I build and scale infrastructure that uses AI models-ChatGPT, Claude, Perplexity, Grok-as reliable tools inside systems that have to work: multi-language platforms, SEO architectures, data pipelines, and real-world services.
House of Bitcoin started as a simple idea and scaled into a proof-of-concept that demonstrates every aspect of production infrastructure: AI integration, multi-language deployment, domain strategy, CDN optimization, technical SEO, and continuous reliability. It's the live testbed for integration patterns.
A simple Astro site deployed on Railway, focused on consolidating Bitcoin knowledge: events, organizations, treasury data, and community resources. The vision was clear-create a reliable single source for Bitcoin information.
This baseline demonstrated the core need for performance. From day one, PageSpeed metrics were a priority-not optional, not later.
Search traffic patterns showed significant international interest. Rather than manually translate, the system scaled to support multiple languages with automated translation pipelines via Supabase.
Frontend moved from Railway to Render with dedicated infrastructure. A Supabase database became the source of truth for all content, keeping translated versions synced and searchable across regions.
To keep evergreen content fresh and comprehensive, frontier models were integrated into content creation and analysis workflows. This wasn't about replacing human knowledge-it was about automating the repetitive work of synthesis.
Structured prompts generate Bitcoin event summaries, analyze treasury movements, and cross-reference organizational updates. All outputs are validated and formatted for consistent presentation.
Ten-plus years of optimization experience applied systematically: efficient resource loading, strategic CDN usage, minimal JavaScript, optimized images, and caching strategies.
The result: consistently high PageSpeed scores across desktop and mobile, translating to faster load times, better user experience, and SEO advantage. This is not luck-it's intentional architecture.
The original premium domain was essential to the vision and took priority to secure. Once secured, the opportunity emerged: a Bitcoin domain collection could provide additional touchpoints, SEO coverage, and specialized resource hubs.
21 additional domains now point into a unified knowledge graph. This is domain strategy, not link spam-each property serves a specific audience while feeding authority back to the primary hub.
Not just "how to use AI," but how to build systems that scale: database architecture, multi-language deployment, CDN strategy, domain portfolio management, performance optimization, and AI integration working as one cohesive system. This is the skill set that matters to employers building reliable infrastructure.
At work, frontier models are tools-the same way we use APIs, databases, or CDNs. The skill isn't knowing what ChatGPT can do in a chat window. It's understanding how to make them reliable, cost-effective, and useful inside infrastructure that has to work at scale.
Integrated ChatGPT, Claude, Perplexity, and Grok into production systems using OpenRouter and direct API access. Handles model selection, fallback logic, cost optimization, and performance monitoring.
Rather than parsing freeform text, use structured prompts and JSON schema to guarantee consistent, predictable output. Essential for infrastructure that depends on correctness.
Models integrated as part of larger data pipelines: pulling context from databases, external APIs, and services, then processing through frontier models with Supabase, webhooks, and automation workflows.
Multi-language platforms, CDN strategy, domain portfolio management, and performance optimization. Systems that serve multiple regions efficiently while maintaining speed and reliability.
If you're building infrastructure that scales-with or without AI-I've solved these problems before.
Review how AI fits into your system. Is the integration pattern sustainable? Are you optimizing for cost and latency? Handling errors properly?
Setting up multi-model routing, provider management, API key rotation, fallbacks, cost control, and structured output pipelines.
Multi-language deployment, CDN strategy, domain portfolio management, database architecture, and performance optimization.
Debugging latency, implementing caching, designing fallback chains, monitoring, and optimizing AI-dependent systems for production.