Practical notes on
AI & systems
Practical notes on AI systems integration, enterprise automation, RAG, cloud, and IoT for teams evaluating implementation.
Moving Batch Jobs to the Cloud: Scheduling, Reruns, and Recoverability
Cloud batch reliability depends on explicit business dates, idempotent execution, controlled retries, auditable reruns, and operational visibility.
Choosing among Cloud Run, Lambda, and container platforms
A practical framework for choosing an execution platform based on workload shape, integration needs, latency, cost, and operational ownership.
Mixing AWS and GCP: Define Network and Billing Boundaries First
A practical guide to routing, DNS, resilience, data transfer charges, and cost ownership in a mixed AWS and GCP architecture.
API Keys, OAuth, and Service Accounts: Drawing the Right Identity Boundaries
A practical framework for choosing and governing API keys, OAuth tokens, and service accounts across enterprise integrations.
Encryption and Key Management Checks Before Cloud Data Landing
A practical engineering checklist for choosing encryption boundaries, controlling keys, and validating recovery before cloud ingestion.
Beyond Shared Folders: Document Classification and Access Logging for Enterprise Knowledge Bases
A practical engineering guide to classification, authorization inheritance, RAG controls, and useful audit trails for enterprise knowledge bases.
Where to Place Prompt Injection Defenses
Prompt injection is not solved by one filter; this guide places controls across ingestion, RAG, tool execution, output, and operations.
Threat Modeling Before Connecting AI Assistants to Internal Systems
A practical framework for defining trust boundaries, permissions, abuse paths, and failure controls before an AI assistant can access enterprise systems.
Modbus, OPC UA, and MQTT Gateways: Making the Engineering Trade-offs
A practical framework for choosing gateway protocols based on device support, semantics, connectivity, security, control, and operations.
Handling Latency and Missing Values in IoT Dashboards
A practical approach to timestamps, quality flags, imputation, and UI design for IoT dashboards that remain trustworthy under imperfect data.
Moving Equipment Alerts from Rule Engines to AI-Assisted Triage
A practical architecture for adding contextual AI triage while retaining deterministic safety rules, auditability, and reliable fallback paths.
Edge Filtering Before IoT Sensor Data Reaches the Cloud: A Practical Engineering Guide
A practical framework for reducing IoT traffic at the edge without weakening alerts, traceability, or downstream analytics.
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