Production outcomes + clearly marked architecture patterns

Systems designed to move a business metric.

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.

11 engineering storiesAWS · Azure · GCPAgents · ML · Security · DataArchitecture, not ad copy
Evidence library

Explore by engineering problem

Showing 11 studies
Pharma · MLOps

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.

6wk→1drelease cycle
24model services
SageMakerMLflowKubeflowOpenLineage
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FinTech · FinOps & resilience

Cost control without trading away banking-grade reliability

Unit economics, policy-as-code and workload-aware scaling rebuilt the AWS estate around cost per transaction.

$240Kannual savings
99.99%availability
AWSTerraformOPAGrafana
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Healthcare · AI agents

200+ governed agents for clinical data operations

A multi-agent clinical extraction system with confidence routing, human review and an evidence trail for every tool call.

200+agents live
94%accuracy
BedrockAgentCoreRAGGuardrails
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Retail · Streaming lakehouse

Live inventory and pricing signals instead of yesterday's batch

A contract-first Kafka pipeline feeding operational views and a governed lakehouse without dual business definitions.

<1 secdashboard latency
4.8Mevents/hour
KafkaDatabricksdbtRedshift
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Industrial · IoT intelligence

A billion daily signals converted into maintenance decisions

Edge buffering, streaming feature extraction and fleet-aware anomaly scoring across eleven manufacturing plants.

1B+events/day
11plants
KinesisTimestreamSageMakerFlink
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Life sciences · Edge ML

Predictive maintenance at the plant, even when the cloud is absent

Signed edge models, offline inference and MES-safe alerts gave operators time to intervene before equipment failure.

38%less downtime
7 minwarning window
ONNXEdge MLAirflowGrafana
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Composite pattern · AI harness

An AI delivery harness for dozens of testable copilots

Golden datasets, prompt versioning, model routing, MCP tool contracts and trace-based release gates turn AI demos into owned software.

4 gatesrelease evidence
Any modelrouting policy
MCPLangGraphOpenTelemetryEval gates
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Composite pattern · Microsoft Azure

A zero-trust Azure landing zone for hosted AI agents

Private networking, workload identity, policy-as-code and versioned Foundry agents across a regulated subscription estate.

Per-agentEntra identity
Privateservice path
Microsoft FoundryEntraAKSDefender
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Composite pattern · Google Cloud

Real-time risk scoring with Vertex AI and explainability built in

A feature-consistent training and serving path with drift detection, fairness checks and instant model rollback.

Onlinefeature serving
Explainabledecision path
Vertex AIBigQueryDataflowFeast
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E-commerce · Platform engineering

From three-week releases to eight safe deploys a day

A paved-road developer platform combined GitOps, ephemeral environments, progressive delivery and cost-aware Kubernetes.

8×/daydeploy frequency
0Sev-1 incidents
Argo CDKarpenterBackstageDatadog
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FinTech · Developer platform

A compliant golden path for 120 product engineers

Reusable service templates, GitOps promotion and Vault-backed secrets made daily delivery auditable by default.

6wk→1drelease cycle
120engineers
BackstageGitHub ActionsArgo CDVault
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How to read these

Architecture first. Claims second.

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.

ConstraintWhat made the problem hard
Control planeHow the system is governed
Failure pathWhat happens when it breaks
OutcomeThe business metric that moved