Ric Ruben is the founder of Faro, the AI readiness platform helping businesses become visible, understandable, and actionable to AI agents. He built Faro after recognizing that the same shift that happened with Google search in 2005 is happening again with AI: the businesses that get found early compound their advantage for a decade.
Before Faro, Ric ran Kadima Digital, a digital operations firm working with growth-stage companies on distribution and digital infrastructure. That experience made the AI readiness gap obvious: most businesses were investing in product and brand while the underlying technical signals that AI agents use to evaluate them went unaddressed.
He writes on Answer Engine Optimization (AEO), MCP server implementation, structured data strategy, and what it actually takes to get recommended by ChatGPT, Claude, Perplexity, and Gemini. His focus is practical: not theory, but the specific checks, fixes, and monitoring approaches that move a Faro Score and get businesses into AI answers.
Faro is used by marketing teams, growth leads, and digital agencies managing AI visibility for multiple clients across SaaS, e-commerce, fintech, and professional services.
How to Show Up in Google AI Overviews (Without Chasing the Algorithm)
Google AI Overviews pull from a small set of sources Google trusts to answer a query directly. Winning a citation is less about ranking tricks and more about being unambiguously the best-structured answer. This guide covers what earns a spot, how to measure it, and the mistakes that keep good pages invisible.
What Is WebMCP? The Protocol Deciding Whether AI Agents Can Use Your Website
WebMCP is a browser-native protocol co-developed by Google and Microsoft that lets websites declare callable functions for AI agents, replacing fragile DOM scraping with structured tool calls. With an origin trial live in Chrome 149 and the Lighthouse Agentic Browsing category now shipping by default, there are concrete things to audit and fix on your site right now.
API Documentation for AI Agents: Why Your Docs Are the New Sales Page
When an AI agent evaluates your product, your API documentation is often the first thing it reads and the main thing it judges you on. This guide covers what agents actually parse, the difference between human-friendly and machine-friendly docs, and seven concrete fixes you can ship this week.
Ahrefs Says 97% of llms.txt Files Get Zero Requests. They Missed the Point.
Ahrefs measured how often AI crawlers fetch llms.txt files and found 97% get zero requests. The study is methodologically sound but the conclusion misses the most important use case: inference-time context injection, not pre-crawl indexing. llms.txt was never primarily a crawler instruction file.
Cloudflare Now Has an Agent Readiness Score. Here's What It Checks — and What It Misses.
Cloudflare's new agent readiness score checks four infrastructure signals. Faro runs 30+ checks across 6 categories including pricing transparency, structured data, MCP endpoints, llms.txt quality, and AI citation signals. The gap is not about better vs. worse — it reflects two fundamentally different problems.
MCP for Business: What Model Context Protocol Means If You Are Not an Engineer
Model Context Protocol (MCP) is Anthropic's open standard for connecting AI models like Claude to external tools and data. Publishing an MCP server puts your product inside the AI's native workflow, making it callable without the user ever leaving their AI assistant. For B2B software companies, this is a new distribution channel, not just an engineering project.
Google's A2A Protocol: What It Means for Your Business When Agents Talk to Agents
Google's A2A protocol lets one AI agent delegate tasks to another, including calling your business endpoint directly on behalf of a buyer. Businesses without an A2A-compatible interface are skipped entirely from that selection process.
AI Answer Position: The New Ranking Metric Every Marketer Needs to Track
AI answer position is where your brand appears inside a flowing AI-generated response, and it matters far more than simply being mentioned at all. Brands named first in an AI answer are 389% more likely to be searched afterward. This post explains what drives position, how to measure it, and which signals to fix first.
Fan-Out Queries: The Background Searches That Determine Your AI Citations
Fan-out queries are the background searches an AI model fires before producing an answer. They determine which brands get retrieved and cited. Optimizing for them is different from SEO, and most brands are not doing it.
How to Get Your Business Cited by ChatGPT, Claude, and Perplexity
Brands recommended first by AI are 389% more likely to be Googled afterward. Earning that recommendation requires specific technical signals and content structures. Most businesses are missing the majority of them.
agents.json: The Missing File That Makes Your Website Agent-Accessible
Most websites tell AI what they contain. agents.json tells AI agents what they can do. It's one of the most important files your site is probably missing.
Why Your Reddit Presence Now Affects Your AI Search Visibility
Research shows Reddit is the dominant source of user-generated content that AI deep-research agents retrieve. Most brands have no active Reddit strategy, which means their AI representation is built on whatever competitors, critics, or unrelated discussions happen to be there.
Grounding, Shaping, Poisoning: The Three AI Visibility Strategies Every Marketer Needs to Know
Grounding gives AI verifiable evidence. Shaping puts your talking points in front of AI systems. Poisoning manipulates AI without users knowing. Most marketing teams are doing all three without realizing it.
Open Knowledge Format (OKF): The Standard AI Agents Use to Navigate Your Business
OKF is Google's answer to how AI agents should represent structured business knowledge. It's new (June 2026) and already reshaping what AI readiness means for businesses.
What Is an AI Readiness Score — and What Does Good Look Like?
An AI readiness score is more than a number. Understanding what it measures, how each check is weighted, and what score thresholds mean in practice makes it a useful strategic tool.
AI Readiness for CMOs: What Marketing Leaders Need to Track in 2026
AI discovery is becoming a marketing metric. CMOs who start tracking it now will have data and strategy advantages that compound over the next two years.
How to Benchmark Your AI Readiness Against Competitors
AI readiness isn't just about your own score. The category question is whether AI agents are more likely to recommend you or your competitors. Here's how to find out.
AI Readiness for E-Commerce: What Online Stores Need in 2026
E-commerce is at the front of the agent economy wave. Shopping agents are already active, and stores that aren't structured for them are losing ground fast.
AEO: Answer Engine Optimization for AI-First Discovery
Answer engine optimization (AEO) is the practice of making your website readable, credible, and citable by AI answer engines. Unlike SEO, success isn't measured in rankings but in whether AI systems recommend you accurately and confidently. This guide covers the full tactical picture.
How ChatGPT, Claude, and Perplexity Decide Which Businesses to Recommend
AI models use a layered set of signals to decide which businesses appear in their answers. Understanding those signals is the first step to being recommended.
How Agencies Can Offer AI Readiness Audits as a Paid Service
Digital agencies are well-positioned to offer AI readiness audits as a new service line. Clients already trust you with their SEO and web presence. AI readiness is a natural extension: the same relationship, a new technical discipline, and a category with almost no client awareness yet. Here's how to package, price, and deliver it.
Why AI Agents Can't Read Your Pricing Page (And How to Fix It)
Most SaaS pricing pages are built with JavaScript frameworks that AI agents can't render. When an agent evaluating your product can't find your pricing, it either skips you or forms a confidence-limited recommendation. Fixing this is a one-time technical change with permanent AI visibility benefits.
AI Discoverability vs. SEO: What's Different, What Overlaps
SEO and AI discoverability are related but not the same. SEO is optimizing for search engine rankings. AI discoverability is optimizing for how AI agents find, evaluate, and recommend your business. Some SEO work transfers; most of the new work is in structured data, API accessibility, llms.txt, and pricing transparency.
Is Your robots.txt Blocking AI Crawlers? How to Check and What to Fix
A misconfigured robots.txt can make your entire website invisible to AI systems. Many businesses don't realize they're blocking GPTBot, ClaudeBot, and PerplexityBot because those rules were added years ago using wildcard disallow patterns. This guide shows you how to check your robots.txt, which crawlers matter, and how to fix blocking rules safely.
Structured Data for AI Agents: Why JSON-LD Is Your Secret Weapon
Structured data is how your website communicates facts to machines rather than humans. JSON-LD schema gives AI agents a reliable, structured source of truth about your organization, products, pricing, and features. This guide covers the five schema types that matter most for AI readiness and how to implement each one.
How to Create an llms.txt File (And Why AI Agents Need It)
llms.txt is a simple markdown file at your domain root that gives AI agents a prioritized map of your most important content. This guide covers the specification, what to include, common mistakes, and how to generate one for any business in under 10 minutes.
What Is AI Readiness? The Complete Guide for Marketers
AI readiness measures how well your website performs when AI agents crawl, evaluate, and recommend your business. This guide explains the six core dimensions, why the bar is different from traditional SEO, and what you can do about it today.
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