From invisible to recommended: 0% → 75% AI visibility in one month

The problem: we sold AI visibility and had none

In our baseline audit, AI assistants recommended GridStrat 0 times across 480 pooled prompts. When founders and marketing leaders asked ChatGPT, Gemini, or Claude who could help them with AI visibility, growth strategy, or search representation, they heard competitor names — never ours.

For a firm whose core service is diagnosing and correcting exactly this problem, that was an uncomfortable number. It was also an opportunity: it made GridStrat its own first client, with a dated, checkable before/after that anyone can hold us to.

What we changed

We ran the same loop we sell — audit → governed facts → source correction → publish → measure → iterate:

• Governed facts library. One canonical set of claims — who GridStrat is, what it does, who it serves, what it has proven — used verbatim across every page and profile.
• Rebuilt site architecture. From a template site to a multi-page static build: real URLs, per-page titles and canonicals, a clean sitemap, crawlable paths.
• Answer-first pages. Dedicated pages for each service and methodology, and for the actual questions buyers ask AI assistants — direct answer first, evidence after.
• Structured data. Organization, Person, Article, and FAQ schema, so machines can read what humans read.
• Entity consistency. The same founder and company identity across the site, LinkedIn, press coverage, and a live newsroom.

No advertising. No link buying. About one month of focused correction work between the corrected site going live and the re-audit.

The improvement held across every buying scenario tested, not one lucky prompt type:

Buyer scenario (founder/CEO persona)	Prompts	Mentions	Hit rate
Rising customer-acquisition-cost pain	21	6	28.6%
AI buyer-discovery shift	15	3	20.0%
Simplify vendor management	12	3	25.0%
Budget-cycle pipeline efficiency	12	3	25.0%

What didn't move — and why that matters more

Each assistant retrieves from a different stack. Gemini grounds answers in live Google Search, which picked up our corrected site within days. ChatGPT retrieves through OpenAI's own crawl and Bing-aligned sources, and prefers third-party corroboration — directories, reviews, industry coverage — before it will name a vendor. Claude retrieves through Brave Search. Winning one engine does not touch the other two. (For why the platforms disagree so consistently, see why AI assistants give different answers.)

A single blended "AI visibility score" hides exactly this failure mode. That is why we measure and remediate per engine — and why our own ChatGPT and Claude remediation is now underway, with results to be published here the same way, whatever they show.

Methodology

Testing ran through GridStrat's audit engine on August 18, 2026: 60 pooled prompts for the AI Visibility Diagnostic Sprint offering — 20 per platform across ChatGPT, Gemini, and Claude — generated from a founder/CEO buyer persona in four buying scenarios, with persona and geographic context transmitted alongside each question, the way real assistant sessions carry user context. A mention counts only when GridStrat appears in the assistant's actual recommendation; position is its rank in the recommendation list. Baseline: 0 mentions across 480 pooled prompts on the same engines before the correction work. AI and search visibility vary by location, user profile, prompt wording, model, and time; no ranking guarantees are made.

Frequently asked questions

How was the 75% figure measured?

15 of 20 Gemini prompts for our flagship offering — founder persona, four buying scenarios, August 18, 2026 — at an average recommendation-list position of #1.4.

Why did Gemini improve while ChatGPT and Claude stayed at zero?

Different retrieval stacks: Gemini reads live Google Search; ChatGPT leans on its own crawl plus third-party corroboration; Claude retrieves through Brave. Each engine needs its own remediation path.

What actually produced the lift?

Governed facts, rebuilt page architecture with real URLs and canonicals, answer-first content for real buyer questions, structured data, and a consistent entity footprint. No ads, no link buying.

Is this repeatable for other companies?

It is the same methodology behind our client work — including Cascade Orthotics, lifted from a 7% to an 86% consumer AI mention rate across three audit waves. The Diagnostic Sprint is where every engagement starts.