How APIs Unlock Better Insights Into AI Search Visibility

A recent study from SurferSEO compared API responses to scraped web interface results across 1,000 ChatGPT prompts. They found the overlap between brands mentioned using the two methods was 24%. Their conclusion: "Monitoring responses from API as a proxy for your AI visibility is totally wrong."

At Gumshoe, we read that study with interest. The data is real, but the conclusion misses the point.

What the Study Actually Shows

SurferSEO found significant differences between raw API calls and scraped interface results:

• Response length: API averaged 406 words versus 743 words from the interface
• Source citations: API provided sources 75% of the time; scraped results always included them
• Brand detection: API missed brands 8% of the time; scraped results caught them consistently

These gaps exist because the researchers compared raw API calls (with default parameters) against the ChatGPT web interface. The web interface applies system prompts that shape response length, formatting, citation behavior, and tone. Raw API calls without those prompts produce different output.

The study demonstrates that default API settings differ from interface behavior ,but it does not demonstrate that APIs cannot replicate interface behavior.

System Messages Control Output Patterns

The ChatGPT web interface is an API client. As such, it sends system messages that instruct the model how to respond. Those instructions determine whether responses include citations, how long they run, and what format they take.

SurferSEO's own methodology acknowledged this because they included "a leaked OpenAI system prompt from GitHub" as a third test condition. The existence of leaked prompts confirms that interface behavior is driven by system messages, which can be reverse-engineered and replicated. However, their failure to replicate ChatGPT's behavior via the API using this leaked prompt does not mean replication is impossible.

Research from Ma et al. (2025) used controlled RAG API experiments to study citation patterns in generative search. They found that citation preferences are "intrinsic LLM tendencies" that persist across interfaces when properly controlled. The API didn't produce different results because it was an API but because the system configuration differed.

Why API Access Matters for GEO

Once you replicate interface behavior through system message engineering, API access unlocks what scraping cannot: personalization at scale.

The Personalization Gap in Anonymous Scraping

ChatGPT has over 800 million weekly active users. Most are logged in with access to memory features, custom instructions, chat history, and the latest models. Logged-in users receive personalized responses shaped by their interaction history.

Anonymous scraping captures what logged-out users see, an experience that differs substantially from what logged-in users receive:

• No conversation history or continuity
• No memory or personalization
• No custom instructions
• Restricted to GPT-5 Instant rather than flagship models

Research on traditional search found that 11.7% of results differ due to personalization (Hannak et al., 2013), and that figure understates the variation in AI interfaces, where memory, custom instructions, and conversation history compound the effect. SEO strategist Lily Ray has documented how this creates an "accuracy crisis" for visibility tracking: the same query generates substantially different results for different users based on location, interests, and previous interactions (Ray, 2025).

To get personalized scraped results that match real user behavior, you need sock puppet accounts: fabricated identities with maintained history, preferences, and activity patterns. But platforms detect sock puppets with 89-95% accuracy using behavioral analysis (Yamak et al., 2018). The method is resource-intensive, ethically questionable, and fragile.

API Persona Simulation

API system messages can replicate each of these personalization features in a controlled, deterministic way. User memory is injected directly into the system prompt, while custom instructions map cleanly to system-level guidance. Conversation history can be simulated or seeded from real user sessions, enabling realistic end-to-end scenarios.

For example, you can model a CMO researching marketing automation, a developer evaluating API documentation, or a small business owner comparing accounting software, and then validate these synthetic personas against observed real-world behavior.

How We Approach This at Gumshoe

We invest significant effort in replicating web interface response patterns through API system messages. By engineering prompts that match interface behavior (response length, citation format, source inclusion), our API-based measurements align with what users actually experience.

We then validate against native interface queries with this dual approach confirming that our system message engineering accurately replicates real-world behavior.

The Right Frame for This Debate

The question isn't "API versus scraping" becase raw API calls with default settings will differ from interface scrapes. That finding is expected and not particularly useful.

The productive question is: can you engineer API calls to replicate interface behavior while gaining the nuance of personalized results?

We believe so. System messages control output patterns which means that persona prompts enable user segment modeling.

For brands tracking their visibility in AI search, this methodology provides actionable measurement. You can isolate variables, test across personas, and track changes over time with confidence that your measurements reflect what users actually see.

Visit https://www.gumshoe.ai/ to understand how your brand appears across AI search engines.

By Nicholas Clark

Nick Clark is an AI researcher and PhD student at the University of Washington focused on epistemic aspects of large language models and user interaction with knowledge systems. He combines academic rigor with practical impact through roles at Gumshoe AI, while mentoring early-stage startups at the iStartup Lab.

Sources:

• Hannak, A., Sapiezynski, P., Molavi Kakhki, A., Krishnamurthy, B., Lazer, D., Mislove, A., & Wilson, C. (2013). Measuring personalization of web search. Proceedings of the 22nd International Conference on World Wide Web (WWW '13), 527–538. https://doi.org/10.1145/2488388.2488435
• Ma, L., Qin, J., Xu, X., & Tan, Y. (2025). When content is Goliath and algorithm is David: The style and semantic effects of generative search engine. arXiv preprint arXiv:2509.14436. https://arxiv.org/abs/2509.14436
• Ray, L. (2025, November 3). LLM tracking tools face accuracy crisis from personalization features [Post]. X. https://x.com/liloray
• Yamak, Z., Sauber, J., & Dumontier, M. (2018). SocksCatch: Automatic detection and grouping of sockpuppets in social media. Knowledge-Based Systems, 149, 124–136. https://doi.org/10.1016/j.knosys.2018.02.020