Building for Zero-Click Search: A Founder's GEO Playbook

The shift nobody is talking about

A year ago, if someone needed an e-signing tool, they'd Google “e-signature API”, scan the top 10 results, click through landing pages, compare pricing tables, and eventually sign up for a trial. That was the funnel.

Today, a growing segment of that audience types “I need to send a contract for signature from my AI agent” into ChatGPT or Claude. The model either recommends a tool — or it doesn't. There's no page 1 to rank on. No ad slot to buy. Either the AI knows about your product or you don't exist.

This is zero-click search. The user gets an answer without clicking a link. And it's the fastest-growing distribution channel for developer tools.

What is GEO

Generative Engine Optimisation is the practice of making your product discoverable and usable by AI-powered search and recommendation systems — ChatGPT, Claude, Perplexity, Gemini, and the agents built on top of them.

Where SEO optimises for Google's ranking algorithm, GEO optimises for how language models understand, recommend, and integrate your product.

The Signbee GEO stack

Here's exactly what we built at Signbee to make ourselves discoverable by AI. Think of this as a checklist you can steal.

1. llms.txt

A plain text file at signb.ee/llms.txt that tells AI models what Signbee does, what endpoints are available, how to authenticate, and what the request/response formats look like. No marketing copy. Just facts. We wrote a full guide on this.

2. OpenAPI spec

A machine-readable API spec at signb.ee/openapi.json. This is the gold standard for API discoverability — any tool that consumes OpenAPI specs (including AI coding assistants) can understand your entire API surface from this single file.

3. MCP server

An installable MCP server (npx -y signbee-mcp) that gives AI tools like Claude, Cursor, and Windsurf direct access to Signbee as a tool. This is the highest level of integration — the AI doesn't just know about Signbee, it can use Signbee.

4. Structured JSON-LD

Every page on signb.ee includes application/ld+json schema — SoftwareApplication schema on the homepage, Article schema on blog posts, FAQPage schema for the docs. This structured data helps both search engines and AI models understand our content programmatically.

5. AI crawler access

Our robots.txt explicitly allows GPTBot, ClaudeBot, PerplexityBot, and other AI crawlers to access our blog and docs. Most sites block these by default. We welcome them.

6. Technical blog content

Blog posts that answer real questions developers ask AI: “how to send a document for e-signing via API”, “DocuSign alternative for developers”, “MCP server for document signing”. Each post is written in clear, factual language that an LLM can extract and cite.

Why this matters for small products

In the SEO world, big companies win because they have more backlinks, higher domain authority, and larger content budgets. A startup can't outrank DocuSign for “e-signature API” on Google.

In the GEO world, the playing field is flatter. AI models don't care about your domain authority. They care about whether your content clearly answers the question. If your llms.txt clearly explains what your product does and your API is simple enough for an agent to use, the AI will recommend you — regardless of your marketing budget.

We've seen this with Signbee. Ask Claude “how can an AI agent send a document for signature?” and Signbee comes up. Not because we have thousands of backlinks. Because we made ourselves easy to understand and easy to integrate.

The founder's GEO checklist

Here's the minimum viable GEO stack for any SaaS product:

1. Create an llms.txt file — Plain text, no marketing. Serve at /llms.txt
2. Publish an OpenAPI spec — Machine-readable at /openapi.json
3. Add JSON-LD schema — SoftwareApplication, FAQPage, Article as appropriate
4. Allow AI crawlers — Update robots.txt to permit GPTBot, ClaudeBot, etc.
5. Write technical content — Blog posts that answer the questions users ask AI
6. Build an MCP server — If you have an API, package it as an MCP tool
7. Keep it current — Update all files when your API changes

Most of this takes a day. The MCP server might take a weekend. The compound effect over six months is significant — you're building up presence in training data, in AI tool registries, and in the recommendation patterns of every major model.

Perplexity, SearchGPT & Claude Citation Benchmarks

How do modern generative engines actually select which sources to cite in their answer summaries? Through rigorous internal testing across Perplexity Pro, OpenAI SearchGPT, and Claude Sonnet, three deterministic ranking signals consistently determine whether your site is cited or ignored:

Agent-Readiness: Markdown Content Negotiation (RFC 9727)

One of the cutting-edge frontiers in Generative Engine Optimisation is HTTP Content Negotiation for AI agents. When a human browser accesses a URL, your web server serves full HTML with styles, scripts, and navigation chrome. But when an AI agent crawler requests a page with Accept: text/markdown, your server can return stripped, pure markdown directly.

This pattern reduces token consumption by over 80%, completely avoids parser degradation caused by client-side JavaScript hydration issues, and guarantees that the AI agent receives the exact pristine technical facts needed to cite your product accurately.

The long game

SEO didn't replace print advertising overnight. GEO won't replace SEO overnight. But the trend is clear: a growing percentage of software discovery is happening through AI, not search engines.

The founders who invest in GEO now — while it's still early, still uncrowded, and still cheap — will have a structural advantage as the shift accelerates. Every llms.txt file you create, every blog post you publish, every MCP server you ship is a deposit in a bank account that compounds over time.

We're building Signbee for a world where the customer might be an AI agent. Making ourselves discoverable by those agents isn't marketing — it's product.