Introducing DeepSweep: How YEN Measures Your Real AI Search Visibility

GEO

Generative AI will never give two users the exact same answer, and that single fact has created an urgent measurement problem for businesses trying to track their AI visibility. You're the Expert Now (YEN) built DeepSweep, a Generative Engine Optimization (GEO) tracking tool that prompts your target queries up to 100 times across ChatGPT, Gemini, and Claude to calculate a business's true Mention Rate, Top 3 Rate, and competitive Rank — rather than trusting a single, unreliable AI response.

TL;DR

• Generative AI answers are non-deterministic: the same prompt asked twice can return different businesses in a different order.

• A single AI response is not a reliable measurement — it's a data point that must be treated statistically, not as fact.

• DeepSweep runs each query up to 100 times per platform across ChatGPT, Gemini, and Claude to calculate Mention Rate, Top 3 Rate, and competitive Rank.

• A live DeepSweep audit of a Buffalo pest-control company found its AI visibility ranged from 70.2% on one query to 2.9% on another — a 24x swing for the same business.

• Published research backs the approach: peer-reviewed studies show LLM answer consistency can range from roughly 35% to 99.7% depending on the model.

• Presence (does the AI name you?) and prominence (where does it rank you?) are separate problems, and only frequency-based tracking reveals which one a business actually needs to fix.

Introduction

Search is shifting from a page of ranked links to a single synthesized answer. When a customer asks ChatGPT, Gemini, Claude, or Perplexity for a recommendation, the model names a handful of businesses and stops there — there is no page two. If a business isn't in that handful, it's effectively invisible to that customer, and unlike traditional SEO, a business has little direct control over when or how it surfaces.

Generative Engine Optimization (GEO) is the discipline built to measure and improve that visibility. But measuring it turns out to be harder than it looks, because of one inconvenient property of generative AI that most businesses don't realize: ask the same question twice, and you can get two different answers.

That's the problem YEN designed DeepSweep to solve.

The Core Problem: AI Answers Aren't Reproducible

When you send an identical prompt to an LLM multiple times, you get different responses — sometimes subtly different, sometimes dramatically so. This comes from sampling randomness, floating-point calculation quirks, and concurrent processing on the servers running the model. It isn't a bug that gets switched off in a consumer product; it's baked into how these systems generate text.

Published research quantifies just how unstable this can be:

• Small models are the least consistent. In one repetition study, models with 2–8 billion parameters were asked the same multiple-choice question 10 times each. The share of questions answered the same way every time fell in the 50–80% range, even at low temperature settings.

• Consistency varies wildly even between models with similar accuracy. A 20-run study found the most-frequent-answer consistency ranged from roughly 35% for one widely-used model up to about 99.7% for another — despite the two models scoring similarly overall. Two AI tools can be equally "smart" and still wildly disagree with themselves from one run to the next.

• Longer answers are less stable. Multiple studies found that as an AI's response gets longer, the variance in what it actually says — including which businesses it names — increases.

• Even "deterministic" settings aren't fully deterministic. At temperature zero, where a model should theoretically repeat itself exactly, researchers still observed measurable instability in the final parsed answer.

The implication for any business trying to track its AI visibility is direct: a single query to ChatGPT is not a measurement, it's a sample. If you ask "who's the best pest control company in Buffalo" once and see your name, that tells you almost nothing about how often you'd be named across the next hundred people who ask the same thing.

How DeepSweep Solves the Measurement Problem

DeepSweep is built around the only statistically honest approach to this problem: ask each prompt many times, across each model, and report the frequency — not a yes/no.

The tool prompts a business's target, high-intent customer queries up to 100 times using the latest generative AI models with grounded search enabled, across the three platforms that matter most for local and commercial search:

• ChatGPT

• Gemini

• Claude

From that repeated sampling, DeepSweep calculates three core performance indicators for every business it tracks:

• Mention Rate — the percentage of AI responses that name the business at all

• Top 3 Rate — how often the business lands in the first three names when it is mentioned

• Rank — where the business sits on a competitive leaderboard against every other business surfaced for that query

Because the underlying AI answers vary run to run, DeepSweep reports these numbers with a statistical range rather than a single hard figure — the honest version of "your Mention Rate is 39.8%" is "39.8%, and if we ran it again we'd expect somewhere between 36% and 43%." A one-shot measurement could land anywhere in that band and mislead a business about where it actually stands.

What a Real Audit Reveals

A recent DeepSweep audit of a Buffalo pest-control company — 708 AI responses across 8 high-intent local queries and all three platforms — shows exactly why this approach matters.

The business ranked #2 of 53 competitors overall, with a 39.8% Mention Rate and an 82.3% Top 3 Rate when named. On paper, that's a strong, stable position. But breaking the same business down by individual query told a very different story:

• On "best tick control services," it was named in 70.2% of responses.

• On "best bee exterminator," it was named in just 2.9% of responses.

That's a 24x spread in visibility for a single business, invisible to any tool that reports only one overall number. Just as notably, the business's average position stayed strong (roughly 1st to 4th place) across nearly every query, even where its mention rate collapsed. In other words: when the AI does name the business, it ranks it well. The problem isn't placement — it's presence. The models simply aren't naming the business at all for certain query types.

That distinction — presence versus prominence — is the kind of finding a single spot-check can never surface, and it's exactly the kind of finding that tells a business where to focus its next content push.

Why This Matters for Every Business

The traditional pay-per-click advertising playbook was built for a search results page. That page is disappearing for a growing share of searches. Roughly 35% of U.S. consumers already use AI at the product-discovery stage, and while organic search traffic has fallen since generative AI became widespread, impressions inside AI-generated answers have risen sharply. Customers are increasingly reading about businesses inside an AI answer, not clicking through to a website to find them — which means the metric that matters is shifting from traffic to citation share.

Without a tool like DeepSweep, a business asking "am I visible in AI search?" and typing one question into ChatGPT is asking the wrong kind of question. The answer they get back is one noisy sample from a system that's designed to vary. DeepSweep replaces that guesswork with a statistically grounded answer: how often, where, and against which competitors.

Frequently Asked Questions

What is DeepSweep?

DeepSweep is a GEO visibility tracking tool built by You're the Expert Now (YEN) that prompts a business's target customer queries up to 100 times across ChatGPT, Gemini, and Claude to calculate reliable Mention Rate, Top 3 Rate, and competitive Rank metrics.

Why can't I just ask ChatGPT if my business shows up?

Because generative AI is non-deterministic — the same question asked twice can return different businesses in a different order. A single response is one noisy sample, not a measurement. Published research shows answer consistency across repeated identical prompts can range from roughly 35% to 99.7% depending on the model.

What do Mention Rate, Top 3 Rate, and Rank actually measure?

Mention Rate is the percentage of AI responses that name a business at all. Top 3 Rate is how often it lands in the first three names when mentioned. Rank is its competitive position on a leaderboard against every other business the audit surfaces for that query. Together they separate whether an AI names a business (presence) from where it places that business (prominence).

How many times does DeepSweep test each query?

Up to 100 times per query, per platform, across ChatGPT, Gemini, and Claude, with grounded search enabled — the repetition is what makes the resulting percentages statistically meaningful rather than a single guess.

What did a real DeepSweep audit find?

An audit of a Buffalo pest-control company across 708 AI responses found a 24x spread in visibility between its best-performing query (70.2% Mention Rate) and its worst (2.9%), even though its average ranking position stayed strong throughout — showing the business had a presence problem, not a prominence problem, on specific service queries.

How is this different from traditional SEO tracking?

Traditional SEO tools measure ranked page positions and clicks on a search engine results page. DeepSweep measures how often and where a business is named inside a synthesized AI answer — a fundamentally different, and increasingly more important, kind of visibility as customers shift from clicking search results to reading AI-generated recommendations directly.