Blog
Technical guides from 20+ AI systems built end-to-end. RAG pipelines, agent orchestration, LLM integration, and Docker deployment.
A growing share of your buyers never see a list of blue links — they read an answer synthesised by ChatGPT, Perplexity, or Google's AI Overview. Generative engine optimization (GEO) is the discipline of being the source that answer is built from: crawler access, llms.txt, extractable content, and structured authorship. From making this portfolio citable.
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Every AI project has a moment where someone says 'it works' after watching it handle three or four inputs. That's a demo, not a measurement. This guide covers how to build evaluation into an AI system from the start — what to actually measure, how to score it without burning a budget, why LLM-as-judge is so easy to misuse, and how regression testing and production signals catch what offline numbers miss.
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Most LoRA tutorials show you how to run a training script. The hard parts are upstream: knowing when LoRA is the right tool, building a dataset that actually teaches the model what you want, and verifying afterwards that you didn't just memorise noise. This guide covers the full loop from problem framing to deployment, based on production LoRA training across language and image models.
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Most AI security writing in 2026 stops at prompt injection. If you self-host the stack, the larger attack surface is everything underneath the model. Container hardening, network isolation, Ollama specifics, vector database security, and supply-chain hygiene — from operating a 20+ app self-hosted AI fleet on Hetzner.
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EU data sovereignty for AI in 2026 is a procurement filter, not an architecture preference. EU region on a US hyperscaler stopped being an answer after Schrems II and FISA 702 reauthorisation. A working operator's view of the legal stack, why self-hosting on European infrastructure is the path of least legal resistance, and where it doesn't help.
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Most RAG security advice stops at 'sanitize your prompts.' Production RAG has a larger attack surface: injected instructions inside retrieved documents, row-level access leaks through vector search, PII surfacing in generated responses, and audit gaps that fail compliance reviews. This guide covers the specific controls that actually close those gaps.
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Most AI projects die somewhere between the impressive demo and the production deployment. The failure modes are predictable, and the fixes are not about better models.
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The EU AI Act is live. Most of what you read about it is either panic or hand-waving. This guide covers what actually matters for teams building and deploying AI systems in Europe.
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A maritime services company needed to process compliance documents, risk assessments, and operational reports faster. We built a self-hosted AI system with four analysis modes that reduced document review from 2 hours to 3 minutes.
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Running AI models in Docker containers is straightforward. Running them reliably in production with proper security, monitoring, and resource management is the hard part. Patterns from 82 production containers.
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Most document processing demos show a clean PDF going in and structured data coming out. Production document AI is harder. This guide covers the architecture that makes it work.
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Running your own LLM sounds great until you see the GPU bill. Using cloud APIs sounds easy until you read the GDPR fine print. This guide compares both approaches honestly.
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Every business wants AI capabilities. Most LLM integration projects fail not because the models are bad, but because the integration is poorly architected. This guide covers practical patterns for connecting LLMs to your existing systems — from API selection to local inference to production deployment — based on 7 live applications.
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Building a single AI agent that demos well is straightforward. Building multi-agent systems that run reliably in production — handling failures, maintaining state, executing tasks in parallel, integrating with real business systems — is a different engineering problem entirely. This guide covers how to do it right.
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Most RAG tutorials show you how to get something working in a notebook. Production RAG is a different problem. This guide covers the architecture decisions that determine whether your RAG system scales: document processing, chunking strategy, vector store selection, hybrid retrieval, and response quality. Built from 20+ production deployments.
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