From six-week model releases to a governed one-day path
A SageMaker and MLflow delivery plane with reproducible features, approval gates, canaries and automatic rollback.
Not a logo wall. A field library of the constraints, architecture decisions, safety controls and measurable outcomes behind AI, cloud, security, data and ML systems.
A SageMaker and MLflow delivery plane with reproducible features, approval gates, canaries and automatic rollback.
Unit economics, policy-as-code and workload-aware scaling rebuilt the AWS estate around cost per transaction.
A multi-agent clinical extraction system with confidence routing, human review and an evidence trail for every tool call.
A contract-first Kafka pipeline feeding operational views and a governed lakehouse without dual business definitions.
Edge buffering, streaming feature extraction and fleet-aware anomaly scoring across eleven manufacturing plants.
Signed edge models, offline inference and MES-safe alerts gave operators time to intervene before equipment failure.
Golden datasets, prompt versioning, model routing, MCP tool contracts and trace-based release gates turn AI demos into owned software.
Private networking, workload identity, policy-as-code and versioned Foundry agents across a regulated subscription estate.
A feature-consistent training and serving path with drift detection, fairness checks and instant model rollback.
A paved-road developer platform combined GitOps, ephemeral environments, progressive delivery and cost-aware Kubernetes.
Reusable service templates, GitOps promotion and Vault-backed secrets made daily delivery auditable by default.
The six homepage stories preserve the site's existing outcomes. New Azure, GCP and AI-harness stories are explicitly labeled composite patterns until client-approved evidence replaces their illustrative benchmarks.