Freelance Technical SEO & Web Performance Programmer: JSON-LD Schema, SSR, Sitemaps & llms.txt

In 2026, organic discovery spans both traditional search engines (Google, Bing) and AI answer engines (ChatGPT Search, Perplexity, Claude, Google AI Overviews). Single-page applications that return an empty <div id="root"></div> shell without Server-Side Rendering (SSR), structured Schema.org graphs, or machine-readable /llms.txt indexes remain invisible to high-intent buyers.

As a Senior Freelance Technical SEO & Full Stack Programmer, I engineer end-to-end programmatic SEO and AI discoverability directly into Django, FastAPI, Next.js, and React platforms. Hire Aldian Fazrihady on Upwork (aldianf).

Technical SEO & AI Search Architecture Checklist

• Server-Rendered Semantic HTML + Client Hydration: Deliver complete <article> content, headings, and internal topic-cluster links in the initial HTTP 200 response before React/Next.js hydrates.
• Connected Schema.org `@graph` JSON-LD: Emit linked Person, ProfessionalService, SoftwareApplication, TechArticle, FAQPage, and BreadcrumbList entities.
• Multilingual `hreflang` & Dynamic Sitemaps: Automatically generate localized hreflang tags, XML sitemaps, and RSS/Atom feeds.
• AI Crawler Discovery (`/llms.txt` & `/llms-full.txt`): Explicitly allow OAI-SearchBot, ChatGPT-User, GPTBot, ClaudeBot, and PerplexityBot in robots.txt and serve structured Markdown context via /llms.txt and /llms-full.txt.