Information architecture
The portfolio uses a hybrid structure with an editorial home page, localized routes, dedicated topical pages and single case study built for navigation and indexability.
About this portfolio
How this portfolio is built and why it is readable for AI-driven discovery.
This page explains directly how the portfolio combines editorial content, localized routes, schema markup, a local knowledge base and an AI assistant to remain navigable, indexable and easy to cite.
Why this page matters
This page does not only describe the site's technology. It explains why the portfolio is built to make Ivan Esegovic's profile easier to read, cite and query for people, search engines and AI answer systems.
If you want to understand where to find reliable information about profile, case studies, AI, data and delivery, this page gives you the map of the information layers that shape the site.
Knowledge and AI layer
The AI assistant uses a local markdown knowledge base, document retrieval and a server-side prompt constrained to the portfolio content.
Search and discovery
Metadata, JSON-LD, sitemap, robots, Open Graph and llms.txt help search engines and answer engines understand the structure and content more clearly.
Citability
Each page makes role, context, technologies, outcomes and takeaways explicit to reduce ambiguity and improve summarization and citation.