Practical notes on
AI & systems
Field notes from delivering AI systems integration, enterprise automation, RAG, cloud and IoT — not marketing fluff.
Running Staged Acceptance for an AI Project
AI acceptance should verify requirements, data, models, integrations, and operations in stages instead of waiting until launch.
Picking Your First AI Project as an SME
Your first AI project should start with a frequent, measurable, low-risk workflow, not with the flashiest model.
Process Automation vs RPA vs AI: How to Choose
A practical engineering guide to choosing process automation, RPA, or AI based on stability, integration, exceptions, and maintenance cost.
Cost Traps in Serverless Architectures, and How to Avoid Them
Serverless can reduce operational work, but cost control still depends on workload shape, boundaries, observability, and governance.
How to roll out document automation without errors
Document automation works best when templates, data, validation, review, and integrations are designed as one controlled workflow.
AWS or GCP for Taiwan SMEs: A Practical Engineering View
A practical comparison of AWS and GCP for Taiwan SMEs, covering latency, data residency, AI services, cost, and operations.
Designing Audit Trails and Traceability for AI Output
AI systems need evidence chains that explain how an output was produced, what data shaped it, and who acted on it.
The Security Baseline for Enterprise AI
Before scaling enterprise AI, define data boundaries, identity controls, model/vendor rules, and auditability as engineering requirements.
When edge computing is actually worth it
A practical engineering view on when IoT workloads should run at the edge instead of only in the cloud.
Choosing a Time-Series Database for IoT: Start With Data and Operations
A practical engineering guide to selecting a time-series database for IoT workloads, from ingest patterns to retention and operations.
Integrating MQTT and OPC-UA on the Factory Floor
A practical guide to combining OPC-UA and MQTT for factory data, edge gateways, cloud systems, and maintainable operations.
Handing Off from AI to a Human Agent, Safely and Smoothly
A smooth AI-to-human handoff depends on clear escalation rules, safe context sharing, and operational systems that agents can actually use.
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