CEO and Co-founder of @schemaapptool. Loves helping companies stand out in search! Optimist, Queen's & MIT Grad, Mom, Entrepreneur, Rower & chocolate addict!
“To succeed in AI search, focus on clarity, context, and coherence in your schema markup.”
Our CEO @marthavanberkel shares actionable strategies for enterprise marketers in this feature with @WixStudio 👇
Read the article:
na3.hubs.ly/y0nvSB0
We’re seeking a Full Stack Developer to help build intuitive, performant, and scalable applications across our front-end and back-end systems.
Learn more & apply today 🔗
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AI is speaking on behalf of your brand. But does it actually understand your business?
Hybrid Graph-Vector RAG helps you stay in the loop by giving AI structured, brand-owned knowledge that you control, and it can actually understand.
Learn more 👇
na3.hubs.ly/y0hnjy0
MasterControl prepared early for AI search by partnering with Schema App.
The results:
✔️+29% in domain-wide search visibility
✔️+39% in blog impressions with entity linking
✔️More AI Overviews & LLM citations on Schema Markup supported pages
Learn more👉na3.hubs.ly/y0hqn60
In our upcoming webinar, Diane Hammons of @WGContent and @marthavanberkel of Schema App break down how human-centred storytelling, schema markup, and content knowledge graphs work together to build trust and authority in AI-driven discovery.
Register👇
na3.hubs.ly/y0dJ6-0
Our CEO, @marthavanberkel, will be speaking at @brightonSEO this September 15-16 at the San Diego Convention Center.
Interested in attending and don't have a ticket yet? You’ve got 48 hours only (Jan 14–16) to enter for a complimentary day pass!
👉 na3.hubs.ly/y0g7Df0
Martha van Berkel (@marthavanberkel) explains that relationships and accurate data are key for LLMs because they excel when they can infer connections between data points, making accurate data and defined relationships crucial for reliable results.
maj.to/4rlK7Vp
Martha van Berkel (@marthavanberkel) wants you to move beyond using schema for visibility and see it as a way to create a knowledge graph that shapes how large language models perceive your brand.
maj.to/4rlK7Vp
Please read the whole thing. It's just 3 points.
1. LLMs *do understand schema* when they see it in input, contrary to a popular belief that the structured data is lost due to tokenization. This is because they're trained on it as they are trained on code.
2. LLMs *don't* always see schema in input because most tools and functions supplying context to the model strip it away during pre-processing.
(The next part is where SEO people get confused.)
3. The tools AI systems use to process content which is passed as context to LLMs sometimes parse the HTML page by using structured data to better understand the page and sometimes they don't. It's a software design choice and nothing to do with LLM's capability.
TL;DR: LLMs understand structured data if they get it, but most of the time they get plain text / markdown, which *may* or *may not* have been rendered by leveraging schema at the pre-processing level.
Don't miss the chance to get a ticket to #brightonseo before the prices go up! Even better - you can save 10% off the already low price with my discount code here: ti.to/roughagenda/brightonse…
🚨 Entity Hub is live! 🚨
Take control of how search and AI understand your brand.
Schema App’s Entity Hub helps enterprise teams build a high-quality Content Knowledge Graph and move beyond keywords into true semantic SEO.
Learn more → na3.hubs.ly/y04xl10