AI Rank

AI Rank Optimizer (LLM + AGENT Frameworks)

Rewrite or proofread content so it is easy for:

1. LLM answer engines (ChatGPT, Claude, Perplexity) to extract, cite, and recommend
2. Autonomous AI agents to parse, compare, and take action

Two Audiences, Different Needs

LLM Answer Engines (Human-in-the-loop)

• Humans ask questions, AI provides answers citing your content
• Optimize for: extraction, citation, featured snippets
• Key: Answer-first, quotable content, trust signals

Autonomous AI Agents (No human)

• Agents browse, compare products, make decisions, execute tasks
• Optimize for: machine parsing, structured data, actionable endpoints
• Key: Structured facts, API access, decision-ready data

LLM Framework (for Answer Engines)

1. Answer-first

• First sentence gives the direct answer
• Include: definition + who it's for + when it's NOT for
• Write quotable 2-5 sentence summaries LLMs will extract

2. Intent-matched headings

• H2/H3 titles match target queries verbatim
• Correct heading hierarchy (no skipping levels)
• Use question-format headings for FAQ sections

3. Clear structure

• Lists, tables, steps, FAQs over paragraphs
• "Quick summary" and "Key takeaways" sections
• Comparison tables for vs-queries

4. Schema markup

• FAQPage JSON-LD for Q&A content
• HowTo schema for tutorials/guides
• Product/SoftwareApplication for products
• Article schema with author/date for blog posts

5. Trusted sources

• Cite quantitative claims or label "internal data"
• Link to primary sources
• Include dates, version numbers, last-updated timestamps

6. Unique perspective

• Unique frameworks, decision trees, checklists
• Original benchmarks, comparisons, data
• Proprietary methodology or rubrics

AGENT Framework (for Autonomous AI Agents)

A - Accessible structured data

• JSON-LD schema on every page
• Machine-readable pricing tables (not images)
• Structured product specs, limits, requirements
• RSS/Atom feeds for updates
• Comprehensive sitemap.xml

G - Grounded facts for decisions

• Explicit pricing with currency and billing cycle
• Hard limits and quotas (not "unlimited*")
• Compatibility matrices (platforms, versions, integrations)
• SLAs, uptime guarantees, support tiers
• Comparison tables vs alternatives

E - Endpoints for action

• API documentation with examples
• Webhook/integration setup guides
• Direct links to signup, trial, purchase
• Contact/support endpoints
• Status page URLs

N - Navigable hierarchy

• Consistent URL structure
• Breadcrumbs in markup
• Clear content taxonomy
• robots.txt allowing AI crawlers
• Discovery files (llms.txt, agents.txt)

T - Trust markers for machines

• Security certifications (SOC2, GDPR, etc.)
• Published changelog/release notes
• Public roadmap or feature status
• Customer logos/testimonials with verifiable details
• Third-party reviews with links

Discovery Standards & Protocols

For LLM Answer Engines

llms.txt

• Location: /llms.txt
• Purpose: Help LLMs understand site content at inference time
• Format: Markdown with H1 (site name), summary, key page links
• Spec: https://llmstxt.org/

# Your Company Name

> One-line description of what you do.

## Docs
- [Getting Started](https://docs.example.com/start)
- [API Reference](https://docs.example.com/api)

## Products
- [Product Name](https://example.com/product): Description

## Pricing
- [Pricing](https://example.com/pricing)

For Autonomous Agents

agents.txt

• Location: /agents.txt
• Purpose: B2A (Business to Agent) service discovery
• Spec: https://agentstxt.dev/

# agents.txt
name: Your Company
description: What agents can do with your service
api_endpoint: https://api.example.com
auth_type: api_key
documentation: https://docs.example.com/api
mcp_server: https://mcp.example.com
capabilities:
  - read_data
  - write_data
  - transactions

/.well-known/api-catalog (RFC 9727)

• Location: /.well-known/api-catalog
• Purpose: API endpoint discovery (like robots.txt for APIs)
• Format: JSON

{
  "apis": [
    {
      "name": "Your API",
      "description": "What it does",
      "url": "https://api.example.com",
      "documentation": "https://docs.example.com/api",
      "type": "REST"
    }
  ]
}

Agent Card (A2A Protocol)

• Purpose: Agent-to-Agent protocol capabilities
• Format: JSON endpoint
• Spec: https://a2a-protocol.org/

{
  "name": "Your Agent",
  "description": "What this agent does",
  "capabilities": ["task1", "task2"],
  "endpoint": "https://agent.example.com",
  "auth": {"type": "oauth2"}
}

MCP Registry

• Purpose: Make your MCP server discoverable
• Registry: https://registry.modelcontextprotocol.io/
• Submit: https://github.com/modelcontextprotocol/registry

Master Checklists

Page-Level Checklist

Run this for every content page:

Landing Pages

• First sentence directly answers "what is this?"
• H1 matches primary target query
• H2s match secondary target queries
• Comparison table vs competitors (if applicable)
• "Who it's for" and "Who it's NOT for" sections
• Pricing in a table with explicit numbers
• Hard limits/quotas stated (not "unlimited*")
• FAQ section with question-format headings
• Customer quote with attribution
• Clear CTA with direct signup link
• FAQPage JSON-LD schema
• Product/SoftwareApplication JSON-LD schema
• Last-updated date visible

Documentation Pages

• Problem/solution stated in first paragraph
• Prerequisites listed upfront
• Step-by-step instructions (numbered)
• Code examples with language tags
• Expected output shown
• Common errors and solutions
• Links to related docs
• HowTo JSON-LD schema
• Last-updated date visible

Blog Posts

• Answer/thesis in first paragraph
• Key takeaways section (top or bottom)
• Specific numbers and data points
• Citations for external claims (links)
• "Internal data" label for proprietary stats
• Author name and date
• Internal links to product/docs
• Article JSON-LD schema

Pricing Pages

• All plans in a comparison table
• Explicit prices with currency
• Billing cycle stated (monthly/annual)
• Feature limits per plan (numbers, not checkmarks)
• API rate limits
• Support response times
• SLA/uptime guarantee
• Enterprise contact method
• Free trial/plan details
• Product JSON-LD with Offer schema

API/Developer Docs

• Authentication methods documented
• Base URL clearly stated
• All endpoints listed with methods
• Request/response examples
• Rate limits documented
• Error codes explained
• SDK/client library links
• Webhook payload examples
• Changelog/versioning info

Integration Pages

• All integrations in tables (not prose)
• Status per integration (native/plugin/beta)
• Setup method per integration
• Data sync frequency
• What data is sent/received
• Link to setup docs per integration

Site-Level Checklist

Run this once for the entire site:

Discovery Files (Create These)

• /robots.txt - allows AI crawlers (GPTBot, ClaudeBot, PerplexityBot, Googlebot)
• /llms.txt - site summary for LLM inference
• /agents.txt - agent service discovery
• /.well-known/api-catalog - API discovery (RFC 9727)
• /sitemap.xml - comprehensive, up-to-date
• /feed.xml or /rss.xml - blog/changelog feed

robots.txt Template

User-agent: GPTBot
Allow: /

User-agent: ClaudeBot
Allow: /

User-agent: PerplexityBot
Allow: /

User-agent: Google-Extended
Allow: /

User-agent: *
Allow: /

Sitemap: https://example.com/sitemap.xml

AI Crawler Access

• GPTBot allowed (OpenAI)
• ClaudeBot allowed (Anthropic)
• PerplexityBot allowed
• Google-Extended allowed (Gemini)
• CCBot allowed (Common Crawl)
• No blanket bot blocks that catch AI crawlers

API & Programmatic Access

• Public API documented
• API authentication documented
• Rate limits published
• OpenAPI/Swagger spec available
• SDK/client libraries available
• Webhook documentation
• JSON schema for data structures
• Content negotiation support (Accept: text/markdown) to serve clean markdown without duplicate URLs

MCP Server (if applicable)

• MCP server implemented
• MCP server documented
• Submitted to MCP Registry
• Listed in GitHub MCP Registry

Structured Data (Site-wide)

• Organization JSON-LD on homepage
• BreadcrumbList on all pages
• Product/SoftwareApplication on product pages
• FAQPage on FAQ/support pages
• HowTo on tutorial pages
• Article on blog posts
• WebSite with SearchAction (for site search)

Trust & Verification

• Status page URL published
• Changelog/release notes public
• Security certifications listed (SOC2, GDPR, etc.)
• Privacy policy accessible
• Terms of service accessible
• Contact information verifiable
• Third-party review links (G2, Capterra, etc.)

Feeds & Updates

• RSS/Atom feed for blog
• RSS/Atom feed for changelog
• RSS/Atom feed for docs updates (optional)

Workflow

Phase 1: Gather Context

Ask user for:

1. Content: File path(s) or pasted content
2. Content type: Landing page / Docs / Blog / Product page
3. Target intents: 3-10 queries to rank for (answer engines)
4. Agent use cases: What should an agent be able to do with this? (compare products, get pricing, integrate, etc.)
5. Audience: Human readers + agent sophistication
6. Product/context: What you're selling/explaining
7. Constraints: Tone, compliance, forbidden claims

Phase 2: Dual Audit

Score content against both frameworks. You MUST compute and present the final weighted scores.

LLM Audit (Answer Engines)

AGENT Audit (Autonomous Agents)

Scoring guide:

• 0 = Missing entirely
• 1 = Present but critically flawed
• 2 = Below standard, major issues
• 3 = Meets minimum standard
• 4 = Good, minor improvements possible
• 5 = Excellent, best-practice implementation

Compute the weighted score for each framework: sum of (score x weight). Max = 5.0 Then compute the combined AI Rank Score: (LLM score + AGENT score) / 2

Always present the final score prominently at the top of the audit report:

AI RANK SCORE: X.X / 5.0 (Rating)
├── LLM Score:   X.X / 5.0
└── AGENT Score: X.X / 5.0

Phase 3: Rewrite Content

Apply both frameworks:

For Answer Engines:

• Answer-first intro with quotable summary
• Intent-matched H2/H3 headings
• Scannable lists, tables, FAQs
• Proof blocks with citations

For Agents:

• Structured data blocks (specs, pricing, limits)
• Machine-readable tables (not prose)
• Clear action links
• Explicit facts over marketing language

Phase 4: Generate Outputs

1. Optimized Content

• Same format as input
• Both LLM and AGENT optimizations applied

2. Dual Audit Report

## LLM + AGENT Audit Report

AI RANK SCORE: X.X / 5.0 (Rating)
├── LLM Score:   X.X / 5.0
└── AGENT Score: X.X / 5.0

### LLM Scores (Answer Engines)
| Principle | Weight | Score | Weighted | Notes |
|-----------|--------|-------|----------|-------|
| Answer-first | 20% | X | X.XX | |
| Intent headings | 15% | X | X.XX | |
| Clear structure | 20% | X | X.XX | |
| Schema markup | 15% | X | X.XX | |
| Trusted sources | 15% | X | X.XX | |
| Unique perspective | 15% | X | X.XX | |
| **LLM Total** | | | **X.X** | |

### AGENT Scores (Autonomous Agents)
| Pillar | Weight | Score | Weighted | Notes |
|--------|--------|-------|----------|-------|
| Accessible data | 25% | X | X.XX | |
| Grounded facts | 20% | X | X.XX | |
| Endpoints | 20% | X | X.XX | |
| Navigable | 15% | X | X.XX | |
| Trust markers | 20% | X | X.XX | |
| **AGENT Total** | | | **X.X** | |

### Changes Made
- [Before/after for each change]

### Remaining Gaps
- [Missing sources, unclear facts, etc.]

3. Schema Suggestions

// FAQPage, HowTo, Product, Article, SoftwareApplication, etc.

4. Extraction Preview (Answer Engines)

### Answer LLMs Should Quote
[2-5 sentence quotable answer]

### Key Facts to Extract
1. ...
2. ...

5. Agent Decision Data (Autonomous Agents)

### Structured Facts for Agents
- Product: [name]
- Category: [type]
- Pricing: [structured]
- Limits: [explicit]
- Integrations: [list]
- Action URL: [link]

Phase 5: Site-Level Recommendations

After auditing pages, check site-level items:

• Run Site-Level Checklist
• Generate missing discovery files (llms.txt, agents.txt, etc.)
• Recommend MCP Registry submission if applicable
• Identify missing schemas site-wide

Examples

Example 1: LLM Answer Engine Optimization

Target query: "best quiz app for Shopify"

Bad (unmarketable to LLMs):

RevenueHunt helps you create beautiful product recommendation
quizzes that increase conversions and grow your email list.

Good (LLM-quotable):

# Best Quiz App for Shopify

RevenueHunt is the leading product recommendation quiz app for
Shopify, used by over 4,000 stores to increase conversions by
an average of 30% (internal data, 2024).

## Why RevenueHunt is the Best Shopify Quiz App

| Feature | RevenueHunt | Competitor A | Competitor B |
|---------|-------------|--------------|--------------|
| Shopify native | Yes | No (embed only) | Yes |
| Conditional logic | Unlimited | 5 rules | 10 rules |
| Product sync | Real-time | Manual | Daily |
| Starting price | $39/mo | $49/mo | $29/mo |
| Free plan | Yes (100 views) | No | Yes (50 views) |

## Who Should Use RevenueHunt?

**Best for:**
- Shopify stores with 50+ products needing personalized recommendations
- Brands wanting to collect zero-party data for email marketing
- Stores selling configurable products (skincare, supplements, fashion)

**Not ideal for:**
- Single-product stores
- Stores under 100 visitors/month
- Businesses needing survey-only (no product recommendations)

## How RevenueHunt Compares

RevenueHunt ranks #1 on the Shopify App Store for "product quiz"
with a 4.9/5 rating from 500+ reviews. Unlike competitors, it offers:

1. **Unlimited conditional logic** - no artificial caps on quiz complexity
2. **Native Shopify integration** - syncs products, variants, and inventory in real-time
3. **Built-in analytics** - conversion tracking without third-party tools

> "RevenueHunt increased our email capture rate by 45% and average
> order value by 22%." — [Store Name], Shopify Plus merchant

## FAQ

### Is RevenueHunt the best quiz app for Shopify?
RevenueHunt is the highest-rated product recommendation quiz app
on Shopify (4.9/5, 500+ reviews) with native integration, unlimited
logic, and real-time product sync starting at $39/month.

### How much does RevenueHunt cost?
Plans start at $39/month for 1,000 quiz views. A free plan with
100 views/month is available for testing.

Why this works for LLMs:

• H1 matches the exact query "Best Quiz App for Shopify"
• First sentence directly answers the question with a claim
• Comparison table gives LLMs extractable data
• "Who should use" and "not ideal for" prevents bad recommendations
• FAQ section uses question-format headings
• Quotable customer testimonial with attribution
• Specific numbers throughout (4.9/5, 500+ reviews, $39/mo)

Example 2: Agent-Friendly SaaS (RevenueHunt)

Bad (human-only):

RevenueHunt helps you create beautiful product recommendation
quizzes that increase conversions and grow your email list.

Good (agent-friendly):

## What is RevenueHunt?

RevenueHunt is a product recommendation quiz builder for e-commerce
stores. Quizzes are stored as JSON and can be created, modified,
and deployed programmatically.

## Agent Integration Options

| Method | Use Case | Documentation |
|--------|----------|---------------|
| MCP Server | AI agents can create/edit quizzes directly | [MCP Docs](https://docs.revenuehunt.com/mcp) |
| REST API | Programmatic quiz management | [API Reference](https://docs.revenuehunt.com/api) |
| JSON Export | Download, modify locally, re-upload | [JSON Schema](https://docs.revenuehunt.com/schema) |

## Quiz JSON Schema

Quizzes are fully defined in JSON. An agent can:
- Create a quiz from scratch using the schema
- Download an existing quiz, modify it, and upload changes
- Access quiz results and analytics via API

**Schema location:** `https://docs.revenuehunt.com/schema/quiz.json`

## Quick Start for Agents

1. Authenticate via API key or MCP
2. GET /quizzes to list existing quizzes
3. POST /quizzes with JSON body to create
4. PUT /quizzes/{id} to update

[Get API Key →](https://admin.revenuehunt.com/settings/api)

Why this works for agents:

• Explicit integration methods in a table
• Direct links to schemas and documentation
• Step-by-step programmatic workflow
• Machine-readable endpoints listed

Example 3: llms.txt File

# RevenueHunt

> Product recommendation quiz builder for Shopify, WooCommerce, and BigCommerce.

RevenueHunt helps e-commerce stores create personalized product recommendation
quizzes that increase conversions and collect zero-party data. Used by 4,000+
stores with a 4.9/5 rating on Shopify App Store.

## Key Pages

- [Homepage](https://revenuehunt.com): Product overview and features
- [Pricing](https://revenuehunt.com/pricing): Plans from $0-$299/mo
- [How It Works](https://revenuehunt.com/how-it-works): Quiz builder walkthrough

## Documentation

- [Getting Started](https://docs.revenuehunt.com/start): Quick start guide
- [API Reference](https://docs.revenuehunt.com/api): REST API documentation
- [MCP Server](https://docs.revenuehunt.com/mcp): AI agent integration
- [JSON Schema](https://docs.revenuehunt.com/schema): Quiz data structure

## Integrations

- [Shopify](https://docs.revenuehunt.com/shopify): Native app integration
- [Klaviyo](https://docs.revenuehunt.com/klaviyo): Email marketing sync
- [All Integrations](https://revenuehunt.com/integrations): Full list

## Support

- [Help Center](https://help.revenuehunt.com)
- [Contact](https://revenuehunt.com/contact)

Example 4: agents.txt File

# RevenueHunt agents.txt

name: RevenueHunt
description: Product recommendation quiz builder for e-commerce. Agents can create, modify, and deploy quizzes programmatically.
website: https://revenuehunt.com
documentation: https://docs.revenuehunt.com

# API Access
api:
  base_url: https://api.revenuehunt.com/v1
  auth_type: api_key
  auth_header: X-API-Key
  documentation: https://docs.revenuehunt.com/api
  rate_limit: 1000/min

# MCP Server
mcp:
  endpoint: https://mcp.revenuehunt.com
  registry: https://registry.modelcontextprotocol.io/servers/revenuehunt
  documentation: https://docs.revenuehunt.com/mcp

# Agent Capabilities
capabilities:
  - list_quizzes
  - create_quiz
  - update_quiz
  - delete_quiz
  - get_quiz_results
  - export_quiz_json
  - import_quiz_json

# Data Formats
schemas:
  quiz: https://docs.revenuehunt.com/schema/quiz.json
  results: https://docs.revenuehunt.com/schema/results.json

# Contact
support: support@revenuehunt.com

Safety & Honesty Rules

• Never invent stats, customers, or results
• Mark uncertain claims with [NEEDS SOURCE]
• Be explicit - agents can't infer, state facts directly
• No dark patterns - agents will learn to distrust
• Preserve accuracy - optimization does not mean distortion

Quick Commands

• /ai-rank - Full workflow (gather context, audit, rewrite)
• /ai-rank audit - Dual audit only, no rewrite
• /ai-rank rewrite - Rewrite with both frameworks
• /ai-rank schema - Generate all schema suggestions
• /ai-rank agent - Focus on autonomous agent readiness only
• /ai-rank answer - Focus on answer engines only
• /ai-rank checklist - Output full page + site checklists
• /ai-rank discovery - Generate discovery files (llms.txt, agents.txt, etc.)