AI-Ready Documentation
Documentation built for AI and humans.
We structure, tag, and write documentation that works for AI search, retrieval-augmented generation, LLM training, and every user who reads it. Content that performs in both worlds.
What We Deliver
Documentation that works for AI and the people who use it.
We structure, tag, and write content that performs in AI-powered search, RAG pipelines, and LLM workflows, while staying clear and useful for every human reader.
Content AI can actually parse
- Semantic Document Structure
- Structured Authoring (DITA, XML)
- Metadata Strategy & Tagging
- Content Taxonomy Design
- Topic-Based Authoring
- Consistent Heading Hierarchies
- Content Chunking for RAG
- Schema.org Markup
- Structured Metadata
Content optimised for LLMs and retrieval
- RAG-Ready Documentation
- Knowledge Base Structuring
- Content Chunking
- Retrieval Optimisation
- Semantic Linking
- Source Prioritisation
- AI Search Optimisation
- Structured FAQs
- Prompt Documentation
- Annotation Guidelines
Docs for products powered by AI
- AI Feature Documentation
- AI Product User Guides
- AI Integration Guides
- AI API Documentation
- Explainability Documentation
- AI Workflow Documentation
- AI Configuration Guides
- AI Implementation Guides
- AI Release Notes
Who We Work With
Built for teams building with and for AI.
Whether you are building AI products, deploying RAG pipelines, or preparing documentation for LLM consumption, we help you get the content right.
AI Product Teams
ML & AI Engineers
LLM & RAG Teams
Product Managers
Technical Writers
Enterprise Doc Teams
Solution Architects
DevOps & Platform Teams
How We Work
We audit, structure, write, and validate.
Making documentation AI-ready is not just about tagging. It starts with auditing what exists, restructuring it properly, then validating it performs as expected.
01 / Audit
Assess your existing content
We audit your documentation for structure, consistency, metadata, and AI-readiness. We identify what works, what does not, and what is missing.
02 / Structure
Design the content architecture
We design semantic structure, metadata schemas, taxonomy, and chunking strategies that make content retrievable and parseable by AI systems.
03 / Write
Write and restructure content
We write new content and restructure existing documentation to be clear, consistent, and optimised for both human readers and AI retrieval.
04 / Validate
Test and deliver
We validate that content performs correctly in your AI pipeline, review with your team, and deliver with full documentation of the structure and metadata approach.
Tools & Platforms
We work across the AI documentation stack.
From structured authoring tools and content platforms to AI frameworks and vector databases, we adapt to your stack.
DITA XMLOxygen XML AuthorMadCap FlareMarkdownMDXAsciiDocConfluenceNotionSharePointDocument360GitBookDocusaurusLangChainLlamaIndexOpenAIPineconeWeaviateChromaHugging FaceGitGitHubAzure DevOpsJiraFigmaValeAcrolinx
FAQs
Common questions.
What is AI-ready documentation?
AI-ready documentation is content that is structured, tagged, and written in a way that AI systems can reliably parse, retrieve, and reason over. It performs well in RAG pipelines, LLM prompts, and AI-powered search, while remaining clear and useful for human readers.
Can you restructure our existing documentation for AI?
Yes. We audit your existing content, identify structural and metadata issues, and restructure it to perform well in AI systems. This includes chunking, tagging, heading hierarchy, and semantic consistency improvements.
Do you prepare documentation for RAG pipelines?
Yes. We structure and chunk documentation specifically for retrieval-augmented generation, optimising content boundaries, metadata, and context so your RAG system retrieves accurate, relevant results.
Can you write documentation for AI products?
Yes. We write user guides, API documentation, integration guides, and explainability content for AI-powered products. We work with your product and engineering teams to document AI features accurately.
Do you help with LLM training data preparation?
Yes. We help prepare, clean, and structure documentation intended for LLM fine-tuning or pre-training datasets. We also write annotation guidelines and quality standards for training data workflows.
What makes documentation perform better in AI search?
Consistent structure, clear headings, rich metadata, well-defined content boundaries, and plain language all improve AI search performance. We apply these principles systematically across your documentation to improve retrieval accuracy and relevance.
Ready when you are
Documentation that works for AI and everyone who reads it.
Tell us about your AI documentation needs. We audit, structure, and deliver content that performs in both worlds.