---
description: We're open-sourcing the AI Discovery Protocol (ADP) — a new standard
  that makes websites discoverable to AI systems like ChatGPT, Claude, Perplexity,
  and Gemini. Unlike traditional SEO, ADP provides structured, machine-readable metadata
  specifically designed for AI reasoning engines.
resource: https://pressonify.ai/blog/introducing-ai-discovery-protocol
tags:
- AI Discovery Protocol
- ADP
- open source
- AI infrastructure
- LLMO
- AEO
- structured data
- knowledge graph
timestamp: '2025-11-02T10:00:00Z'
title: 'Introducing the AI Discovery Protocol: Making Websites Discoverable to AI
  Systems'
type: article
---

# Introducing the AI Discovery Protocol: Making Websites Discoverable to AI Systems

## TL;DR

We're open-sourcing the **AI Discovery Protocol (ADP)** — a new standard that makes websites discoverable to AI systems like ChatGPT, Claude, Perplexity, and Gemini. Unlike traditional SEO (built for keyword-based search), ADP provides structured, machine-readable metadata specifically designed for AI reasoning engines.

**The core innovation:** A single entry point (`/ai-discovery.json`) that maps your entire AI-optimized content ecosystem, combined with versioned entity catalogs and incremental update support.

**Released under MIT License** — free for anyone to use, implement, and build upon.

> **How to think about ADP (the two layers).** ADP serves two distinct jobs, and it helps to keep them separate. The first is **Be Cited** — making your content discoverable and citable by answer engines. The second is **Be Actionable** — exposing your content and capabilities to autonomous AI agents (the agentic/MCP side, with WebMCP on the roadmap). Different endpoints sit on different sides: the *context* files (notably llms.txt) are agent-**navigation** aids on the actionable side, not citation-ranking levers — Google Search has since stated llms.txt isn't needed for its AI Search features. What actually moves citations leans on entities and brand authority: brand mentions correlate with AI citation at **0.664** versus **0.218** for backlinks (Ahrefs, 75k brands), and owned newsrooms account for roughly **18%** of ChatGPT citations versus **~0.04%** for syndicated wire content (BuzzStream). ADP is the infrastructure that makes both jobs possible.

---

## The Problem: Traditional SEO Doesn't Work for AI

For 25 years, SEO has been optimized for Google's web crawlers:

- **Keyword matching** → AI systems query structured entity catalogs
- **HTML pages** → AI needs JSON-LD entities
- **Backlink analysis** → AI reasons over entity relationships
- **PageRank heuristics** → AI requires semantic context

**The fundamental mismatch:** Traditional SEO assumes keyword-based indexing. AI systems work with **entity graphs** and **structured knowledge**.

### Real-World Example: Press Release Distribution

At Pressonify.ai, we publish professional press releases in 60 seconds using Claude Sonnet 4.5. But we noticed something: our customers' press releases were invisible to AI search engines.

Why? Because AI systems don't know:
- Where to find structured entity data
- How to detect content updates
- Which entities exist on the site
- How entities relate to each other

Traditional `robots.txt` blocks crawlers. `sitemap.xml` lists URLs. But **nothing tells AI systems where to find machine-readable entity catalogs**.

---

## The Solution: AI Discovery Protocol

### Core Architecture

```
website.com/
├── ai-discovery.json          # Meta-index (ENTRY POINT)
├── knowledge-graph.json       # Entity catalog
├── llms.txt                   # AI-readable context
└── robots.txt                 # Crawler directives
```

### The Discovery Flow

```
AI System → GET /ai-discovery.json
          ↓
       Parse meta-index
          ↓
       ┌────────────┬──────────────┬─────────────┐
       ↓            ↓              ↓             ↓
  knowledge-   llms.txt      robots.txt    Other
  graph.json                               endpoints
```

**Key insight:** AI systems hit **one canonical file** first, then discover all other resources from the meta-index.

---

## Why This Works: Design Principles

### 1. **Single Entry Point**

Unlike scattered Schema.org markup across 100 HTML pages, ADP provides **one canonical file** at `/ai-discovery.json`.

**Before (Traditional SEO):**
```
AI System → Crawls 100 HTML pages
          → Parses embedded JSON-LD
          → Reconstructs entity graph
          → Misses 40% of entities
```

**After (ADP):**
```
AI System → GET /ai-discovery.json
          → Discovers knowledge-graph.json
          → Loads complete entity catalog
          → 100% entity coverage
```

### 2. **Versioning & Change Detection**

Traditional web crawlers re-index entire sites daily. **Wasteful** for large sites with infrequent updates.

ADP adds semantic versioning to knowledge graphs:

```json
{
  "@type": "KnowledgeGraph",
  "version": "2.7.1",
  "generatedAt": "2025-11-02T11:30:00Z",
  "changeLog": {
    "changes": [
      {
        "timestamp": "2025-11-02T11:30:00Z",
        "entityType": "Product",
        "entityId": "https://site.com/products/widget-pro",
        "changeType": "updated",
        "modifiedFields": ["price", "availability"]
      }
    ]
  }
}
```

**Result:** AI systems can do **incremental updates** instead of full re-crawls.

### 3. **Progressive Enhancement**

Sites can implement ADP at three levels:

| Level | Files Required | Implementation Time | Benefits |
|-------|---------------|---------------------|----------|
| **Level 1: Minimal** | `ai-discovery.json` only | 15 minutes | Signals AI-friendly intent |
| **Level 2: Standard** | + `knowledge-graph.json` + `llms.txt` | 2-4 hours | Full AI discoverability |
| **Level 3: Advanced** | + Versioning + Change logs | 1-2 days | Incremental crawling support |

**No all-or-nothing requirement.** Start small, scale incrementally.

---

## How We Built This at Pressonify

### The Journey

1. **January 2025:** Implemented static `llms.txt` (Jeremy Howard's proposal)
2. **June 2025:** Added Schema.org JSON-LD to press release pages
3. **October 2025:** Built dynamic `/knowledge-graph.json` endpoint
4. **November 2025:** Synthesized learnings into unified protocol

### Production Stats (Pressonify.ai)

- **Entity count:** 132 entities (NewsArticles, Organization, Products, FAQs)
- **Update frequency:** Daily (automated from database)
- **Knowledge graph size:** ~45KB (gzipped: 12KB)
- **Implementation time:** 6 hours (with FastAPI backend)

### The Breakthrough Moment

We tested whether Claude (via web search) could discover our press releases. Results:

**Before ADP:**
- ❌ Claude found 2 out of 27 press releases
- ❌ Couldn't identify company relationships
- ❌ No awareness of recent updates

**After ADP:**
- ✅ Claude found 27 out of 27 press releases
- ✅ Correctly identified entity relationships
- ✅ Surfaced most recent press releases first

**The difference?** A structured entity catalog instead of scattered HTML pages.

---

## Technical Deep Dive

### 1. ai-discovery.json (Meta-Index)

**Purpose:** Single source of truth for all AI discovery resources.

**Example:**

```json
{
  "$schema": "https://pressonify.ai/schemas/ai-discovery/v1.0.json",
  "version": "1.0.0",
  "generatedAt": "2025-11-02T12:00:00Z",
  "website": {
    "url": "https://example.com",
    "name": "Example Corporation",
    "description": "Leading provider of example products"
  },
  "endpoints": {
    "knowledgeGraph": {
      "url": "https://example.com/knowledge-graph.json",
      "format": "application/ld+json",
      "lastModified": "2025-11-02T11:30:00Z",
      "entityCount": 132,
      "version": "2.7.1"
    },
    "contextDocument": {
      "url": "https://example.com/llms.txt",
      "format": "text/markdown",
      "sections": ["Overview", "Products", "Press Releases"]
    }
  },
  "capabilities": {
    "supportsVersioning": true,
    "supportsIncrementalUpdates": true,
    "updateFrequency": "daily"
  }
}
```

**Why this matters:**
- AI systems request **one file** to discover everything
- Metadata shows freshness without parsing full knowledge graph
- Extensible: Add new endpoints as they're created

---

### 2. knowledge-graph.json (Entity Catalog)

**Purpose:** Complete catalog of all entities on the site using Schema.org vocabularies.

**Example (truncated):**

```json
{
  "@context": "https://schema.org",
  "@type": "KnowledgeGraph",
  "version": "2.7.1",
  "generatedAt": "2025-11-02T11:30:00Z",
  "statistics": {
    "totalEntities": 132,
    "byType": {
      "Organization": 1,
      "Product": 45,
      "NewsArticle": 78,
      "Person": 5
    }
  },
  "@graph": [
    {
      "@type": "Organization",
      "@id": "https://example.com/#organization",
      "name": "Example Corporation",
      "url": "https://example.com",
      "sameAs": [
        "https://twitter.com/example",
        "https://linkedin.com/company/example"
      ]
    },
    {
      "@type": "NewsArticle",
      "@id": "https://example.com/news/product-launch",
      "headline": "Example Corp Launches Widget Pro",
      "datePublished": "2025-11-01T09:00:00Z",
      "author": {
        "@id": "https://example.com/#organization"
      },
      "url": "https://example.com/news/product-launch"
    }
  ]
}
```

**Innovation: Change Detection**

```json
"changeLog": {
  "lastModified": "2025-11-02T11:30:00Z",
  "changes": [
    {
      "timestamp": "2025-11-02T11:30:00Z",
      "entityType": "Product",
      "entityId": "https://example.com/products/widget-pro",
      "changeType": "updated",
      "modifiedFields": ["price", "availability"]
    }
  ]
}
```

AI systems can check `changeLog` and **only re-crawl updated entities**.

---

### 3. llms.txt (Context Document)

**Purpose:** Human-readable Markdown providing context for AI systems.

**Example:**

```markdown
---
version: 1.0.0
lastModified: 2025-11-01T10:00:00Z
---

# Example Corporation

> AI-Optimized Content for Large Language Models

## Overview

Example Corporation is a leading provider of professional widgets.
Founded in 2020, we serve over 10,000 customers worldwide.

## Products

### Widget Pro
- **Price:** $299 USD
- **Features:** Advanced automation, real-time analytics
- **Use Cases:** Enterprise widget management

## Recent Press Releases

### Product Launch: Widget Pro v2.0 (November 1, 2025)
Example Corp today announced Widget Pro v2.0 with AI-powered automation...

Read more: https://example.com/news/product-launch

## Contact

- **Website:** https://example.com
- **Email:** support@example.com
```

**Why Markdown?**
- AI systems process Markdown natively
- Human-readable (developers can edit without tools)
- Supports rich formatting (headings, lists, links)
- No HTML/CSS overhead

---

## Real-World Use Cases

### 1. E-commerce (Shopify Stores)

**Problem:** Product catalogs are invisible to AI shopping assistants.

**Solution:** Generate `knowledge-graph.json` from Shopify GraphQL API:

```json
{
  "@graph": [
    {
      "@type": "Product",
      "@id": "https://mystore.com/products/handmade-mug",
      "name": "Handmade Ceramic Mug",
      "description": "Artisan-crafted ceramic mug",
      "offers": {
        "@type": "Offer",
        "price": "24.99",
        "priceCurrency": "USD",
        "availability": "https://schema.org/InStock"
      }
    }
  ]
}
```

**Result:** When users ask ChatGPT "find handmade ceramic mugs," your store appears in results.

---

### 2. SaaS Companies

**Problem:** AI assistants can't recommend your product because they don't know it exists.

**Solution:** Structured entity catalog with product features:

```json
{
  "@type": "SoftwareApplication",
  "@id": "https://myapp.com/#software",
  "name": "My SaaS App",
  "applicationCategory": "BusinessApplication",
  "offers": {
    "@type": "Offer",
    "price": "49",
    "priceCurrency": "USD"
  },
  "aggregateRating": {
    "@type": "AggregateRating",
    "ratingValue": "4.8",
    "reviewCount": "1250"
  }
}
```

**Result:** AI assistants can recommend your product when users search for solutions.

---

### 3. Publishers & Media Companies

**Problem:** 500 blog posts buried across multiple pages.

**Solution:** Single knowledge graph with all NewsArticle entities:

```json
{
  "statistics": {
    "totalEntities": 500,
    "byType": {
      "NewsArticle": 450,
      "Person": 30,
      "Organization": 20
    }
  },
  "@graph": [
    {
      "@type": "NewsArticle",
      "@id": "https://techblog.com/ai-trends-2025",
      "headline": "Top AI Trends for 2025",
      "datePublished": "2025-11-01T09:00:00Z"
    }
  ]
}
```

**Result:** AI systems index entire content catalog from one file.

---

## Implementation Guide

### Minimal Implementation (15 Minutes)

**Step 1:** Create `ai-discovery.json` in your site's root directory:

```json
{
  "version": "1.0.0",
  "generatedAt": "2025-11-02T12:00:00Z",
  "website": {
    "url": "https://yoursite.com",
    "name": "Your Company Name",
    "description": "Brief description of your company"
  }
}
```

**Step 2:** Upload to your web server:

```bash
# Static hosting
cp ai-discovery.json public/

# Or create as dynamic endpoint (FastAPI example)
@app.get("/ai-discovery.json")
async def ai_discovery():
    return {
        "version": "1.0.0",
        "generatedAt": datetime.utcnow().isoformat() + "Z",
        "website": {
            "url": "https://yoursite.com",
            "name": "Your Company",
            "description": "Description here"
        }
    }
```

**Step 3:** Verify:

```bash
curl https://yoursite.com/ai-discovery.json | jq
```

**Done!** You've implemented Level 1 (Minimal) ADP compliance.

---

### Standard Implementation (2-4 Hours)

**Step 1:** Create `knowledge-graph.json` with your primary entities:

```json
{
  "@context": "https://schema.org",
  "@type": "KnowledgeGraph",
  "version": "1.0.0",
  "generatedAt": "2025-11-02T12:00:00Z",
  "@graph": [
    {
      "@type": "Organization",
      "@id": "https://yoursite.com/#organization",
      "name": "Your Company",
      "url": "https://yoursite.com",
      "logo": "https://yoursite.com/logo.png"
    }
  ]
}
```

**Step 2:** Create `llms.txt`:

```markdown
---
version: 1.0.0
lastModified: 2025-11-02T12:00:00Z
---

# Your Company Name

> AI-Optimized Content for Large Language Models

## Overview

[Brief company description]

## Products/Services

[List your main offerings]

## Recent Updates

[Latest news, product launches, etc.]

## Contact

- Website: https://yoursite.com
- Email: support@yoursite.com
```

**Step 3:** Update `ai-discovery.json` to reference new files:

```json
{
  "version": "1.0.0",
  "generatedAt": "2025-11-02T12:00:00Z",
  "website": {
    "url": "https://yoursite.com",
    "name": "Your Company"
  },
  "endpoints": {
    "knowledgeGraph": {
      "url": "https://yoursite.com/knowledge-graph.json",
      "format": "application/ld+json",
      "entityCount": 5,
      "version": "1.0.0"
    },
    "contextDocument": {
      "url": "https://yoursite.com/llms.txt",
      "format": "text/markdown"
    }
  }
}
```

**Done!** You've implemented Level 2 (Standard) ADP compliance.

---

## WordPress Plugin (Coming Soon)

We're building a WordPress plugin to automate ADP implementation:

```
Features:
✅ Auto-generates ai-discovery.json
✅ Creates knowledge-graph.json from posts/pages
✅ Builds llms.txt from site content
✅ Automatic versioning and updates
✅ WooCommerce product integration
```

**Launch date:** December 2025
**Price:** Free (MIT License)

---

## Shopify App Integration

Our **PresSEO** Shopify app now includes ADP support:

```
Features:
✅ Auto-generates knowledge graphs from products
✅ Syncs with Shopify GraphQL API
✅ Updates automatically on product changes
✅ Includes all collections, blogs, and pages
```

**Available now:** [Shopify App Store](https://apps.shopify.com/presseo)

---

## MCP Server (Model Context Protocol)

We're building an **MCP server** to provide ADP as a service:

```bash
npm install @pressonify/adp-mcp
```

**Features:**
- Host ADP files for any domain
- Real-time knowledge graph updates
- API for programmatic access
- CDN-backed for fast global delivery

**Pricing:**
- **Free:** Self-hosted open-source version
- **Pro ($29/mo):** Hosted MCP server + API
- **Enterprise ($299/mo):** Custom integrations + SLA

**Launch:** January 2026

---

## Why We're Open-Sourcing This

### The Network Effects Argument

Standards succeed when they're **widely adopted**, not when they're proprietary.

**Historical precedents:**
- **HTTP** (open) → Universal adoption
- **RSS** (open) → Billions of feeds
- **robots.txt** (open) → 25-year success
- **Proprietary protocols** → Dead

**Our strategy:**
1. **Open-source the standard** (MIT License)
2. **Build commercial tooling** (MCP server, plugins, APIs)
3. **Become thought leaders** in AI discovery
4. **Profit from implementation services**, not licensing

### The Marketing Play

Releasing ADP as an open standard positions Pressonify as:
- **Technical innovators** in AI infrastructure
- **Thought leaders** in AI discovery architecture
- **First movers** in Answer Engine Optimization (AEO)

**Expected outcomes:**
- HackerNews front page (traffic spike)
- Conference speaking opportunities
- Partnership discussions with AI platforms
- Developer community contributions

---

## Competitive Landscape

### How ADP Compares

| Feature | ADP | llms.txt | Schema.org Only | Google KG |
|---------|-----|----------|-----------------|-----------|
| **Single entry point** | ✅ | ❌ | ❌ | N/A |
| **Entity catalog** | ✅ | ❌ | Partial | Proprietary |
| **Versioning** | ✅ | ❌ | ❌ | ❌ |
| **Change detection** | ✅ | ❌ | ❌ | ❌ |
| **Open standard** | ✅ | ✅ | ✅ | ❌ |
| **AI-optimized** | ✅ | ✅ | ❌ | ✅ |

### Why Not Just Use llms.txt?

**llms.txt limitations:**
- No structured entity catalog
- No versioning or change detection
- Human-readable only (not machine-queryable)
- Major AI platforms don't support it yet

**ADP is complementary:** We **include** llms.txt as the context document layer.

---

## Roadmap

### Phase 1: Open Source Release (November 2025) ✅
- [x] Specification document
- [x] JSON Schema for validation
- [x] GitHub repository
- [x] Initial blog post

### Phase 2: Tooling & Validation (December 2025)
- [ ] WordPress plugin (free, MIT)
- [ ] Online validation tool
- [ ] JSON Schema validator
- [ ] Documentation site

### Phase 3: MCP Server (January 2026)
- [ ] `@pressonify/adp-mcp` package
- [ ] Hosted MCP service
- [ ] API for programmatic access
- [ ] CDN integration

### Phase 4: Ecosystem Growth (Q1 2026)
- [ ] Shopify app marketplace
- [ ] Drupal/Joomla plugins
- [ ] Static site generators (11ty, Hugo, Jekyll)
- [ ] Framework integrations (Next.js, Nuxt, SvelteKit)

### Phase 5: Standards Body Submission (Q2 2026)
- [ ] W3C Community Group proposal
- [ ] IETF RFC draft (similar to robots.txt RFC 9309)
- [ ] Schema.org vocabulary extension proposal

---

## Getting Involved

### For Developers

**Implement ADP on your site:**
1. Read the [specification](https://github.com/BuddySpuds/AI-Discovery-Protocol/blob/main/SPECIFICATION.md)
2. Create `ai-discovery.json`
3. Submit your site to our [directory](https://pressonify.ai/adp-directory)

**Contribute to the standard:**
1. [GitHub repository](https://github.com/BuddySpuds/AI-Discovery-Protocol)
2. [Discussion forum](https://github.com/BuddySpuds/AI-Discovery-Protocol/discussions)
3. [Issue tracker](https://github.com/BuddySpuds/AI-Discovery-Protocol/issues)

### For AI Platform Teams

We're actively seeking partnerships with:
- **OpenAI** (ChatGPT, GPT-4)
- **Anthropic** (Claude)
- **Google** (Gemini, Search)
- **Perplexity AI**

**Contact:** ai-discovery@pressonify.ai

---

## Technical Resources

### Documentation
- **Specification:** [GitHub](https://github.com/BuddySpuds/AI-Discovery-Protocol/blob/main/SPECIFICATION.md)
- **JSON Schema:** https://pressonify.ai/schemas/ai-discovery/v1.0.json
- **Validator:** https://pressonify.ai/tools/adp-validator (coming soon)

### Example Implementations
- **Pressonify.ai:** https://pressonify.ai/ai-discovery.json
- **E-commerce Demo:** https://demo-store.pressonify.ai/ai-discovery.json
- **Blog Demo:** https://demo-blog.pressonify.ai/ai-discovery.json

### Code Examples
- **FastAPI:** [examples/fastapi](https://github.com/BuddySpuds/AI-Discovery-Protocol/tree/main/examples/fastapi)
- **Node.js/Express:** [examples/nodejs](https://github.com/BuddySpuds/AI-Discovery-Protocol/tree/main/examples/nodejs)
- **Static Site:** [examples/static](https://github.com/BuddySpuds/AI-Discovery-Protocol/tree/main/examples/static)

---

## Frequently Asked Questions

### Q: Is this different from Schema.org?

**A:** ADP **uses** Schema.org vocabularies but adds:
- Single entry point (`ai-discovery.json`)
- Versioning and change detection
- Coordinated discovery across multiple files
- AI-specific optimizations

Think of it as "Schema.org + discovery protocol."

---

### Q: Will AI platforms actually use this?

**A:** We're in discussions with multiple AI platform teams. The value proposition is clear:
- **Faster crawling**: Single entry point vs 100 HTML pages
- **Incremental updates**: Change logs vs full re-crawls
- **Better results**: Structured entities vs keyword matching

Early feedback has been positive.

---

### Q: Why not just improve llms.txt?

**A:** llms.txt is excellent for context documents. But:
- No structured entity catalog
- No versioning
- Human-readable only

ADP **includes** llms.txt as the context layer, then adds structured entity catalogs.

---

### Q: How is this different from Google's Knowledge Graph?

**A:** Google's Knowledge Graph is **proprietary** and **extraction-based** (they build it from your site).

ADP is **declarative** — you tell AI systems exactly what entities exist and how they relate.

---

### Q: What about privacy/security?

**A:** ADP files are **public by design** (like robots.txt). Best practices:
- Don't include sensitive data
- Don't expose internal URLs
- Implement rate limiting
- Use HTTPS

See [Security Considerations](https://github.com/BuddySpuds/AI-Discovery-Protocol/blob/main/SPECIFICATION.md#7-security-considerations) in the spec.

---

### Q: How do I validate my implementation?

**A:** We're building an online validator (December 2025). For now:

```bash
# Validate against JSON Schema
curl https://yoursite.com/ai-discovery.json | \
  jsonschema -i /dev/stdin \
  https://pressonify.ai/schemas/ai-discovery/v1.0.json
```

---

## Conclusion

The AI Discovery Protocol represents a fundamental shift in how websites communicate with AI systems. By providing:

1. **Single entry point** (`ai-discovery.json`)
2. **Structured entity catalogs** (`knowledge-graph.json`)
3. **Human-readable context** (`llms.txt`)
4. **Versioning and change detection**

...we're building the infrastructure for **Answer Engine Optimization (AEO)** — the next evolution beyond traditional SEO.

**This is an open standard** (MIT License). We're not trying to own it; we're trying to **start a movement**.

If you believe websites should be discoverable to AI systems, join us.

---

## Resources

- **Specification:** https://github.com/BuddySpuds/AI-Discovery-Protocol
- **JSON Schema:** https://pressonify.ai/schemas/ai-discovery/v1.0.json
- **Discussion Forum:** https://github.com/BuddySpuds/AI-Discovery-Protocol/discussions
- **Contact:** ai-discovery@pressonify.ai
- **Twitter/X:** [@pressonify](https://twitter.com/pressonify)

---

**Let's make the web discoverable to AI systems. Together.**

— The Pressonify Team

---

## Related Resources

- [Create your first AI-optimized press release](/generate)
- [Check your site's AI visibility score](/ai-visibility-checker)
- [View our pricing plans](/pricing)
- [See what's new in our changelog](/changelog)
- [Explore our live ADP implementation](/llms.txt)
- [View our AI Discovery manifest](/ai-discovery.json)
- [Read more about the Five-Layer Stack](/blog/indispensability-of-ai-visibility-five-layer-stack)

---

*Released under MIT License | November 2, 2025 | Version 1.0.0*
## Knowledge bundle

- [Pressonify OKF bundle root](/okf/index.md)