AI & GenAI Architecture
A clear blueprint for enterprise AI that holds up under real constraints: security, data boundaries, cost, reliability, and governance.
- Vendor-neutral reference architectures across data, ML, and GenAI
- Decision-ready roadmaps for regulated, high-stakes environments
- Aligns with ISO/IEC 42001 and EU AI Act readiness
The Challenge
Enterprises rarely struggle with "getting a demo to work." They struggle with scaling beyond pilots: fragmented platforms, unclear ownership, and rising cloud spend.
Inconsistent security posture and governance friction blocks production. GenAI adds new failure modes (provider routing, prompt injection, data leakage, tool access) that are easy to underestimate.
Our Approach
We design an architecture you can actually run: a coherent target state across data, ML, and GenAI; and a control-plane view for complete visibility.
It includes identity, policy, observability, evaluation, and FinOps; plus an operating model (RACI, forums, and decision records) that lets teams and vendors execute with confidence.
Key Outcomes
Core Deliverables
Architecture Blueprint
A comprehensive reference architecture covering data, ML, and GenAI layers—structured as context, container, and component views that your teams and vendors can implement.
- Key interfaces and platform contracts (how teams integrate, deploy, and operate)
- Non-functional requirements: SLOs, availability, latency, resiliency assumptions
Global Energy Major
The Context
Consolidated fragmented ML initiatives into a single secure platform governed by clear architecture.
The Outcome
Result: 100+ use cases supported, standardized integration patterns, and reduced shadow IT.