1. Overview
In the evolving landscape of digital marketing, visibility on AI-driven platforms like ChatGPT has become as important as ranking on traditional search engines such as Google. To improve SEO strategies for client websites, our AI team developed an AI-powered system that analyzes how ChatGPT generates its responses to various SEO-related queries. This project aimed to understand which web sources ChatGPT prioritizes and how content positioning can be optimized for inclusion in ChatGPT’s responses across AI-driven search experiences.
2. Problem Statement
As conversational AI platforms increasingly influence content discovery, marketers lack visibility into how these systems select sources and structure responses. This limits the ability to optimize content specifically for AI-generated search results.
- Limited clarity on which websites ChatGPT references when answering SEO-related queries
- Uncertainty around the content formats ChatGPT prioritizes in its responses
- Lack of understanding of how traditional Google rankings align with ChatGPT outputs
- Inability to optimize content specifically for inclusion in conversational AI results
3. Proposed Solution
To address these challenges, At Gravity Base, our team designed a ChatGPT Search Result Analysis System, a custom-built automation and data analysis pipeline. This analytical approach helped identify key SEO factors that influence ChatGPT’s content referencing and improved strategies for achieving higher inclusion in AI-based search results. The system utilized Comet, an AI assistant that autonomously performed queries on ChatGPT and copied the results into Google Sheets. These results were then fed into Opal, where the data was formatted into a structured table, enabling easy analysis of how ChatGPT generates its answers and which websites are most frequently referenced.
- Query Execution: Performs predefined search queries on ChatGPT related to SEO and marketing.
- Response Collection: Collects and stores ChatGPT-generated responses for each query.
- Source Classification: Analyzes and classifies the web sources such as blogs, listicles, official websites.
- Preference Analysis: Determines which websites and content structures ChatGPT prefers when forming its responses.
4. System Architecture
The system uses a modular automation and analysis architecture to capture, structure, and evaluate ChatGPT responses at scale.
- Comet (Agentic AI Assistant): Performs repeated searches on ChatGPT with a predefined list of SEO-related queries such as best SEO company and top marketing agency. Comet automates the process of running these queries 10 times at once and copies ChatGPT’s generated responses into Google Sheets.
- Opal (Data Formatting Tool): Processes the ChatGPT responses in Google Sheets and converts them into a tabular format. Using Gemini 2.5 Pro, Opal structures the data, identifying which companies are mentioned, the source names, source domains, and whether a target brand appears in the ChatGPT response.
- Google Sheets: Stores the ChatGPT responses and the formatted data, enabling easy access and analysis of which websites ChatGPT prefers when referencing SEO-related content.
- Data Storage (Google Cloud / Open Platform): Saves all ChatGPT responses, query metadata, and extracted URLs.
5. Implementation Highlights
- Prompt Automation: Our team defined a list of 50+ SEO-related prompts to simulate real-world user queries. Comet automatically ran these queries 10 times simultaneously and copied the responses to Google Sheets.
- Comet Tool Integration: Automated collection of all ChatGPT outputs, ensuring large-scale data consistency.
- Opal Integration: Data from Google Sheets was automatically fed into Opal, where it was transformed into structured tables using Gemini 2.5 Pro, ready for detailed analysis.
- Google Cloud Tool (Open Platform): Centralized data repository for storing responses and source websites.
- Ripple Automation: Developed a no-code automation that copied queries and responses and structured them in a standard Excel format for further analysis.
- Excel Analysis Model: Designed by our team to auto-detect and categorize each result, providing insights into which types of content ChatGPT tends to extract information from.
- Source Differentiation: The system marked whether each source was a blog, listicle, or company site, helping identify content types most likely to be used by ChatGPT for generating SEO responses.
6. Results and Benefits
The system delivered actionable insights into how ChatGPT prioritizes and references web content.
- Identified Key Content Patterns: Found that ChatGPT heavily relies on realistic blogs and listicle-style content from reputable domains.
- Source Visibility Mapping: Created a structured dataset of websites most referenced by ChatGPT, clarifying favored content types.
- Improved SEO Strategy: Helped the marketing team optimize content in ChatGPT-preferred formats, improving visibility on AI-driven platforms.
- Automation Efficiency: Reduced manual effort by over 70% in data collection and formatting, allowing teams to focus on analysis and SEO strategy refinement rather than data gathering.
- Enhanced Decision-Making: Provided data-driven insights to optimize keywords and link-building for better rankings on Google and AI-based search platforms.
7. Conclusion
The ChatGPT Search Result Analysis System marked a significant step toward bridging the gap between traditional SEO and AI-driven content discovery. By uncovering how ChatGPT sources and prioritizes information from the web, our AI team gained critical insights for optimizing web visibility in conversational AI outputs. This AI-powered analytical framework streamlines SEO research and sets a new standard for integrating AI platform understanding into digital marketing strategy. With the insights gained from this system, digital marketers can refine their strategies to optimize content, improve SEO rankings, and enhance visibility in both Google and AI-driven platforms like ChatGPT.