Patterns from building and operating software.
Implementation-focused notes on AI architecture, integrations, computer vision and the details that matter in production.
Enterprise Knowledge Ownership: Scoping Update Workflows and Content Correction Lifecycles
Establish content ownership, review SLAs, and correction lifecycles to prevent stale documents in enterprise search repositories.
Production-Ready AI: Defining Acceptance Gates for Operational Workflows
A convincing AI demo provides no release criteria. Learn how CTOs can build rigorous acceptance gates, accuracy baselines, and escalation paths.
Modernization Cutover: Parallel Runs, Reconciliation and Rollback
A modernization cutover plan covering system-of-record ownership, comparison windows, reconciliation tolerances, unresolved exceptions and rollback limits.
AI Acceptance Tests: Proving a Workflow Is Ready for Production
A convincing demo provides limited assurance for AI accuracy or operational risk. Learn how to define representative test sets, baseline metrics, failure severities and human review before production release.
SaaS Tenant Isolation Beyond the Database
Protect SaaS tenants across databases, caches, queues, file storage and support tools by carrying verified tenant context through every application boundary.
Integration Contracts: Design for Failure
Design enterprise integrations around explicit contracts, versioning, reconciliation and failure ownership so connected systems remain dependable when services fail.
Document Intelligence: From Extraction to Verified Records
Design document intelligence pipelines that preserve source evidence, validate extracted fields and route uncertainty to review before creating business records.
Reliable Automation: State, Retries and Recovery
Engineer reliable automation with durable states, idempotent operations, bounded retries and recovery tools that make partial failures visible and manageable.
Focused on decisions behind real systems.
AI boundaries
Where probabilistic interpretation should stop and deterministic software should begin.
Integration resilience
Retries, idempotency, state and observability across connected systems.
AI infrastructure
The processing pipelines and operational software around model workloads.