Practical notes on
AI & systems
Field notes from delivering AI systems integration, enterprise automation, RAG, cloud and IoT — not marketing fluff.
Turning Manual Data Checks into Maintainable Automated Rules
A practical guide to extracting human judgment, designing rule contracts, and deploying automated data checks with safe failure handling.
Designing a Trusted Data Layer for Reports and AI Q&A
A practical architecture for giving dashboards and AI assistants consistent, traceable, and governed access to enterprise data.
Monitoring Schema Drift in Data Pipelines: Contracts, Detection, and Safe Recovery
A practical framework for detecting schema drift, assessing downstream impact, and recovering pipelines without losing data.
Event Naming and Versioning for Cross-Team Integrations
A practical framework for naming events, evolving schemas, and retiring versions without creating hidden cross-team dependencies.
Turning Unstructured Documents into a Searchable Knowledge Base
A practical guide to document ingestion, extraction, chunking, hybrid retrieval, access control, and ongoing knowledge-base quality.
Practical Retry and Idempotency Design for Webhooks
A practical guide to response semantics, backoff, idempotency keys, replay controls, and safe recovery for production webhook integrations.
Safe Integration Patterns When Legacy Systems Have No API
A practical guide to choosing and securing database, file, CDC, and UI-based integrations for legacy systems without APIs.
Integrating Conversation History Across LINE, Websites, and Helpdesks
A practical guide to identity resolution, event modeling, delivery architecture, security, and phased rollout for cross-channel conversation history.
Master Data Design for ERP and CRM Synchronization
A practical framework for defining ownership, identifiers, mappings, conflicts, and operations across ERP and CRM master data.
A Practical Debugging Workflow for Inconsistent Enterprise AI Answers
A systematic workflow for isolating answer-quality problems across prompts, retrieval, models, tools, permissions, and enterprise integrations.
From Chatbot to Workflow Agent: When to Upgrade
Before letting enterprise AI take action, evaluate the workflow, system boundaries, operational risk, and real cost of integration.
Building and Maintaining Evaluation Sets for RAG Systems
A practical approach to sampling cases, labeling evidence, measuring each RAG layer, and governing evaluation sets as systems evolve.
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