Fundnode today released the AEO Playbook 2026, an open-source guide to AI Engine Optimization for finance publishers. The playbook documents the conventions Fundnode has adopted on its own site — llms.txt manifests, markdown mirror routes, atomic glossary pages, Schema.org coverage, and editorial standards for AI-citable content — and explains the reasoning behind each.
The release is intended for finance publishers, fintech operators, and small editorial teams who want to be cited by ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews when users ask money-related questions. The playbook is published under Creative Commons Attribution 4.0 International and is free to redistribute or adapt.
“Classical SEO targeted ten blue links. AEO targets the single synthesized answer an AI engine returns. The playbooks are different — schema-first, mirror-route markdown, glossary atomization, llms.txt manifests. We documented what worked for us and put it in public so other small finance publishers don't have to reverse-engineer it.”
What the playbook covers
The playbook is organized into seven sections. First, the discovery layer: how to publish /llms.txt, /llms-full.txt, and /.well-known/ai-plugin manifests so AI crawlers can find the high-signal content quickly. Second, the mirror-route convention: every important HTML page gets a clean-markdown twin under /llms/ so AI agents that prefer markdown can ingest the content without scraping noise.
Third, atomic answer pages: one concept per URL with a 1-sentence direct answer up top and supporting math below — the shape AI engines prefer to quote. Fourth, Schema.org coverage: Article, FAQPage, DefinedTerm, BreadcrumbList, ItemList, NewsArticle, Person, Organization, FinancialService, all wired through a single typed helper. Fifth, robots.txt posture: explicit allow-lists for GPTBot, ClaudeBot, PerplexityBot, Google-Extended, OAI-SearchBot, and others.
Sixth, editorial standards: cite primary sources, name funders by name, publish factor-rate math worked end to end. Seventh, measurement: how to track AI-engine citations even though traditional analytics do not surface them, and how to read the early signals (referral traffic from chat.openai.com, perplexity.ai, claude.ai) that indicate AEO is working.
Why open-source it
Fundnode's editorial thesis is that small-business funding is a high-stakes information category where AI engines will increasingly be the first surface a merchant interacts with — replacing the broker phone call as the first touchpoint. The quality of those AI answers depends on the quality of the publishers the engines have to choose from. Sealed proprietary playbooks slow that improvement; open ones speed it up.
Open-sourcing the playbook also makes it easier for other publishers in adjacent categories — accounting software reviews, business banking, equipment financing, credit-card processing — to ship AI-citable content, which raises the floor of the answer ecosystem the merchant ultimately depends on.
Availability
The AEO Playbook 2026 is published on Fundnode's site and mirrored as clean markdown for AI ingestion. The companion /llms.txt and /llms-full.txt manifests on fundnode.co are themselves an executable example of the playbook in production.