Practical notes on
AI & systems
Practical notes on AI systems integration, enterprise automation, RAG, cloud, and IoT for teams evaluating implementation.
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.
Managing and Versioning Prompts in Production
Prompts need the same operational discipline as code when AI features must survive model changes, audits, and real user traffic.
The Cost and Trade-Offs of Self-Hosting an LLM
Self-hosting an LLM is not just a GPU decision; it changes cost, operations, governance, and integration responsibilities.
Why Data Governance Is a Prerequisite for AI Adoption
Data governance is not paperwork; it is the engineering foundation that lets enterprise AI use data safely and reliably.
Connecting ERP Data Into an AI Assistant, Safely
A safe ERP-connected AI assistant needs clear data boundaries, enforced permissions, auditable access, and careful choices about retrieval.
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