NorthLoop Coffee: From Invisible to Agent-Discoverable in 12 Days
A Minneapolis specialty coffee roaster with a thriving e-commerce site—completely invisible to AI agents. Here's how we made them discoverable across every major agent index.
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The Client
NorthLoop Coffee is a specialty coffee roaster based in the North Loop neighborhood of Minneapolis. Founded in 2019, they roast single-origin beans and sell direct-to-consumer through their e-commerce site. Annual online revenue sits around $400k, with roughly 60% of sales coming from subscription orders.
Their site looked great to humans. Custom Shopify theme, professional photography, excellent Google SEO—they ranked on page one for "Minneapolis coffee roaster" and "specialty coffee subscription Minnesota." Traditional SEO was working.
But none of that mattered to AI agents.
The Problem
When we ran our initial agent discovery audit, the results were stark:
| Agent Index | Query | Result |
|---|---|---|
| Cloudflare AI Index | "specialty coffee subscription Minneapolis" | Not indexed |
| Exa.ai | "NorthLoop Coffee roaster" | 0 results |
| Brave Search API | "buy specialty coffee Minneapolis online" | Not found |
| Tavily | "coffee subscription service Minnesota" | 0 mentions |
The site was completely invisible to every major agent search index. Beyond discoverability, the technical metrics explained why:
- Page weight: 2.1MB for the homepage (Shopify theme with heavy JavaScript, CSS, tracking pixels, and embedded fonts)
- Parse time: ~2,200ms for an agent to extract useful product information
- Structured data: Basic Shopify JSON-LD for products only, no organization or service schema
- LLM.txt: None
- Markdown content: Not available
An AI agent asked to "find a specialty coffee subscription in Minneapolis" would never find NorthLoop Coffee—not because the business wasn't good, but because it wasn't speaking the language agents understand.
What We Did
We applied the Full Package optimization over 12 days. Here's the step-by-step process:
Days 1-2
Discovery Audit
Ran full benchmark across Cloudflare AI Index, Exa.ai, Brave Search API, and Tavily. Documented baseline metrics: page weight, parse time, structured data coverage, and index presence. Identified 14 optimization opportunities ranked by impact.
Days 3-4
LLM.txt and LLM-full.txt Creation
Created machine-readable site description files. The LLM.txt covers business identity, product catalog summary, subscription options, and contact information. The LLM-full.txt adds detailed roast profiles, sourcing information, pricing tiers, and API-style product data agents can parse directly.
Days 5-6
JSON-LD Structured Data
Expanded beyond Shopify's default product schema. Added Organization, LocalBusiness, WebSite, BreadcrumbList, and detailed Product schemas with offers, reviews, and availability. Every page now carries complete structured data an agent can act on without parsing HTML.
Days 7-9
AgentCDN Layer
Deployed a Cloudflare Worker that intercepts agent requests and serves clean content. HTML stripped of JavaScript, CSS, tracking pixels, and navigation chrome. Pre-generated markdown summaries for every product page. Cached API endpoints returning structured product data (name, price, origin, roast level, availability) in JSON format.
Days 10-11
Index Submissions
Submitted site to Cloudflare AI Index, registered with Exa.ai, and configured Brave Search API access. Verified indexing across all three platforms. Submitted LLM.txt to emerging agent directories.
Day 12
Verification and Baseline
Ran the full benchmark suite again. Verified discovery across all indexes. Established monitoring baselines for ongoing optimization. Delivered client report with before/after metrics and recommendations for month-two priorities.
The Results
After 12 days, NorthLoop Coffee went from completely invisible to fully discoverable:
| Metric | Before | After | Change |
|---|---|---|---|
| Cloudflare AI Index | Not indexed | Indexed | Discoverable |
| Exa.ai results | 0 mentions | Site appears | Discoverable |
| Brave Search API | Not found | Found | Discoverable |
| Page weight (agent) | 2.1MB | 48KB | 97.7% smaller |
| Parse time | 2,200ms | 95ms | 95.7% faster |
| Exa latency | N/A | <200ms | Sub-200ms |
| Structured data types | 1 (Product only) | 6 types | Complete coverage |
| LLM.txt | None | Full + Extended | Machine-readable |
What This Means in Practice
When an AI agent is asked "find me a specialty coffee subscription in Minneapolis," NorthLoop Coffee now appears in the results. The agent can read their structured product data, understand pricing and availability, and even place an order through their e-commerce API—all without a human ever loading a web page.
More importantly, NorthLoop Coffee is one of the first specialty coffee businesses in the Minneapolis market to be agent-optimized. Their competitors—many of whom have excellent Google SEO—are invisible to agent search. This is classic first-mover advantage in a channel that's about to scale.
As Cloudflare continues to expand its agent infrastructure (they already serve 20% of the web), and as agent commerce platforms like Stripe's Agent Commerce Suite drive more purchasing through AI agents, being discoverable now positions NorthLoop Coffee for a channel that most of their competitors don't yet know exists.
The Benchmark Methodology
All metrics were collected using our agent discovery benchmark framework, which tests:
- Index presence: Queries to Cloudflare AI Index, Exa.ai, Brave Search API, and Tavily for business-relevant search terms
- Parse performance: Time for an agent to extract structured information from the page (measured via headless request + content extraction)
- Page weight: Raw HTML size vs. clean markdown content size served to agents
- Structured data coverage: JSON-LD schema types present and validated
- LLM.txt compliance: Presence, completeness, and accuracy of machine-readable site files
We publish the benchmark script as open source. You can request access or run it against your own site.
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