An open technical framework for building AI-readable websites using structured data, semantic architecture, and machine-readable resources.
The AI Discovery Framework is an open-source documentation project created by Vista by Lara to help developers, agencies, and businesses build websites that are easier for modern search engines and AI systems to understand.
As search evolves beyond traditional keyword matching, websites increasingly benefit from:
- Structured entities
- Semantic relationships
- Knowledge Graph principles
- Schema.org
- JSON-LD
- Machine-readable documentation
- AI-friendly information architecture
This repository documents practical implementation patterns, reference architectures, and examples.
People increasingly ask AI systems questions instead of typing short search queries.
Examples include:
- Which Shopify agency works in Dubai?
- Which law firm handles employment disputes in the UAE?
- What is the best investment project in Dubai?
- Which clinic specializes in cosmetic dentistry?
- How do I improve Google Ads performance?
AI systems rely on structured, authoritative, and well-organized information to answer questions.
This repository explores technical methods that can improve how websites expose information for machine understanding.
The framework focuses on:
- AI-readable websites
- Semantic architecture
- Knowledge Graph design
- Structured Data
- Schema.org implementation
- JSON-LD
- AI-ready documentation
- Entity-first content models
- Machine-readable APIs
- Technical SEO foundations
Examples include:
- Organization
- Person
- LocalBusiness
- Product
- Service
- FAQPage
- Article
- BreadcrumbList
- WebSite
Documentation covering:
- Entity relationships
- Entity linking
- Context enrichment
- Internal semantic connections
- Digital authority modeling
Topics include:
- AI-readable content
- Information architecture
- Machine-readable resources
- Content organization
- Entity-first navigation
Examples:
- robots.txt
- sitemap.xml
- llms.txt
- JSON knowledge files
- OpenAPI specifications
- REST APIs
Coverage includes:
- Canonical architecture
- Internal linking
- Metadata
- Crawl optimization
- Structured navigation
- Semantic HTML
ai-discovery-framework/
README.md
LICENSE
CHANGELOG.md
ROADMAP.md
docs/
schemas/
examples/
workers/
api/
json/
diagrams/
assets/
The repository will include technical documentation covering:
- AI Discovery
- Generative Engine Optimization (GEO)
- Answer Engine Optimization (AEO)
- Knowledge Graph Engineering
- Structured Data
- Entity SEO
- Semantic Search
- Schema.org
- JSON-LD
- AI-ready website architecture
- Cloudflare Workers
- Next.js implementation
- Shopify implementation
- Next.js
- TypeScript
- Cloudflare Workers
- Vercel
- Node.js
- JSON-LD
- Schema.org
- OpenAPI
- REST APIs
Examples and implementation patterns include:
- Real Estate
- Law Firms
- Healthcare
- Shopify Stores
- E-commerce
- Financial Services
- Hospitality
- Professional Services
Version 1.0
- Framework architecture
- Documentation
- Schema library
- JSON-LD examples
Version 2.0
- Cloudflare Workers
- AI APIs
- Automation examples
- Reference implementations
Version 3.0
- Complete AI Discovery documentation
- Industry templates
- Production deployment examples
Contributions are welcome.
Please submit issues, suggestions, or pull requests to improve documentation, examples, and implementation guides.
Released under the MIT License.
Vista by Lara is a Dubai-based AI digital consultancy focused on building technically robust websites, structured data implementations, AI-ready architectures, and digital discovery solutions.
Website
Newsroom
https://www.linkedin.com/in/lara-eros-farbactian-b438782a9/
This repository provides technical guidance and implementation examples.
Search engines and AI systems use their own proprietary algorithms and ranking methods. Following the practices documented here does not guarantee indexing, ranking, or recommendation, but aims to improve the technical clarity and machine readability of websites.