Schema Markup is How Brands are Understood by AI
AI systems are answering questions about your brand. The question is whether you are providing the data that controls the narrative or leaving it up to chance.
Enterprise teams are increasingly asking how to ensure their brand is accurately represented across AI systems and large language models (LLMs). In this article, we’ll share how Schema App is helping brands get AIO visibility, solve hallucinations and maintain brand control by making their brand information accessible using Schema Markup.
Increasing AI and Search Visibility for Brands
In this AI era, the search landscape is changing so rapidly that executive teams are seeking visibility strategies that must be implemented immediately. Good news, in a recent case study, Schema App saw a 19.72% increase in AIO visibility by implementing connected Schema Markup with linked entities, proving that Schema Markup is driving visibility for brands in AI.
Beyond this case study, we have seen increases in AIO visibility across multiple Enterprise clients after implementing optimized entities with Entity Hub.
Managing Brand Accuracy in AI
Brand accuracy has never been more important, as hallucinations in LLMs can spread misinformation to your consumers. In our recent case study with Wells Fargo, Schema App resolved an AIO hallucination by adding semantic Schema Markup to their location pages. Schema Markup, when connected to the website’s content, improved answer accuracy.
When implemented across a brand’s website, Schema Markup, also known as structured data, provides a control point for how the brand is understood and represented by AI.
The Real Challenge: Structured Data Visibility Across AI Systems
Today, search engines like Google and Bing can execute JavaScript when crawling pages. This means Schema Markup deployed through JavaScript is visible to search and AI systems that rely on those indexes, including tools like Gemini and Copilot. However, not all AI crawlers behave the same way.
Vercel’s real-world crawler data reveals a clear divide: while Googlebot can fully render JavaScript, many AI crawlers—including GPTBot, ClaudeBot, and PerplexityBot—currently do not. These crawlers can only reliably interpret content and structured data included in the initial HTML response. This shift is driving increased demand for server-side and edge-based Schema Markup delivery to ensure consistent AI visibility.
So far, this has had a limited impact. AI referral traffic remains low, and tools like ChatGPT often rely on Google and Bing indexing. But as AI adoption grows and AI-native crawlers evolve, structured data must be accessible beyond JavaScript-rendered environments.
We at Schema App believe that over time, these AI bots will use JavaScript, similar to Google’s journey with it back in 2015. They learnt that in order to understand the web, they needed to execute JavaScript. However, Schema App will not wait and see; instead, we’re investing to give our customers more server-side options than ever.
In addition to our existing CMS integrations with Adobe Experience Manager, Drupal, WordPress, and Shopify, and our existing MCP servers, Schema App is actively working on offering server-side integrations with Akamai and Cloudflare. Schema App is committed to ensuring our customers lead the industry and have the most robust data layer available for search and AI, inside and outside the Enterprise.
Schema App Offers Flexible Integrations Built for Enterprise AI Visibility
Schema App provides multiple integration and deployment options designed to ensure JSON-LD Schema Markup is accessible across search engines, AI crawlers, and emerging agent platforms.
Rather than prescribing a single approach, we support flexibility based on your architecture, security requirements, and goals.
Client-Side Deployment
Client-side deployment continues to work effectively for search and for AI systems that rely on search engine indexes today. For many organizations, this remains the fastest and most practical option since it does not require IT to implement, allowing faster deployment. As part of Schema App’s client-side deployment, we offer robots-only, enabling the Enterprise to define and control which bots can read their data layer.
Server-Side CMS Integrations
For teams that want structured data to be included directly in the initial HTML response, Schema App offers server-side CMS integrations. These ensure structured data is accessible to crawlers that do not execute JavaScript and align with Enterprise governance needs; however do require IT to implement.
Schema App supports the following CMS integrations:
Edge Integrations
For Enterprise environments, Schema App supports server-side rendering at the edge through CDN integrations. While this integration requires IT or DevOps investment, it provides an easy way to bring Schema Markup server-side without a CMS integration.
Akamai integration is expected to be ready by the end of March 2026, with Cloudflare also in active development and expected to be released by the end of March 2026. CloudFront integration is under consideration at the request of our customer base.
Agent-Ready Infrastructure with MCP
Schema App also offers a Model Context Protocol (MCP) interface that enables Enterprises to reuse structured data and the powerful inferencing capabilities that result from it being in a Content Knowledge Graph for internal AI use.
The Schema App data layer resulting from your partnership with Schema App serves as a grounded data source for answer engines.
NLWeb Endpoints in Development
Schema App is actively building NLWeb endpoints to support agentic readiness within the Enterprise. To see NLWeb in action, check out the onsite search on Schema App’s homepage, and look for an NLWeb interface soon within the Schema App application (app.schemaapp.com).
Brand Control Through Schema Markup
Schema App is more than a visibility strategy for search and AI. It is a brand control point.
It allows brands to define how their content and entities are interpreted, which technology can access that information, and how it is consumed.
Customer Feedback Shapes Our Product Roadmap
We are proud to help our customers prepare for the future by giving them options to gain visibility and control their brand across today’s search and AI platforms and those to come.
At Schema App, product decisions start with our customers. Enterprise teams asked how to prepare for AI without breaking search. We responded by expanding server-side, edge, and AI-native delivery options. Teams asked for flexibility across architectures. We delivered integration paths that meet them where they are.
Our agility is our customer’s strategic advantage. We are here to help you win.
Martha van Berkel is the co-founder and CEO of Schema App, a semantic technology company that leverages advanced Schema Markup to build Content Knowledge Graphs for enterprise marketing teams. She focuses on helping marketing teams globally understand the strategic value of Schema Markup and thrive in an ever-evolving digital landscape.
Prior to Schema App, Martha was a Senior Manager at Cisco Systems for 14 years, responsible for the global online support strategy. She holds a degree in Applied Mathematics and Engineering, attended MIT for Innovation and Strategy, is a Mother of two, and is an avid rower.