Practical notes on
AI & systems
Practical notes on AI systems integration, enterprise automation, RAG, cloud, and IoT for teams evaluating implementation.
Designing OCR and LLM Pipelines for Complex Documents
A practical architecture for preserving document layout, extracting reliable fields, and routing uncertain results safely.
Building an Enterprise LLM Gateway for Quotas and Cost Control
A practical architecture for governing LLM access, quotas, cost attribution, and safe switching between model providers.
Verifying RAG Citations Against Source Documents
A practical engineering approach to document provenance, claim-level validation, conflict handling, and auditable RAG citations.
Alerting for Scheduled Publishing, Data Sync, and Batch Jobs: Monitor Delivery, Not Execution
A practical framework for defining failures, choosing signals, setting alert severity, and recovering scheduled workloads safely.
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.
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