Practical notes on
AI & systems
Field notes from delivering AI systems integration, enterprise automation, RAG, cloud and IoT — not marketing fluff.
Executable Details to Include in Operations Handoff Documents
A useful operations handoff enables engineers to act, verify outcomes, contain risk, and recover without relying on the original delivery team.
Designing Controlled Degradation and Failover for Model Provider Outages
A practical framework for classifying failures, routing models, preserving transaction safety, and recovering AI services with confidence.
Monitoring and Rollback for Failed RAG Knowledge Base Updates
Design observable, testable, and reversible RAG update pipelines that keep defective data and indexes out of production.
Engineering Questions to Ask During AI Project Discovery
A practical discovery framework for clarifying AI workflows, data readiness, integrations, risks, acceptance criteria, and operational ownership.
Incident Severity and Recovery for Production AI Systems
A practical framework for classifying, containing, recovering, and learning from incidents in enterprise AI systems.
After the PoC: Designing Production Ownership and an Operations Budget
A practical framework for assigning ownership, forecasting operating costs, and setting production gates for enterprise AI systems.
What Processes Should You Inventory Before Adopting an Enterprise AI Assistant?
A practical framework for assessing workflows, data, permissions, integrations, and exceptions before building an enterprise AI assistant.
Build or Buy an Internal AI Platform? A Practical Decision Framework
Evaluate differentiation, integration depth, governance, operating capability, and total cost before choosing a build, buy, or hybrid AI platform strategy.
Building an AI Adoption Roadmap That Avoids Demo-Only Projects
A production-minded AI roadmap must address workflow value, data, integration, risk controls, and ongoing operations—not just model capability.
Automated Support Replies: Designing Templates, Retrieval, and Human Confirmation
A practical engineering guide to combining controlled templates, grounded retrieval, and risk-based human review for reliable support automation.
When Low-Code Automation Should Become a Production System
A practical framework for deciding when a low-code workflow needs production-grade architecture, controls, and operations.
Compensation Patterns for Form, Approval, and Notification Automation
Use idempotency, transactional outboxes, and explicit compensation to make cross-system automation recoverable and auditable.
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