We Installed an Entitymap on Waikay.io. Here’s What Happened.

TL;DR

Waikay.io installed an entitymap, a structured machine-readable brand knowledge graph file, on April 25, 2026, and measured its impact on how AI models answer queries about the brand. By June 1, across Google-indexed AI surfaces (Gemini and Sonar/Perplexity), results were strong: AI Knowledge Scores improved by up to 26 points in as little as 48 hours, and the entitymap was cited 2.2-3.0x more often than the site’s own About page. Results on Bing-dependent models (ChatGPT, Copilot) are still pending indexation.

+26 pts

AI Visibility Score lift on hallucinations topic in 48 hours

2.2x

Entitymap cited more than About page (Gemini)

3.0x

Entitymap cited more than About page (Sonar)

Section 01

What Is an Entitymap and Why Does It Exist?

When an AI answers a question about your brand, it does not read your website the way a person would. It looks for entities: specific facts, relationships, and claims it can retrieve and stitch together into an answer. If those entities are scattered across unstructured pages, the AI fills in the gaps from wherever it can: competitor content, review sites, old press coverage, or nothing at all.

An entitymap is a single structured file that describes your brand as a knowledge graph. It lists your entities (the brand, its products, key concepts, the people behind it), the relationships between them (PRODUCED_BY, OFFERS, TARGETS, MEASURES), and source-tagged content chunks that tie each fact to a specific URL on your site.

Not a sitemap

A sitemap tells crawlers which pages exist. An entitymap tells AI models what your brand means, what it does, and how its parts relate to each other.

Not Schema.org

Schema.org marks up individual pages. An entitymap describes the whole brand as a connected graph, with every claim sourced to a URL.

Built for live retrieval

Models like Gemini fetch pages at query time. An entitymap gives them one authoritative source rather than fragments from across the web.

What we wanted to find out

We deployed an entitymap on waikay.io on April 25, 2026 and tracked what happened to our AI Visibility Scores across five topics over the following five weeks. This case study is the result. The entitymap was the only change we made during the measurement window.

Section 02

What Is an AI Visibility Score?

When an AI like Gemini or ChatGPT answers a question about your brand, it draws on entities: specific facts, concepts, products, and claims it has retrieved about you. Your AI Visibility Score measures how much of what the AI says about you matches what is actually on your website.

Think of it as a Venn diagram. The left circle is everything on your website. The right circle is everything the AI says about you. The score (0-100) measures the size of the overlap. A score of 95 means the AI’s picture of your brand closely matches your own. A score of 58 means a lot of what it says is missing, wrong, or pulled from somewhere else.

How we measure it

We query Gemini in live retrieval mode with prompts like “What do you know about Waikay in relation to [topic]?” Live retrieval means Gemini fetches pages from the web at query time rather than relying only on training data. A change a live retrieval model can read today can affect answers within 48 hours, far faster than anything requiring a model to be retrained.

We track scores monthly on five topics, with readings going back to early 2026. The entitymap went live on April 25, 2026 as the only change we made during the measurement window.

Section 03 Key data

Five Topics, Five Different Stories

Each chart shows the AI Visibility Score history for one topic. The Y-axis is zoomed to the relevant range so changes are clearly visible. The red dashed line marks April 25, when the entitymap went live.

The pattern

The entitymap pulls scores toward a 90-97 ceiling. Topics already there stay there. Topics far below it, particularly those that were flat or actively declining, see the largest and fastest improvements. The effect is strongest precisely where Gemini had been pulling weaker sources into its answers.

Section 04

What’s Actually Getting Cited

Beyond scores, we tracked which URLs Gemini and Sonar (Perplexity) cite when answering brand questions about waikay.io. The entitymap row is highlighted in both tables.

Gemini: 168 total citations

Sonar: 76 total citations

The About page is the URL every SEO playbook says should win brand queries. In both models, a single 30KB structured file is cited more often: 2.2x on Gemini, 3.0x on Sonar.

Section 05

Where It Doesn’t Work Yet

The entitymap is not indexed by Bing. That single fact explains why ChatGPT, Copilot, and Claude never cite it. Those models are not rejecting the format. They have never seen the file. Until Bing crawls it, we have no evidence about how the format performs on those surfaces.

The fix is straightforward
Submit via Bing Webmaster Tools, add to sitemap.xml, and link from the homepage. We skipped all three before deploying. It is roughly a 20-minute job. We will publish updated cross-model data once it is done.

Section 06

If You Want to Test This Yourself

1. Create your entitymap files
   You need two files: entitymap.html and entitymap.json. Use the prompt templates from the entitymap project to generate both with any major AI model.

2. Baseline first
   Track AI Visibility Scores on 5-10 topics for at least 2-3 months before you deploy anything. Without a baseline, post-install changes are uninterpretable.

3. Deploy on one date, change nothing else
   A clean before/after requires a single intervention. Do not run other content or linking changes in the same window.

4. Submit to Bing before you launch
   Bing Webmaster Tools submission, sitemap.xml inclusion, and a homepage link. Do this first.

5. Watch the low-scoring topics
   That is where the effect shows up fastest. Topics already near the ceiling will not move. That is the expected result, not a failure.

6. Your entitymap should beat your About page within 6-8 weeks
   If it is not being cited more often than your About page for brand queries by then, check indexation and internal linking first.