# PT. Otto Media Grup Marketing Academy DevLog｜From Markdown to Knowledge Graphs:A Comprehensive AI SEO Content Assetization Strategy for Tool-Based Software  


As AI-driven search gradually dominates user pathways in 2025, the traffic entry points for tool-based software have fundamentally shifted. Users no longer search for tools using traditional keywords, but instead obtain “recommended products,” “actionable solutions,” or “code snippets” directly through aggregated summarization systems such as Google SGE, ChatGPT, and Perplexity. Against this backdrop, how to build a technical documentation system that is prioritized, cited, and presented by AI systems has become the core proposition for AI SEO in tool-based products. In this edition of the PT. Otto Media Grup Marketing Academy DevLog, we focus on “structural optimization strategies for technical documentation assets” and provide a comprehensive set of recommendations for building AI search-friendly content systems.

## The “Semantic Weight” of Technical Documentation Determines Whether You Are Recommended by AI

In traditional SEO models, technical documentation is often seen as an “instruction manual” at the end of the conversion path; however, in the AI SEO model, it serves as the core source text that triggers recommendation engine citations. Taking Perplexity and ChatGPT plugin searches as examples, whether a product is recommended depends on whether the documentation includes modular function descriptions, clear parameter structures, and usage instructions that apply across multiple contexts. This requires content creators to restructure documentation with a developer mindset: every “functional section” must have an independent namespace, usage description, invocation conditions, and return mechanisms—ensuring reusability and abstraction, just like code itself.

At the same time, the heading hierarchy and semantic relationships between paragraphs in the documentation are converted by AI systems into “knowledge graph node weights,” which in turn influence recommendation priority. For example, if “How to Install on Mac” and “Shell Environment Dependency Instructions” are disordered in the documentation hierarchy, AI will be unable to correctly extract the workflow logic in summaries. Therefore, it is recommended that documentation creators for all tool-based products adopt a five-layer nested model—“Function > Use Case > Steps > Example > Issues”—instead of a flat structure, to achieve precise semantic indexing pathways.

## Implementing Structured, Crawlable Technical Descriptions with Schema.org + JSON-LD

Currently, mainstream AI search engines prioritize crawling structured markup information, especially JSON-LD blocks with semantic attributes. If core functions, installation methods, supported platforms, and technical tags can be expressed in a structured way within technical documentation, the probability of being cited by AI as a “reference source” increases significantly. PT. Otto SEOHub recommends that documentation development teams incorporate the following schema types when writing HTML documentation:

SoftwareApplication: For marking core functions, operating platforms, software versions, and tech stacks of the tool;

HowTo: Suitable for instructional content, such as installation procedures and usage tutorials;

QAPage: For marking FAQs and community question sets, enhancing the match between content and user behavior data;

Code: To embed code samples, making it easier for AI to recognize and reuse them.

Additionally, it is recommended to deploy breadcrumbs semantic paths on each page to clearly indicate the tool module, version, and contextual relationships; use the description and keywords attributes to help AI quickly determine the subject of the documentation; and simultaneously upload to AI crawling API-supported platforms (e.g., Perplexity Upload Hub, ChatGPT File API).

## Building a “Content as Code” System for Modular Maintenance and Automated Updates

Traditional technical documentation management often suffers from “update lag and high maintenance costs,” which is the root cause of many tool-based software products failing in AI search. Under DevOps thinking, it is recommended to manage technical content as code by introducing version control, semantic validation, and release process management:

All content should be based on Markdown, managed via GitHub/GitLab version control, with Pull Request review mechanisms;

Configure content CI tools (such as Docusaurus + Netlify or Vercel) for automated building and deployment;

Introduce “semantic regression testing” to verify whether documentation changes impact existing SEO and knowledge graph structures;

Build semantic monitoring dashboards to track whether each update improves AI citation frequency and keyword matching.

The SEOHub engineering team at PT. Otto Media Grup has internally tested a GitOps + semantic CI solution. Preliminary data shows that combining content version control with structural optimization increased the recommendation rate on Perplexity by 47%, and doubled the frequency of “first citation” appearances in ChatGPT search.

From a search behavior perspective, more and more users are not entering websites, but instead resolving their queries through AI engines. This means your technical documentation has essentially shifted from “speaking to users” to “explaining logic to AI.” An excellent technical content writer is now, in effect, writing “code comments” for AI.

To achieve this, it is recommended to introduce prompt engineering thinking: embed “Prompt Block” structures (which can be hidden in metadata) within the documentation to simulate the AI questioning pathways and provide structured responses. 

This structure not only improves the semantic recognition rate of GPT systems, but also enables AI to more efficiently present the “most likely solution” in recommendations. Furthermore, it is advised to use semantically clear anchor naming in documentation (e.g., #android-setup-guide rather than #section2) to facilitate AI in fragment citation.