Field notes from the Alpine Edge team on running secure cloud infrastructure, deploying Private AI built around your data requirements, and turning manual processes into automated ones. Written by the engineers doing the work.
What private AI actually costs to run, how it compares with hosted APIs at different volumes, and the conditions that make self-hosting worth the engineering.
A practical way to compare AI use cases using process costs, data readiness, error risks and the value a pilot should demonstrate.
What to include in an AI consulting engagement, how to assess costs and timelines, and which questions to resolve before signing.
Nine questions to compare AI consulting partners, clarify who owns implementation and check how the system will be supported after launch.
The assets and decisions an AI consulting project should produce, from the first use case assessment to implementation and handover.
How AI-native applications turn user requests into workflows, and what their architecture needs to handle data, permissions and errors.
How temperature and water leak sensors connect to remote monitoring, assigned alerts and maintenance tickets across multiple sites.
A sample first-month plan for embedded AI engineering, from understanding the workflow to testing it with users and deciding what comes next.
How to assess business process automation, calculate the return after review costs and decide which steps need human judgement.
Four threads, all drawn from client work rather than press releases.
Running open models inside your own environment, EU data residency, and what GDPR actually requires of an AI deployment.
Finding where AI creates value: process discovery, automation opportunity analysis, workflow redesign, and adoption.
Migration strategy, infrastructure as code, CI/CD, observability, and the operational habits that keep systems boring.
GDPR and ISO 27001 as engineering problems: controls, evidence, and audit trails that survive a real assessment.
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