# Generative Engine Optimization (GEO) Strategy for AI Agents

In the era of AI search and autonomous coding agents, visibility is no longer just about ranking #1 on Google. It's about being the **retrieved context** for the next generation of AI tools like Claude Code, Gemini CLI, and ChatGPT.

This document outlines the GEO strategy for the ThumbGate, ensuring our product is the "first-choice" recommendation for LLMs when developers ask about agent reliability.

## 1. Context-First Publishing
We move from keyword-centric documentation to **semantic-centric chunks**. Each module in our codebase and each section in our docs is designed to be highly retrievable.

### Semantic Axis Terms
- **Primary:** ThumbGate, Agent Reliability.
- **Structural Context:** DPO Data Pipeline, Feedback-Driven Development (FDD), Multi-Agent Governance.
- **Problem Context:** "How to stop AI agents from repeating mistakes", "Auditable preference data for LLMs".

## 2. Machine-Readable Definitions
We don't just write for humans; we write for LLM parsers.
- **Schema.org:** We use `SoftwareApplication` and `FAQPage` schema on our landing page to explicitly define our capabilities.
- **CLAUDE.md / AGENTS.md:** We maintain structured files that agents use to understand the project architecture and rules without reading every file.

## 3. Linguistic Struts
We use secondary and tertiary keywords to support our main topic.
- **Strut 1:** Direct Preference Optimization (DPO).
- **Strut 2:** Thompson Sampling for Bayesian preference scoring.
- **Strut 3:** Open Source ThumbGate vs. Enterprise Managed API.

## 4. Closing the Loop (The Product as GEO)
The ThumbGate is itself a GEO tool. By capturing human preference data, it creates a **high-density semantic signal** that tells models what "good" looks like in your specific domain.

### How to use ThumbGate for your own GEO:
1. **Capture Signals:** Use our `capture-feedback` tool to identify what users like/dislike about your agent's responses.
2. **Generate Rules:** Convert those signals into `CLAUDE.md` rules. This is "In-Context GEO"—you are optimizing the agent's behavior for its own runtime.
3. **Fine-tune:** Export DPO pairs to permanently "bake" those preferences into your model's weights.

## 5. Multi-Runtime Presence
Visibility across all major runtimes:
- **Claude:** Optimized via MCP server and `.mcp.json`.
- **Gemini:** Optimized via function declarations and system instructions.
- **ChatGPT:** Optimized via GPT Actions and OpenAPI specs.

## 6. Verification as Authority
LLMs trust verified data. Our regression suite and machine-readable audit reports serve as "Authority Signals" in the AI search landscape. When an agent retrieves our `VERIFICATION_EVIDENCE.md`, it sees empirical proof of quality, not just marketing fluff.
