I. Introduction
In the past, to learn about a brand, we would typically open Google or Baidu, search for a few keywords, and browse the search results page by page. For brands, evaluating how well their SEO was performed basically required checking keyword rankings and web page impressions.
However, many people's search habits have changed. For instance, if you are planning to buy a NEV (New Energy Vehicle), you might no longer open a dozen web pages. Instead, you would directly ask:
"Is the 2026 Tesla Model 3 still worth buying? What are its pros and cons compared to other NEV models?"
A few seconds later, ChatGPT directly provides a well-organized answer, even conveniently listing the reference sources. Users might not even look at the search results page before making their initial judgment.
This raises an interesting question: If users are seeing AI-generated answers instead of traditional search results, is the old "monitor rankings" approach still sufficient for brands? The answer is obviously not that simple.
In the past, we cared about "what rank my brand is in the search results." Now, we need to pay further attention to: Has the AI mentioned my brand? How does it evaluate me? What sources does it cite when recommending me? Do competitors appear more frequently?
This is why GEO, or Generative Engine Optimization, is gradually gaining attention. Simply put, SEO is about making it easier for search engines to find you, while GEO focuses on whether the AI will mention you, how it introduces you, and what content it cites when users ask AI questions.
But a problem arises: ChatGPT's responses look like mere natural language. Can they really be batch-fetched and analyzed just like ordinary web pages?
This time, I will use Tesla vehicles as a practical case study, attempting to use Bright Data's AI Bot Scraper to fetch responses from ChatGPT and other AI platforms for the same set of questions, and convert the content, citation sources, and other information in the responses into structured data.
If in the past we were monitoring "search results," this time, let's see if we can directly monitor "AI answers."
Bright Data Registration Link: https://www.bright.cn/products/web-scraper/llm?utm_source=brand&utm_campaign=brnd-mkt_cn_csdn_aipaisen202608
II. Technical Analysis
If we understand traditional web crawlers as "moving things from web pages," then what the AI Bot Scraper does is actually closer to "turning AI responses into an analyzable dataset." This is its biggest difference from traditional SERP scraping.
1. From Traditional SERP to AI Answer
In the past, when collecting search data, we focused on a few core fields in the search results page:
Keyword → Rank → Title → URL → Snippet
These data points help us judge a website's exposure in search engines, but they mainly solve the question of "what the search results are."
However, when users now directly ask questions to AI platforms like ChatGPT, what they ultimately see is no longer a string of web links, but a comprehended and organized answer generated by AI.
Therefore, the target of AI Bot Scraper collection has also changed: Prompt → AI Answer → Citations → Sources → Links
Bright Data's official documentation also explicitly distinguishes AI Bot Scraping from traditional SERP Scraping: traditional SERP scraping mainly targets search results pages, while AI Bot Scraper targets natural language queries to obtain structured answers generated by AI with context.
This means that if we want to study "how AI evaluates Tesla," simply scraping Google search results is not enough. What we really need is what the AI ultimately tells the user, and why it says so.
2. What Exactly Can AI Bot Scraper Scrape?
Taking ChatGPT as an example, it doesn't just simply return an answer_text and stop. Bright Data's AI Scraper can further obtain structured fields such as Prompt, Answer, Citations, Links, Recommendations, Country.
For example, if we input:
"Is Tesla Model 3 still worth buying in 2026?"
The final data obtained can be understood like this:
Prompt ↓ AI Answer ↓ ├── Answer Text ├── Citations ├── Sources / Links ├── Recommendations └── Other Metadata
In this way, the AI response becomes a data record that can be stored in a database for further statistics and analysis. For GEO, this is especially important.
Because we can not only count whether Tesla is mentioned by AI, but also continue to analyze:
Is the AI's description positive or negative?
Which brands does the AI compare Tesla with?
Which websites does the AI cite when recommending Tesla?
Which sources repeatedly influence the AI's answers?
This shifts from simply "scraping answers" to analyzing AI's brand perception and information sources.
3. Not Just ChatGPT: Unified Collection of Multi-Platform AI Answers
If we only monitor one AI platform, the value is actually quite limited. Because for the same question, ChatGPT, Perplexity, and Gemini may not give exactly the same answers.
For enterprises, it is more valuable to establish a cross-platform AI visibility monitoring system.
Currently, Bright Data's AI Scrapers cover platforms such as ChatGPT, Perplexity, Gemini, Google AI Mode, and Copilot.
For example, we can design such a monitoring workflow:
Same Prompt ↓ ┌───────────────┐ │ ChatGPT │ │ Perplexity │ │ Gemini │ │ Google AI Mode│ └───────────────┘ ↓ Structured AI Answers ↓ Brand / Competitor / Source Analysis
The advantage of doing this is that what we see is no longer a single AI's single response, but a set of data that can be compared horizontally.
For example, asking the same question: "Is Tesla still worth buying in 2026?"
ChatGPT might emphasize software and autonomous driving more, Perplexity might cite more automotive media, and Gemini might expand from product and market information.
These differences themselves are highly valuable monitoring data.
4. From a Single Query to Scaled AI Data Collection
Of course, if you only occasionally open ChatGPT to ask a few questions, you don't need a dedicated data collection tool at all.
What truly reflects the value of AI Bot Scraper is when we turn a single query into long-term, batch, and automated monitoring tasks.
For example, focusing on Tesla, we can prepare dozens or even hundreds of Prompts:
Is Tesla worth buying? How is the Tesla Model 3? How is the Tesla Model Y? Which is better, Tesla or BYD? What is Tesla's biggest advantage? What is Tesla's biggest disadvantage? What are the most worth-buying NEV brands in 2026? ……
Then regularly collect the answers to these questions on different AI platforms, and compare the changes in brand mentions, recommendations, and cited sources.
III. Practical Implementation
After explaining so many technical concepts, we still need to run it once to see if it actually works well.
1. Preparation
Before starting the scraping, we need to register a Bright Data account, and then we need to prepare the input parameters.
Bright Data Registration Link: https://www.bright.cn/products/web-scraper/llm?utm_source=brand&utm_campaign=brnd-mkt_cn_csdn_aipaisen202608
Here, instead of just asking "How is Tesla," I designed Prompts from several dimensions: brand evaluation, product performance, competitor comparison, and purchase decision. For example:
Brand Evaluation: "What are the main strengths and weaknesses of Tesla in 2026?"
Model Evaluation: "Is Tesla Model 3 still worth buying in 2026?"
Competitor Comparison: "Tesla vs BYD, which electric car brand is better?"
Purchase Decision: "What are the best electric vehicle brands in 2026?"
The advantage of doing this is that what we finally get is not an isolated AI response, but a set of samples that can be further analyzed.
Utilizing Bright Data's capability to support batch processing of up to 5,000 URLs, we can throw all these questions into the scraping queue at once, instead of inputting them manually one by one.
2. Traditional SERP
Let's first search for one of the questions in the most familiar way.
For example:
"Is Tesla Model 3 still worth buying in 2026?"
What traditional search engines return is mainly a set of web page results.
We can further scrape:
Rank Title URL Text
For example, automotive media, review websites, Tesla's official website, and other sources might appear. These data are certainly valuable because we can use them to judge:
Which websites are discussing the Model 3, and which content ranks relatively high.
But the problem is also obvious. Suppose 10 web pages appear in the search results; we still need to open these pages ourselves, read the content, and then judge:
Which websites actually think the Model 3 is worth buying?
More importantly, we do not know how the AI will ultimately synthesize this information.
Search results tell us "what web pages are there," but they do not directly tell us "what answer the AI will give to the user."
However, mainstream search engines now, such as Baidu, Bing, and Google, have all launched AI search, prioritizing the display of AI search results.
3. AI Bot Scraper
There are two choices here: one is to write code to call the API, which is suitable for developers; the other is no-code page scraping, which only requires inputting parameters and is suitable for non-developers.
1. Python Script API Call
Here we use a Python script to call the API. The strength of Bright Data lies in its alignment with developers' workflows; you can directly configure the request parameters in the control panel and then generate the API code with one click.
import requests
import json
headers = {
"Authorization": "Bearer [replace with API key]",
"Content-Type": "application/json",
}
data = json.dumps({
"input": [{"url":"https://chatgpt.com/","prompt":"What are the main strengths and weaknesses of Tesla in 2026?","country":"","web_search":false,"additional_prompt":""},{"url":"https://chatgpt.com/","prompt":"Is Tesla Model 3 still worth buying in 2026?","country":"","web_search":false,"additional_prompt":""},{"url":"https://chatgpt.com/","prompt":"Tesla vs BYD, which electric car brand is better?","country":"","web_search":false,"additional_prompt":""},{"url":"https://chatgpt.com/","prompt":"What are the best electric vehicle brands in 2026?","web_search":false,"additional_prompt":""}],
"limit_per_input": null,
})
response = requests.post(
"https://api.brightdata.com/datasets/v3/scrape?dataset_id=gd_m7aof0k82r803d5bjm¬ify=false&include_errors=true",
headers=headers,
data=data
)
print(response.json())2. No-Code API Call
① Log in to your account and go to the backend control page, find Scraper → Scraper Library → AI Search → chatgpt
② Next, input our four prompts. You can also select the country and add other detailed parameters, or click "+ Add Input" to continue adding (if it's large-scale collection, you can choose the asynchronous scraper mode). On the left, code is provided for developers to use. You can select the corresponding programming language and input your own Bright Data API token. After inputting the parameters, click "Run Manually".
③ After it finishes running, just select the file format you need and download it.
It can be seen that the result file contains information such as prompts, response text, and citation links.
4. Data Analysis
Previously, we just "scraped" ChatGPT's responses. But if it's just to copy a few paragraphs of text, there's actually no need to set up a dedicated data collection process.
The true value of AI Bot Scraper lies in converting AI responses, which could originally only be read manually, into structured data that can be further analyzed.
This time, I designed four types of Prompts in total, corresponding to four scenarios: brand evaluation, model evaluation, competitor comparison, and purchase decision.
Through these questions, we can observe from different angles how ChatGPT describes Tesla in 2026.
4.1 Brand Evaluation: Tesla's Advantages Are No Longer Just "Building EVs"
First, let's look at: "What are the main strengths and weaknesses of Tesla in 2026?"
This question is relatively open and is suitable for observing AI's overall brand perception of Tesla.
From the actual returned results, ChatGPT's core positioning of Tesla has undergone a relatively obvious change: Tesla is no longer just a NEV brand, but a technology company spanning automobiles, energy, software, and AI simultaneously.
In terms of advantages, the response first mentions Tesla's manufacturing scale and supply chain capabilities, followed by software, FSD, and the AI ecosystem, while also specifically mentioning the energy storage business.
But interestingly, ChatGPT did not just sing praises.
In the weaknesses section, it explicitly mentioned profit pressure in the automotive business, a relatively concentrated product line, intensified competition, commercialization risks of autonomous driving, and high capital investment.
Therefore, if we compress this information into a few brand tags, we can roughly get:
| Dimension | AI's Perception of Tesla |
|---|---|
| EV Manufacturing | Very Strong |
| Charging Infrastructure | Very Strong |
| Software / FSD | Huge Potential, but Yet to be Verified |
| Energy Business | Continuously Strengthening |
| Product Richness | Relatively Insufficient |
| Automotive Profit | Facing Pressure |
| Autonomous Driving | High Potential, High Risk |
An interesting GEO signal has actually emerged here:
The focus of AI's perception of Tesla is gradually shifting from "EV leader" to a comprehensive technology platform integrating "Automobiles + AI + Energy + Autonomous Driving."
4.2 Model 3: AI Thinks It's "Worth Buying," but It's No Longer a Blind Recommendation
The second question is more specific: "Is Tesla Model 3 still worth buying in 2026?"
This time, the answer given by ChatGPT is quite clear:
Worth buying, but with conditions.
The response suggests that if consumers can charge at home and plan to keep the car long-term, the Model 3 still has good competitiveness in 2026.
The main advantages focus on energy efficiency, the Supercharger network, lower daily maintenance costs, and current pricing and promotional policies.
On the other hand, the AI also pointed out several obvious purchase risks:
First is the residual value.
If consumers only plan to keep it for two or three years, higher depreciation might offset some of the cost advantages of using the car.
Second, competitors have caught up.
The response directly mentioned brands such as BMW, Hyundai, Kia, BYD, and Volkswagen, indicating that the Model 3 is no longer in a market environment with "almost no rivals."
Third, there are obvious differences in user preferences regarding the product experience.
For example, the minimalist interior and the interaction method heavily relying on the central screen; some people like it, while others might find it inconvenient.
This actually provides a good idea for brand monitoring:
Do not just count "whether the AI recommended Tesla," but further break down "under what conditions the AI recommends Tesla."
Because from the results this time, the recommendation for the Tesla Model 3 is no longer a simple "good car, so buy it," but rather:
Having home charging conditions + long-term ownership + valuing efficiency, software, and charging experience → higher recommendation degree.
This kind of conditional recommendation is actually more analytically valuable than simple positive or negative evaluations.
4.3 Tesla vs BYD: AI Has Started Placing the Two in Different Value Coordinate Systems
The third question directly puts Tesla and BYD together: "Tesla vs BYD, which electric car brand is better?"
The final conclusion given is:
BYD is more suitable for consumers pursuing price, configuration, battery technology, and practicality; Tesla is more suitable for consumers valuing software, efficiency, performance, and the charging ecosystem.
From a GEO perspective, this means brand competition is not necessarily manifested as "who ranks first," but rather:
Which tag your brand occupies in the AI's response.
Moreover, a very important piece of information can also be seen in this result: the AI not only gives its own judgment but also cites third-party content such as the official websites of Tesla and BYD as information sources.
This further illustrates that GEO monitoring cannot just look at the answer itself, but also at "where the AI got this answer from."
4.4 Purchase Decision: Tesla Remains in the First Tier of AI Recommendations
The last question: "What are the best electric vehicle brands in 2026?"
ChatGPT ranked Tesla in 1st place, summarizing its core advantages as:
Charging network, efficiency, software
That is, charging network, energy efficiency, and software ecosystem. This result actually corresponds to the previous brand evaluation and competitor comparison.
And a noteworthy phenomenon appears here: AI's evaluation of Tesla is not completely consistent.
In the specific competitor question "Tesla vs BYD," BYD gained the advantage in overall value;
but when the question expands to "the best NEV brands in 2026," Tesla returns to the first place.
This precisely shows: AI's judgment of a brand will change with the intent of the Prompt.
Therefore, if an enterprise only tests one question and then announces "my brand ranks X in ChatGPT," it is actually not reliable.
A more reasonable approach should be to establish a Prompt Matrix, continuously collecting data from multiple dimensions such as brand perception, product, competitors, price, and user needs.
4.5 From a Single Test to a GEO Monitoring System
Putting these four sets of results together, we can get a relatively complete Tesla AI brand portrait:
| Monitoring Dimension | Main Performance in This Test |
|---|---|
| Brand Perception | Automobiles + AI + Energy + Autonomous Driving |
| Core Advantages | Software, Efficiency, Charging Network, Manufacturing Scale |
| Main Shortcomings | Intensified Competition, Concentrated Product Line, Profit Pressure |
| Model 3 | Still Worth Buying, but Emphasizes Usage Conditions More |
| BYD Comparison | Tesla Leans Towards Technology and Ecosystem, BYD Leans Towards Value and Product |
| AI Recommendation | Still in the First Tier in Comprehensive NEV Brand Recommendations |
| Key Information Sources | Tesla Official Website, Reuters, Automotive Media, Professional Reviews, etc. |
This is what I think is the most interesting part of this practical implementation.
What AI Bot Scraper scraped is not just four paragraphs of ChatGPT text, but a set of "AI brand perception data" that can be further quantified.
If we expand the number of Prompts from 4 to 100 or 1,000, and continue to collect data daily, weekly, and monthly, we can even further calculate:
brand mention rate, recommendation rate, competitor co-occurrence rate, positive/negative sentiment, core brand tags, citation source proportion, and the changes of these metrics over time.
At this point, the value of AI Bot Scraper shifts from "scraping ChatGPT responses" to truly becoming AI search data infrastructure.
And this might be where GEO truly starts to become interesting.
IV. Conclusion
After this practical implementation, I believe the true value of AI Bot Scraper lies not just in "being able to scrape ChatGPT's responses," but in turning AI responses that could originally only be checked manually into data that can be collected in batches, structured, and analyzed.
Traditional SERP focuses on "which web pages rank first," while AI Bot Scraper further focuses on "how the AI ultimately evaluates a brand, and what information sources it cites."
For GEO and brand monitoring, this distinction is very important.
Through the Tesla case, we can further track:
How AI evaluates Tesla;
The exposure and recommendation status of Tesla and competitors like BYD;
Which media and websites AI frequently cites;
Whether there are differences in brand perception under different Prompts and different AI platforms.
And when these data shift from a single test to long-term, batch collection, we can further establish our own AI brand visibility monitoring system.
Therefore, brand monitoring in the AI era may require a change in thinking:
In the past, we focused on "where the search engine ranks me," but now we also need to focus on "whether the AI will recommend me, and why it recommends me."
This might be the new issue that brands truly need to focus on in the GEO era.
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