The questions buyers ask an AI, answered for both readers.

What is GTM engineering?

GTM engineering is building the technical systems that determine how AI tools and AI agents evaluate, cite, recommend, and transact with a business.

Traditional go-to-market work optimizes a business for human readers: messaging, design, ads. GTM engineering makes the same business legible to the machine readers that now sit between you and your buyer: structured data (JSON-LD), agent-facing files like llms.txt, explicit bot-access policy, machine-readable content, and, where the business transacts online, pathways an agent can actually complete.

We practice it as one stack: SEO gets you ranked → GEO gets you cited → agent readiness gets you transacted with. Selling one layer alone and calling it strategy is how most of the market gets this wrong.

What is GEO, and how is it different from SEO?

GEO (Generative Engine Optimization) gets a business named inside AI-generated answers; SEO gets it positioned in a list of links. Different reader, different outcome.

SEO's output is a ranking a human scrolls past. GEO's output is being one of the three or four names ChatGPT, Perplexity, or Gemini includes when a buyer asks "who should we shortlist for this?" The two are complementary, since generative engines draw heavily on search indexes, but GEO adds work SEO never measured: machine-readability, citable content mapped to real buyer questions, and progress tracked as citation share against named competitors rather than ranking position.

"seo_outcome":  "ranked"   // a position in links
"geo_outcome":  "cited"    // a name in the answer
"names_in_a_typical_answer": 3

What is AI agent readiness?

Agent readiness is whether an autonomous AI agent can read your business and complete useful actions with it, from extracting accurate facts to initiating a transaction.

It's measurable today. Cloudflare's public Agent Readiness scanner (isitagentready.com) checks what a site declares through emerging agent standards: discoverability files, content machine-readability, bot access control, and declared APIs or MCP endpoints. Most B2B sites score poorly, not because the businesses are bad but because their sites were built for exactly one kind of reader.

Caveat we volunteer: the scanner measures what a site publicly declares, not how AI tools behave toward it. That's why our audits pair scanner scores with live citation checks.

What is llms.txt?

llms.txt is a markdown file at your site's root that gives AI systems a concise, structured summary of your business. What robots.txt does for permissions, llms.txt does for meaning.

It's one of a family of agent-facing declarations (with JSON-LD structured data, markdown content negotiation, and .well-known files) that together make a site legible to machines. It costs almost nothing to implement, which is exactly why absence is telling: a site without one has usually never been evaluated from the machine's side at all.

How do you measure AI visibility honestly?

With proxies, plainly labeled: citation share against named competitors, readiness-score deltas, agent traffic in your logs, and branded-search lift.

Citation share is the workhorse: take a fixed set of real buyer questions, ask the major AI tools in fresh sessions on a schedule, record who gets named, and track your share of those mentions against competitors by name. Screenshot everything. It's reproducible and it moves when the engineering work lands.

What we won't do: quote per-deal AI attribution or guarantee a ChatGPT ranking. AI-channel attribution is genuinely imperfect industry-wide. A vendor promising either is guessing, and a measurement partner who starts by guessing is measuring nothing.

Is AI referral traffic big enough to matter for B2B yet?

In absolute terms it's still small; that's the honest answer. It's also the fastest-growing referral channel, and its visitors arrive already decided.

The case for 2026 is not "you're losing money today." It's that the standards deciding who gets recommended are forming now, the build takes about sixty days of config- and content-level work, and the scores are public. Your competitors can scan you tomorrow. Being early costs a quarter of effort. Being late means someone else is the default answer when the wave arrives.

How does an AI actually build its shortlist?

From what it can read, verify, and cite: machine-readable content, structured data, third-party corroboration, and pages that directly answer the buyer's question.

When a buyer asks "best providers of [X] for a company like ours," the AI assembles a small set of names from sources it can parse and trust. Businesses that are illegible to machines tend to be absent from that answer regardless of how good they actually are. That's the core inversion of this channel: getting on the shortlist is an engineering problem before it's a marketing problem.