Architecture patterns proven in production

Challenge

A government contractor needed to achieve FedRAMP authorization for their SaaS platform, facing a 6-month audit timeline with over 300 NIST 800-53 controls requiring evidence documentation.

Approach

Designed and built an automated evidence collection pipeline that maps cloud infrastructure state to NIST control families, generates OSCAL-native SSP documentation, and provides continuous monitoring with drift detection alerts.

Outcomes

• Reduced audit preparation time by 70%
• Automated evidence collection for 280+ controls
• Continuous monitoring dashboard with real-time compliance posture
• OSCAL-native SSP generation in under 4 hours

Challenge

A healthcare payer needed to deploy a RAG system over clinical policy documents with strict HIPAA requirements, role-based access control, and audit trails for every retrieval and generation event.

Approach

Architected a multi-tier RAG system with classification-aware document chunking, role-based retrieval filters enforced at the vector database layer, citation tracking for every generated response, and comprehensive audit logging.

Outcomes

• HIPAA-compliant RAG deployment in production
• Document-level access control with inheritance
• Full citation chain for every AI-generated response
• Sub-2-second retrieval latency at scale

Challenge

A state agency needed to deploy AI agents that could assist with case management workflows while maintaining strict approval gates, audit trails, and the ability to explain every action taken.

Approach

Built a multi-agent orchestration platform with task decomposition, tool-use permission boundaries, human-in-the-loop approval workflows for sensitive actions, and comprehensive observability including cost attribution per agent interaction.

Outcomes

• 70% reduction in routine case processing time
• 100% audit coverage for all agent actions
• Human approval gates for all consequential decisions
• Full explainability for agent reasoning chains

Challenge

A financial services firm deploying multiple AI models needed a governance framework that satisfied regulatory requirements for model risk management, bias monitoring, and explainability — while not slowing down their ML team.

Approach

Designed a governance operating model with automated model risk assessment, bias detection pipelines integrated into CI/CD, model card generation, and a lightweight review board process that aligned with existing change management workflows.

Outcomes

• Governance framework operational in 8 weeks
• Automated bias monitoring for all production models
• Model card generation integrated into deployment pipeline
• Regulatory-ready documentation for SOC 2 and internal audit