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Build agent-ready architecture for the AI-powered web.
Structural and metadata architecture designed for AI agents, crawlers, and answer systems to discover, interpret, and use.
Restructured the site's structured data and semantic HTML, making key organisational information easier for AI systems to interpret.
Pricing Packages
LLM Crawlability Audit
Diagnostic review of AI crawler access, markdown availability, and machine-readability signals.
- AI crawler access & robots.txt policy analysis
- Semantic HTML structure & heading hierarchy evaluation
- llms.txt standard check & specification gap analysis
- Context density & AI retrieval readiness assessment
- Prioritised remediation roadmap
Agent-Ready Restructuring
Implementation of structured metadata, entity mappings, and machine-readable endpoints across core pages.
- Structured data and entity relationship mapping
- llms.txt standard implementation & routing setup
- Machine-readable table & data structures
- Structured entity linking & schema validation
- Post-deployment AI crawler verification report
Machine-Readable Content
For complex sites needing structured content endpoints, Markdown resources, or integration with an existing AI retrieval system.
- Tailored scope based on your existing system
- Raw markdown API endpoint creation
- Custom AI application & RAG context chunking optimization
- Machine-readable resource & schema architecture
- Technical documentation & team handoff
What you
pay for
The work is clear from the start.
You know what it costs.
Choose a service and the price is already there.
For something custom, define what you need first. Then see the price.
If the work changes, the price comes with it.
Ranti Deb
Founder, Web Specification Studio
*All prices are in USD. Payments are processed through Paddle.
Ongoing Retainers & Support
If you need ongoing LLM indexation monitoring, monthly context reviews, structured data maintenance, or continuous priority implementation support, we offer flexible monthly retainer packages.
Explore monthly retainer optionsHow it works
Audit
We review crawler access, content structure, structured data, entity signals, and machine-readable resources.
Findings
We document what we found, why it matters, and which structured data and machine-readable improvements are worth making.
Implementation
We implement the agreed structural, metadata, llms.txt, and machine-readable improvements.
Validation
We verify the resulting markup, resources, and access rules against documented requirements.
Frequently Asked Questions
What is the difference between SEO and AEO?
Traditional SEO focuses on how search engines crawl, understand, and index websites and how those signals influence search results. AEO focuses more broadly on how answer systems and AI-powered tools discover, interpret, and use information from websites. The two overlap considerably, so strong technical SEO is often part of a strong AEO foundation.
How do AI systems consume websites differently from search engines?
There is no single retrieval method used by every AI system. Some use dedicated crawlers, some fetch pages in response to user requests, and others rely on search or retrieval systems. We focus on making your information clear, structured, accessible, and machine-readable rather than optimising for one specific system.
Will implementing AEO impact human readers or site design?
No. Enhancing semantic HTML, adding JSON-LD schemas, and exposing `llms.txt` endpoints happens entirely under the hood. These changes can also support accessibility and technical SEO when implemented correctly.
What is the `llms.txt` standard and why is it important?
`llms.txt` is an emerging convention for providing a concise, Markdown-formatted overview of a website and links to selected resources. It can provide a useful machine-readable entry point, but adoption varies across AI systems, so we treat it as one tool within a broader AEO strategy rather than a guarantee of AI visibility.
How do you protect our private or proprietary content from unauthorized AI scraping?
We review robots.txt directives, HTTP response headers, and relevant application routes. Where appropriate, we can control automated access to public resources and restrict access to sensitive routes for identified AI crawlers and services. Robots.txt is treated as an instruction mechanism, not a security boundary.
What is structured data and entity relationship mapping?
Structured data is machine-readable schema markup (such as JSON-LD) that explicitly defines relationships between your organisation, products, services, authors, and documentation. Structured data makes these entity relationships explicit to any AI system, crawler, or answer system consuming your site's information.
Do we need to rebuild our website to support Answer Engine Optimization?
No. Our AEO implementations are non-destructive and layer seamlessly onto your existing stack. Whether you use React, Next.js, Astro, WordPress, or custom backends, we apply schema layers, header configurations, and markdown endpoints without disrupting your existing code or design.
Feature Comparison
| Feature | LLM Crawlability Audit | Agent-Ready Restructuring | Machine-Readable Content |
|---|---|---|---|
| Business Value & Risk Reduction | |||
| AI system discoverability | |||
| AI retrieval readiness | |||
| Brand entity disambiguation & relationship clarity | - | ||
| Prioritised technical action plan | |||
| Technical Scope | |||
| AI crawler access & robots.txt policy analysis | |||
| Semantic HTML structure & heading hierarchy | |||
| llms.txt standard implementation | - | ||
| Structured data and entity relationship mapping | - | ||
| Machine-readable table & data structures | - | ||
| Structured entity linking & schema validation | - | ||
| Raw markdown API endpoints | - | - | |
| Custom AI application & RAG context chunking optimization | - | - | |
| Machine-readable resource & schema architecture | - | - | |