NLWeb: How Microsoft’s Open Protocol Can Turn Schema into an Engine of AI Visibility

The internet is shifting gears again. For decades, websites existed to attract clicks, rank for keywords, and deliver answers to people. But now, the audience isn’t just human, it’s algorithmic. The same crawlers that once indexed your site are evolving into conversational agents that query it.

Microsoft’s NLWeb (Natural Language Web) sits right at the center of this change. It’s an open-source framework designed to make websites machine-readable, conversational, and interoperable with AI systems. For SEOs and marketers, that means schema markup is no longer just about rich snippets, it’s the connective tissue of visibility itself.

Let’s unpack how NLWeb works, what it means for discoverability, and how to prepare your website for the agentic web that’s coming faster than most realize.

The Shift from Link Graph to Knowledge Graph

For the past 25 years, search engines have treated the web as a link graph, a massive network of pages connected by hyperlinks. Links guided crawlers, and text guided ranking.

But as AI systems grow capable of understanding context, intent, and meaning, the web is becoming something else: a queryable knowledge graph. Instead of simply moving from page to page, systems like ChatGPT, Gemini, and Microsoft Copilot are beginning to ask structured questions and expect structured answers.

That’s where schema markup, and by extension, NLWeb steps in.

The new era of visibility isn’t about optimizing for clicks. It’s about optimizing for machine comprehension. You’re no longer just ranking; you’re being understood.

What Is NLWeb and Why It Matters

NLWeb (Natural Language Web) is Microsoft’s open-source framework that transforms traditional websites into natural language APIs. It lets users and intelligent agents, interact with your content conversationally, as if your site were a chatbot trained on its own data.

Think of it like this:

• Traditional websites present information.

• NLWeb-enabled websites respond to information requests.

Instead of depending on screen-scraping or unstructured crawling, NLWeb uses structured schema data as the backbone of interaction. It takes what your site already communicates through schema.org markup and converts it into a semantic interface, something AI agents can query directly.

That means your site isn’t just being indexed; it’s being integrated into the new agentic ecosystem.

How NLWeb Works: From Schema to Semantic API

Under the hood, NLWeb operates as a multi-step pipeline that converts structured data into a conversational interface.

1. Data Ingestion and Extraction: Schema as the Entry Point

The NLWeb toolkit begins by crawling your site and extracting schema.org markup, ideally in JSON-LD format.

This data, your products, articles, events, people, or locations, becomes the foundation for how your website will be understood by both humans and machines.

Here’s a simple example of a product schema in JSON-LD format that NLWeb would ingest:

When NLWeb encounters structured data like this, it can transform it into a queryable knowledge node, meaning an agent could ask, “Which of EcoHydrate’s products are under $20?” and get a precise, schema-derived answer.

If your site relies only on visual presentation or HTML tags without schema, NLWeb has far less to work with. The difference is the same as giving a librarian an organized catalog versus a pile of unlabeled boxes.

2. Semantic Storage: Moving from Keywords to Meaning

Once your data is collected, NLWeb stores it in a vector database, a format designed for semantic search rather than keyword matching.

Instead of looking for identical words, a vector database recognizes conceptual similarity.

For instance, if your schema includes “structured data,” the system will understand that a query for “schema markup” refers to the same concept. This makes conversational querying possible, because the system understands meaning, not just syntax.

This semantic mapping process is what allows AI agents to “talk to your data” naturally.

It’s also what makes data precision so critical. Inaccurate or incomplete schema leads to semantic confusion, which can generate false or irrelevant responses from agents.

3. Protocol Connectivity: The Role of MCP

Every NLWeb instance operates as an MCP (Model Context Protocol) server, an emerging standard for consistent data exchange between AI systems.

This connectivity ensures your data doesn’t exist in isolation. Instead, it’s part of a broader network where various AI agents can query your site in real time.

It’s like giving your content an API key to the agentic web, a seat at the table where future discovery will happen.

Why Schema Quality Now Defines Visibility

If NLWeb is the bridge, schema markup is the material it’s built from.

In the NLWeb framework, schema is no longer a bonus, it’s the entry ticket. Low-quality or incomplete schema can’t be corrected downstream.

Imagine trying to build a conversation engine on bad data: if your “Person” entities lack proper relationships to “Organization” or “Event,” the responses agents generate could be misleading.

That’s why entity-first schema optimization is now the real technical SEO frontier.

Common Schema Weak Points to Audit

• Disconnected Entities: If “Author,” “Publisher,” or “Organization” types don’t reference each other correctly, context gets lost.

• Minimal Attributes: Using only name and description fields limits the value of your data. Add details like sameAs, identifier, or hasPart.

• Improper Nesting: Ensure nested types like Offer or Review are properly linked to parent objects.

• Static Data: If your schema doesn’t update dynamically, it can quickly become outdated, which damages long-term AI visibility.

Here’s a simple contrast between poor and optimized schema.

Poor Example:

Optimized Example:

The optimized example tells the full story, connecting people, organizations, and publication context, all of which NLWeb can convert into a knowledge graph for meaningful AI interaction.

NLWeb vs. llms.txt: Static Guidance vs. Conversational Protocol

Another emerging concept in the same space is llms.txt, a proposed standard to guide AI crawlers by listing priority pages.

It’s like a robots.txt file for language models, static, simple, and focused on efficiency.

However, llms.txt doesn’t support real interaction. It’s a directory, not a dialogue.

In short, llms.txt helps systems find content; NLWeb helps them use it.

For marketers and SEO teams, that difference is massive. The future favors dynamic data that supports reasoning and transaction, not static directories that list links.

The Strategic Imperative: Audit Your Schema Now

The most actionable takeaway from NLWeb’s framework is this: schema is your API.

Whether or not you deploy NLWeb directly, the principles behind it set the new technical baseline for visibility.

Key Steps for SEO and Development Teams

• Run a Full Schema Audit: Validate your JSON-LD using tools like Google’s Rich Results Test and Schema.org Validator.

• Prioritize Entity Interconnectivity: Every person, product, or organization should connect logically within your schema.

• Use sameAs Links Liberally: Link entities to verified external profiles, Wikipedia, LinkedIn, Crunchbase, or official websites.

• Adopt Version Control for Schema: Treat your structured data like code. Track changes and ensure consistency.

• Test for Conversational Queries: Simulate how an agent might ask questions about your site’s content. Adjust schema until answers are contextually correct.

High-quality schema doesn’t just help search rankings. It prepares your content for interaction, which is the next frontier of visibility.

Why NLWeb Future-Proofs Your Digital Strategy

For now, NLWeb is still an emerging standard. But its potential is clear.

By turning websites into queryable endpoints, it bridges the gap between static content and interactive data. This allows brands to:

• Extend their schema investment into new forms of interaction

• Reduce friction by providing direct, intelligent answers

• Strengthen long-term brand authority as a structured knowledge source

This isn’t about chasing the next SEO trend. It’s about ensuring your digital presence remains accessible to both people and machines in a rapidly changing ecosystem.

The organizations that win in the next five years will be those that treat schema as infrastructure, not decoration.