We Installed an Entitymap on Waikay.io. Here’s What Happened.
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.
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.
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.
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.
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.
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 score oscillated between 91 and 96 for three months before install, and between 94 and 97 after. There was no room for meaningful improvement and nothing meaningfully improved. This is the expected result, and it is what gives the other findings credibility.
This topic was trending upward before install and kept climbing afterward, reaching a new high of 97. Because the score was already rising, we cannot credit the entitymap with the full lift.
Three readings flat in the low 80s, drifting slightly downward. The prior trend predicted the next reading would land near 80. It landed at 92: a 10-point jump in the first window after install, with no pre-existing momentum to explain it.
The score had already jumped from 48 to 71 in the month before install, probably from content and linking changes around the Fact Tracker product page. It then added another 10 points in 48 hours. The pre-install rate was +23 in 30 days. The post-install rate was +10 in 2 days, faster per day, suggesting the entitymap contributed. We cannot call it the sole cause.
58 to 84 in 48 hours
The score had been falling by roughly 10 points a month for three months with no clear cause. Gemini was increasingly answering “Waikay and hallucinations” queries using weaker sources. Forty-eight hours after the entitymap went live, it reversed the entire decline. New content takes weeks to index. Backlinks accumulate over months. A 48-hour reversal points at live retrieval as the mechanism, and that is exactly what the entitymap is designed to influence.
| Topic | Pre-install trend | Score change | Speed |
|---|---|---|---|
| Brand overview | High and stable (91-96) | ~+1 | n/a |
| AI brand visibility | Rising (85 to 92) | +5 | Weeks |
| AI Fact Tracker | Rising fast (48 to 71) | +10 | 2 days |
| AI search optimisation | Flat and drifting down (86 to 82) | +10 | 10 days |
| AI hallucinations | Collapsing (81 to 58) | +26 | 2 days |
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.
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.
| Page | Share |
|---|---|
| aio-guide | 8.3% |
| features | 8.3% |
| waikay-blog | 8.3% |
| entitymap.html | 6.5% |
| metrics-to-track | 6.0% |
| brand-visibility-tracker | 5.4% |
| reviews | 4.8% |
| academy-learning | 4.2% |
| about | 3.0% |
| Page | Share |
|---|---|
| features | 6.6% |
| technology-arch | 5.3% |
| aio-guide | 5.3% |
| entitymap.html | 3.9% |
| ai-audit | 3.9% |
| waikay-blog | 3.9% |
| faq | 3.9% |
| fact-tracker | 2.6% |
| about | 1.3% |
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.
Sonar cites more pages per answer than Gemini: 46 distinct URLs across 76 citations, versus Gemini’s more concentrated pattern. That pushes all individual citation shares down on Sonar. The entitymap’s 3.9% looks lower than its 6.5% on Gemini, but the ratio against the About page is actually larger, because the About page suffers the same dilution while the entitymap holds its position.
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.
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.
Three things the current data does not support:
Sole cause on rising topics
AI Brand Visibility and AI Fact Tracker were already climbing before install. We cannot separate the entitymap’s contribution from the work already underway.
Works on all models
Strong evidence on Gemini, good evidence on Sonar, zero evidence on the Bing stack, because Bing has not indexed the file.
A settled new standard
One strong result on one surface is not a standard. Wider conclusions depend on replication by other practitioners.
If You Want to Test This Yourself
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.
Copy the prompt, fill in your brand details, and paste the output into the appropriate files on your site.
View the full prompt template on GitHub →
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.
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.
Submit to Bing before you launch
Bing Webmaster Tools submission, sitemap.xml inclusion, and a homepage link. Do this first. We did not, and the Bing-stack models never saw the file.
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.
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.
Find out if Gemini is citing you, or something else
We are publishing a follow-up once the Bing indexation is in place. The results so far are strong enough to act on. They are not strong enough to call settled. Waikay Entitymap is coming as a paid service. Join the waitlist to be first in line.
Read the entitymap documentation →