Enterprise AI architecture: one reference every AI team builds on
Enterprise AI architecture is the set of shared layers every AI workload uses: model access, retrieval, orchestration, data, identity, observability and governance. We design yours as a vendor-neutral reference architecture and build the first workloads on it.
Platform architecture
Why pilots multiply but the platform never appears
Without a shared enterprise AI architecture, every pilot makes its own choices. One team uses a managed assistant, another calls a model API directly, a third stands up its own vector database. Each works well enough as a demo. None shares identity, logging, evaluation or cost controls, and the security review for each starts from zero.
The cost shows up later. When leadership asks which AI systems touch customer data, nobody can answer quickly. When a model is deprecated or a provider changes pricing, several teams rewrite code at once. When an auditor asks how outputs are monitored, the answer differs by team. Reuse, the main economic argument for a platform, never happens.
A reference architecture fixes this only if teams actually use it. That means it has to be concrete enough to build from, with named components, interfaces and approved products, and flexible enough to absorb new models and patterns without a redesign every quarter. Most reference diagrams fail one of those two tests.
What you get
Layered reference architecture
Logical and physical views of your enterprise AI architecture, covering experience, orchestration, model access, retrieval, data, identity, observability and governance layers, with the interfaces between them and the approved products for each.
Pattern library
Documented, reusable patterns for the workloads you actually run, such as document question answering, summarization pipelines, classification, copilots inside existing applications and tool-using agents, each with a reference diagram and control requirements.
Architecture decision records
Recorded decisions on hosting, model providers, vector storage, orchestration frameworks and integration standards, with options, trade-offs and triggers for revisiting, so the architecture can evolve without relitigating settled questions.
Non-functional requirements and controls
Target latency, availability, data residency, retention, logging and security requirements by risk tier, mapped to the controls in each architecture layer so teams know what they must meet before production.
Reference implementation
A working, deployable implementation of the core layers in your cloud, built with your engineers, that the first production workload runs on and later teams can clone rather than start from an empty repository.
The layers of an enterprise AI architecture
Every enterprise AI architecture we design is organized into layers with clear interfaces, so components can be swapped without rebuilding the workloads that depend on them.
- Experience and integration layer
- Where users and systems meet AI: chat interfaces, copilots embedded in existing applications, APIs and event-driven batch jobs, all authenticated through your enterprise identity provider.
- Orchestration and agent layer
- Workflow and agent frameworks such as LangGraph, prompt and tool management, and standard interfaces like the Model Context Protocol for connecting models to enterprise tools under policy.
- Model access gateway
- A single control point for Azure OpenAI, Amazon Bedrock, Google Vertex AI, Anthropic, OpenAI and self-hosted models, handling routing, quotas, logging, redaction and cost attribution.
- Retrieval and knowledge layer
- Ingestion, chunking, embedding and hybrid search over approved content, using stores such as pgvector or Pinecone, with document-level permissions enforced at query time.
- Data foundation
- Governed data products on platforms such as Databricks or Snowflake, with lineage, quality checks and sensitivity labels, feeding both analytics and AI workloads from one source.
- Observability and evaluation
- Tracing of every request, offline and online evaluation, quality and safety monitoring, and cost dashboards, so each workload can prove it works and keep proving it.
- Security and governance controls
- Identity and authorization, secrets management, content filtering, prompt injection defenses, AI system inventory and policy checks mapped to NIST AI RMF and ISO/IEC 42001.
How we deliver it
Assess
We inventory existing AI pilots, platforms, data stores and cloud standards, review your current enterprise architecture principles, and identify which components can be reused and where duplication or risk is already building up.
Blueprint
We design the target enterprise AI architecture layer by layer, choose products through documented decision records, and define the patterns and non-functional requirements that teams will build against, validated against your top use cases.
Build
Our architects work inside your engineering teams to deliver a reference implementation and the first production workload on it, using infrastructure as code and your existing pipelines so the result is fully yours.
Govern
We connect the architecture to your review board with conformance checks by pattern, a process for adding new components, and a regular refresh of decision records as models and products change.
What changes
- New AI workloads start from approved patterns and a working implementation instead of a blank page.
- Security and risk reviews get faster because controls are built into shared layers, not reinvented per project.
- You can change model providers or add new ones behind the gateway without rewriting applications.
- Leadership can see every AI workload, what data it touches and what it costs, from one platform.
Related services
- Generative AI Platform ArchitectureLLM gateway, model routing, evaluations and cost controls that let teams use multiple models safely, observably and within budget.
- RAG and Knowledge ArchitectureRetrieval-augmented generation for enterprise content: access control, citations, freshness and answer quality you can measure.
- Data Architecture for AIData foundations for AI workloads: unstructured data pipelines, semantic layers, lineage, quality and access control for models and agents.
Enterprise AI Reference Architecture: common questions
What is an enterprise AI architecture?
An enterprise AI architecture is the shared set of layers, standards and services that every AI workload in an organization builds on: user experience and integration, orchestration, model access, retrieval, data, observability, and security and governance controls. It lets teams deliver new AI use cases quickly while keeping identity, logging, cost and risk controls consistent across all of them.
What is an AI reference architecture?
An AI reference architecture is a documented template that shows the components, interfaces and approved products an organization uses to build AI systems, along with reusable patterns for common workloads. A good one is specific enough that an engineering team can build from it directly and includes a working reference implementation, not only a diagram.
How long does it take to design an enterprise AI architecture?
Designing an enterprise AI architecture typically takes 6-10 weeks for the blueprint: current-state review, layered target design, decision records and pattern library. Delivering a reference implementation and the first production workload on it usually takes a further two to four months, depending on the complexity of your data, identity and security requirements.
Should our enterprise AI architecture be multi-cloud?
Not necessarily. Most organizations should anchor the architecture in their primary cloud and keep model access multi-provider through a gateway, which gives flexibility where it matters without doubling infrastructure. Full multi-cloud AI platforms make sense when regulation, customer contracts or existing estates require them, and they add real operating cost.
How does an enterprise AI architecture relate to our existing enterprise architecture?
An enterprise AI architecture should extend your existing enterprise architecture, not replace it. It reuses your identity provider, cloud landing zones, data platform, integration standards and security tooling, and adds AI-specific layers such as a model gateway, retrieval services and evaluation. Your current architecture principles and review processes should govern it.
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