AI Development
Articles
Articles on building AI systems: implementation guides, architecture choices, integration with existing systems and the engineering behind production AI.
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This category covers the build side of AI: implementation guides, architecture decisions, integration with the systems a company already runs, and the engineering practices that separate a demo from software a team can operate. The recurring theme is that the model is usually the smallest part of the work; the pipelines, permissions, evaluation and monitoring around it decide whether the system survives contact with production.
The articles are written for CTOs, engineering leads and technical founders who want enough depth to challenge a vendor or brief their own team. They cover what to build in-house versus buy, how long the realistic timelines are, and which corners cannot be cut. For the delivery side, from proof of concept to a production system with a handover, see the service page below.
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Enterprise AI Solutions: Building AI at Scale for Organizations
AI Proof of Concept: Validate AI Ideas Before Investment
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AI Audit Checklist: Where AI Actually Moves the Needle
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AI Implementation for the German Mittelstand: Where It Works, Where It Doesn't, and How to Start
Why 95% of AI Pilots Fail — And What the 5% That Succeed Are Doing Differently