Enterprise AI consulting for teams that can't afford to get the architecture wrong

We design end-to-end AI platform architectures that account for the constraints most teams discover too late: data classification boundaries, model governance requirements, multi-tenant isolation, and operational observability. Every architecture delivers a deployment-ready RunSpec with infrastructure-as-code, security controls, and compliance mapping baked in.

Key Capabilities

• Platform topology and component selection
• Model evaluation and orchestration design
• Data pipeline architecture for ML/AI workloads
• Scaling strategy and cost optimization
• Migration planning from POC to production

RAG in regulated environments demands more than a vector database and an LLM API key. We architect document ingestion pipelines with classification-aware chunking, role-based retrieval with access control inheritance, and audit-ready evidence chains that satisfy compliance reviewers.

Key Capabilities

• Document ingestion with metadata-aware chunking
• Role-based access control at the retrieval layer
• Citation tracking and source attribution
• Hybrid search (vector + keyword) optimization
• Hallucination guardrails and response validation

AI governance isn't a checkbox — it's an operating model. We build governance frameworks that cover model risk assessment, bias monitoring, explainability requirements, data lineage tracking, and regulatory alignment across your AI portfolio.

Key Capabilities

• Model risk assessment and classification
• Bias detection and monitoring pipelines
• Explainability and interpretability tooling
• AI ethics policy and review board design
• Regulatory mapping (EU AI Act, NIST AI RMF)

Manual compliance is a scaling bottleneck. We build automated evidence collection systems, continuous control monitoring, and real-time compliance dashboards that reduce audit prep from weeks to hours while maintaining audit-ready documentation.

Key Capabilities

• Automated evidence collection and artifact generation
• NIST 800-53 control family mapping
• OSCAL-native SSP generation
• Continuous monitoring and drift detection
• POA&M tracking and remediation workflows

Agent-based AI systems require architecture patterns that most teams haven't built before: reliable task decomposition, human-in-the-loop approval gates, tool-use sandboxing, and conversation state management at scale. We design agent platforms that are observable, auditable, and recoverable.

Key Capabilities

• Multi-agent orchestration and routing
• Human-in-the-loop approval workflows
• Tool-use sandboxing and permission models
• Conversation state and memory management
• Observability and cost attribution

AI platforms need infrastructure that was designed for the workload: GPU scheduling, model serving, feature stores, and experiment tracking — all wrapped in IaC with proper RBAC and network segmentation. We build developer platforms that make the secure path the easy path.

Key Capabilities

• Terraform/Pulumi IaC for AI infrastructure
• GCP, Azure, and AWS optimization
• CI/CD pipelines for ML model deployment
• Network segmentation and Zero Trust architecture
• Cost management and FinOps for AI workloads