Practical notes on
AI & systems
Field notes from delivering AI systems integration, enterprise automation, RAG, cloud and IoT — not marketing fluff.
Implementing Data Lineage from Source Fields to Reports
Build maintainable lineage that traces management metrics through transformation logic, datasets, source fields, and accountable systems.
Preserving Numbers, Terms, and Ownership in AI Summaries
A practical engineering approach to keeping critical figures, contractual conditions, and accountable owners intact in AI-generated summaries.
Reviewing Data and Model Terms in Enterprise AI Contracts
A practical engineering checklist for reviewing data use, model changes, security controls, and exit terms before buying enterprise AI.
Using Distributed Tracing Across APIs and Queues
A practical guide to preserving trace context, measuring queue delay, and diagnosing latency across synchronous and asynchronous services.
Automating Invoice and Order Reconciliation with Exception Rules
A practical framework for matching invoices to orders using normalized data, explicit tolerances, auditable rules, and focused human review.
Segmenting Cloud Networks for Services, APIs, and Databases
A practical approach to isolating public services, internal APIs, and databases without making cloud operations unmanageable.
Auditing Dormant and Overprivileged Service Accounts
A practical method for inventorying machine identities, validating real usage, and reducing access without disrupting production systems.
Provisioning, Rotating, and Revoking IoT Device Certificates
A practical approach to building an auditable IoT certificate lifecycle that survives outages, hardware limits, and security incidents.
Keeping Customer and Product Master Data Consistent
A practical framework for aligning customer and product records across CRM, ERP, commerce, and data platforms.
Keeping Integrations Running During an ERP Upgrade
A practical guide to integration boundaries, data reconciliation, phased cutovers, and recovery planning during an ERP upgrade.
Choosing Enterprise Workloads for Small Language Models
A practical framework for selecting small-model workloads based on scope, latency, cost, deployment constraints, and failure risk.
Validating AI Tool Calls Before They Reach Backend Systems
A practical, layered approach to validating AI-generated tool calls before they can affect enterprise systems.
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