Capability

Production AI Architecture at Scale

Reference architecture and operating model for organizations moving from isolated AI initiatives to governed, reusable platform foundations.

Shared platform architecture with explicit plane boundaries and governed entry points
Cost attribution, observability, and onboarding standards across business units
Governance and compliance evidence embedded into delivery gates — not bolted on after

Discuss your architecture

When to bring this in

This is typically needed when:

Multiple teams are building platform patterns independently — gateways, identity, logging, cost tracking — duplicating effort and fragmenting standards.

Model consumption is not visible centrally, and cost attribution is weak or absent for finance and platform teams.

Security baselines and governance are difficult to enforce because there are no consistent delivery standards across teams.

Shadow AI risk is increasing as teams access models through unmanaged paths with no shared telemetry.

The organization needs to move from isolated pilots to platform-level adoption without centralizing everything.

What the engagement covers

Scope

A principal-led engagement that defines the shared architecture, ownership model, and adoption path for production AI — so teams build on common foundations instead of reinventing platform components.

Reference architecture with explicit plane boundaries separating platform concerns from application delivery

Standard entry points for model and tool interactions with shared routing, telemetry, and cost attribution

Knowledge layer and retrieval architecture: corpus strategy, retrieval patterns, embedding and indexing choices, and grounding expectations

Onboarding standards and ownership model so new teams adopt the platform through a repeatable path

FinOps and SLO-driven guardrails for routing, caching, latency, throughput, and scaling decisions

Governance and compliance evidence embedded into release gate design — not a separate review layer

Trade-off analysis across providers, platforms, and architectural patterns grounded in operating constraints

Progressive adoption model: teams onboard at their own pace, with defaults and exception paths rather than mandates

Typical outputs

What the engagement produces

What changes afterwards

After this engagement

New teams onboard through a standard path instead of building bespoke platform components from scratch.

Model usage and cost attribution become visible across business units, enabling informed capacity and investment decisions.

Governed entry points become the default path for new GenAI and agentic workloads — reducing shadow AI without blocking delivery.

Governance is embedded into shared delivery paths, not managed as a separate review layer.

Federated teams move faster with less duplication because platform concerns are solved once.

What this is not

✕A hands-on engineering delivery team

✕A single-vendor platform recommendation

✕A generic AI strategy workshop

✕A PoC or prototype build

✕A compliance exercise detached from delivery reality

Common questions

Production AI Architecture at Scale

This is typically needed when:

Scope

What the engagement produces

Reference Architecture BlueprintProduction-grade reference architecture covering data, ML, and GenAI integration — structured so teams and vendors can implement consistently.

Knowledge Layer and Retrieval ArchitectureHow structured and unstructured enterprise knowledge becomes queryable, grounded, and operationally reliable.

Platform Map and Ownership ModelIdentity, routing, telemetry, cost attribution, and the boundary between platform and application team responsibilities.

Standards and Reusable Delivery PathsReusable patterns that standardize how enterprise AI systems are built, integrated, evaluated, and operated.

Operating Model and Adoption RoadmapThe governance mechanics and sequenced path that make the architecture implementable in practice.

After this engagement

What this is not

FAQs

Is this for cloud or on-premise environments?

Do you build the systems?

When should we engage for architecture?

Do you cover RAG and enterprise knowledge architectures?

How does this relate to security architecture?