Why enterprise AI pilots stall before production
They rarely fail. They go quiet. Here are the six places it happens, and what each one actually requires.
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Architecture, security, and implementation notes from production enterprise AI work.
Written for the people responsible for making these systems work: architecture decisions with their trade-offs, security findings, deployment patterns, and the parts of enterprise AI that are harder than the vendor documentation suggests.
No predictions, no vendor commentary, no roundups. Every post here contains at least one of: an architecture diagram, a measured result, a working example, or a documented failure mode. If it contains none of those, it does not publish.
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A security review checklist for enterprise AI systems
What to ask before an AI system reaches production. Written to be handed to a security team that has not reviewed one before.
ReadThe real cost of on-premise LLM inference
A structure for the comparison, and the three costs that most on-premise business cases leave out.
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Reference architectures
The designs behind our implementations, with the decisions, trade-offs, and failure modes each one addresses.
Architecture libraryPrivate vs. Cloud AI
Five deployment models compared across data boundary, cost, latency, and compliance — including where each one fails.
Compare modelsImplementation framework
Five phases with defined artifacts and a gate before anything reaches production.
The frameworkStart with an assessment, not a proposal.
A structured evaluation of your data, infrastructure, security constraints, and candidate use cases — delivered as a prioritized roadmap you own.
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