Production AI Architecture at Scale
A production-ready reference architecture for AI that holds up under real constraints: security, data boundaries, cost, reliability, and governance.
- Vendor-neutral reference architectures across data, ML, and GenAI integration
- Designed for regulated, high-stakes environments with clear decision rights
- Aligned with EU AI Act and ISO/IEC 42001 evidence expectations
The Challenge
Enterprises rarely struggle with getting a demo to work. They struggle with scaling beyond pilots: fragmented platforms, unclear ownership, and rising spend.
GenAI adds new failure modes (prompt injection, data leakage, provider routing, tool access) that are easy to underestimate - especially when delivery spans multiple teams and vendors.
Our Approach
We design an architecture you can actually run: coherent target state, standard entry points, operating controls, and a governance/assurance layer for visibility and consistency.
It includes identity, policy, observability, evaluation, and FinOps - plus an operating model (RACI, forums, decision records) that lets teams and vendors execute without ambiguity.
Key Outcomes
Core Deliverables
Reference Architecture Blueprint
A production-grade reference architecture covering data, ML, and GenAI integration - structured so teams and vendors can implement consistently.
- Key interfaces and platform contracts (integration, deployment, operation)
- Non-functional requirements: SLOs, resiliency assumptions, and constraints
Global Energy Major
The Context
Consolidated fragmented ML initiatives into a single secure platform governed by clear architecture and standard integration patterns.
The Outcome
Result: 100+ use cases supported, reduced shadow IT, and consistent delivery across teams.