answer, share of answers | ChatGPT, Perplexity, Gemini, Microsoft Copilot |
| Citation tracking | URLs and domains each answer cites, and their order | Perplexity sources, ChatGPT search citations, AI Overview links |
| AI Overview tracking | Whether an AI Overview appears for a keyword, its text and linked sources | Google results pages per keyword and country |
| Competitor share of voice | Mentions per competitor across the whole prompt set | The same prompts on every engine |
| Answer accuracy checks | What engines say about your pricing, features and availability | Brand prompts such as "Does Your Brand support SSO?" |
| Source gap analysis | Sites cited for your category that never mention you | Citation lists across runs, joined with your content and PR targets |

## Why AI answers are hard to track

Answer engines were built for people typing questions. Getting a number you can trend means driving a chat interface, getting past its bot protection, and sampling answers that change between runs.

| What breaks | What it looks like | How Zenrows handles it |
|---|---|---|
| Chat interfaces are apps | The answer streams in after you type and submit, and there is no URL to fetch | Browser runs Playwright or Puppeteer in a hosted browser that types the prompt, waits for the stream to end, and reads the answer and sources |
| Bot protection on chat products | Cloudflare challenges and Turnstile on the chat page | Browser sessions run with Zenrows' stealth, which gets through Cloudflare, Turnstile and similar protections |
| AI Overviews load late and vary by country | The overview block is missing from raw HTML, or differs between markets | Fetch with Adaptive Stealth Mode and geotargeting returns the rendered results page as a user in that country sees it |
| Answers change between runs | The same prompt names different brands an hour later | Run each prompt several times per period and store every answer, so you trend the share of runs that mention you |
| Answers are prose | Mentions sit inside paragraphs and citations sit in footnotes | Markdown output keeps the AI Overview text and links in a form a parser or an LLM reads directly |

## How to monitor AI search with Zenrows

1. **Write the prompt set.** Mix category prompts ("best CRM for startups"), comparison prompts ("Your Brand vs Example Rival") and brand prompts, and assign each a country.
2. **Run the chat engines in Browser.** Open each engine, submit the prompt, wait for the answer to finish, and save the text and every source link.
3. **Fetch AI Overviews for your keywords.** Request each Google results page from the target country with geotargeting, let Adaptive Stealth Mode render it when needed, and keep the overview block.
4. **Parse mentions and citations.** Match brand names and aliases, record their order, and resolve each cited URL to a domain.
5. **Repeat and trend.** Run the full set several times a week and report share of answers, average position and citation share per engine.

```python Python
# pip install playwright
from playwright.sync_api import sync_playwright

PROMPT = "best project management software for agencies"

with sync_playwright() as p:
    browser = p.chromium.connect_over_cdp("wss://browser.zenrows.com?apikey=YOUR_ZENROWS_API_KEY")
    page = browser.new_page()
    page.goto("https://www.perplexity.ai/")
    page.fill(PROMPT_INPUT_SELECTOR, PROMPT)  # the chat input box
    page.keyboard.press("Enter")
    page.wait_for_selector(ANSWER_DONE_SELECTOR, timeout=90_000)  # shown when the answer finishes
    answer = page.inner_text(ANSWER_SELECTOR)
    sources = page.eval_on_selector_all(SOURCE_LINK_SELECTOR, "els => els.map(e => e.href)")
    print(answer, sources)
    browser.close()
```

```bash cURL
# The weekly keyword set for one country, as a single job
curl -X POST "https://async.api.zenrows.com/v1/jobs" \
  -H "X-API-Key: YOUR_ZENROWS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "type": "regular",
    "status": "closed",
    "zenrows_params": { "mode": "auto", "proxy_country": "us", "response_type": "markdown" },
    "tasks": [
      { "url": "https://www.google.com/search?q=best+crm+for+startups&gl=us&hl=en", "external_id": "crm-startups|us" },
      { "url": "https://www.google.com/search?q=crm+with+email+automation&gl=us&hl=en", "external_id": "crm-email|us" }
    ]
  }'
```

```bash CLI
npx -y @zenrows/cli init
zenrows fetch "https://www.google.com/search?q=best+crm+for+startups&gl=us&hl=en" --proxy-country us --output markdown
# keywords-us.jsonl: one {"url": "..."} per Google results page
zenrows batch create keywords-us.jsonl --proxy-country us --output markdown --wait
```

```text Claude Code (MCP)
# Connect once
claude mcp add zenrows -e ZENROWS_API_KEY=YOUR_ZENROWS_API_KEY -- npx -y @zenrows/mcp

# Then ask:
Use the Zenrows browser to open perplexity.ai, ask "best project management software for agencies", wait for the answer to finish, and list every brand it mentions in order and every source URL it cites. Close the session when done.
```

## Which Zenrows product fits

| If you need to | Use | Why |
|---|---|---|
| Capture answers from ChatGPT, Perplexity or Gemini | [Browser](/products/browser) | A hosted browser you drive with Playwright or Puppeteer handles the chat interface and its protection |
| Track AI Overviews for a keyword list | [Fetch](/products/fetch) | Returns the rendered Google results page from the country you choose |
| Run thousands of keyword and country pairs every week | [Batch](/products/batch) | One job per country, with results by webhook when it finishes |
| Check by hand how engines describe your product today | [MCP Server](/mcp) | Claude or Cursor fetches the live results page and summarises it in the chat |

## What success looks like

| Measure | What good looks like | How to check it |
|---|---|---|
| Prompt coverage | Every prompt runs on every engine and country in each period | Count stored answers per prompt, engine and country against the plan |
| Sample depth | Each prompt runs enough times that week-to-week changes exceed run-to-run noise | Run the same prompt twice on the same day and compare the results |
| Capture completeness | Every stored answer has its full text and every cited URL | Flag answers with no citations on engines that normally show sources |
| Mention accuracy | Brand matching catches aliases and product names without false matches | Review a sample of 50 answers each month by hand |
| Trend history | Twelve or more weeks of share of answers per brand and engine | Check the series for gaps before each report |

## AI search monitoring FAQ

### What is AI search monitoring?

AI search monitoring, also called AI visibility tracking or LLM tracking, is the practice of asking answer engines the questions your buyers ask and recording the responses. You measure how often your brand is mentioned, where it appears in the answer, whether your pages are cited, and how competitors compare. It is the answer-engine counterpart of rank tracking.

### How is AI visibility tracking different from rank tracking?

Rank tracking records a position in a list of ten links that stays fairly stable between checks. An AI answer is generated text: it may mention five brands or none, cite pages that do not rank, and change between two identical prompts. So you track share of answers and citations across many runs instead of one position per keyword.

### Can I scrape ChatGPT and Perplexity answers?

Yes, through Browser. Your Playwright or Puppeteer script connects to a Zenrows browser, opens the engine's web interface, submits the prompt and reads the answer and its sources once the response finishes. Review each engine's terms of use for your use case, and keep the volume of prompts proportionate to what you need to measure.

### Is there a Google AI Overview API?

Google does not publish an API for AI Overviews. To track them, fetch the Google results page for each keyword from the country you care about, with Adaptive Stealth Mode rendering the page when the overview needs it, then read the overview block and its source links from the returned page. Batch runs a full keyword list per country as one job.

### How often should I run each prompt?

Answers vary from run to run, so a single run per week is a weak signal. Run each prompt at least three times per period on each engine and report the share of runs that mention your brand. Daily runs help during a launch or after you publish content meant to be cited.

### Which prompts should I track?

Start from real buyer questions: category prompts ("best tool for X"), comparisons with named competitors, problem prompts that lead to your category, and brand prompts that check facts about your pricing and features. A set of fifty to two hundred prompts per market is a practical start. Group them by topic so you can report visibility per theme as well as overall.

### How do I see which sources ChatGPT cites for my category?

Run your category prompts through ChatGPT search in Browser and store the citation links that come with each answer. Resolve every link to its domain, then count how often each domain is cited across runs and prompts. The domains at the top are the pages answer engines trust for your category: review sites, comparison articles, documentation and forums. Those that never mention your brand are your outreach and content targets.

## Related use cases

- [Brand monitoring](https://www.zenrows.com/use-cases/brand-monitoring.md): Every mention of your brand in news, forums, reviews and search, as clean text for sentiment and alerts.
- [Competitor monitoring](https://www.zenrows.com/use-cases/competitor-monitoring.md): Pricing pages, changelogs, feature pages and job posts on rival sites, snapshotted and diffed on every run.
- [LLM web scraping for RAG and agents](https://www.zenrows.com/use-cases/llm-web-scraping.md): Live pages as Markdown or JSON for RAG pipelines, grounding and agent tool calls.
- [AI agent browser](https://www.zenrows.com/use-cases/ai-agent-browser.md): A cloud browser your agent drives over MCP or CDP to log in, click and fill forms.
- [Sentiment analysis data](https://www.zenrows.com/use-cases/sentiment-analysis-data.md): Review text with ratings, forum threads and comments, cleaned and labeled for sentiment and NLP models.

Start free: https://app.zenrows.com/register. Docs: https://docs.zenrows.com