AI Platform Architecture for Governed, Scalable Enterprise AI

AI platform architecture is the target technical and operating design that connects AI applications and agents with models, enterprise data and knowledge, retrieval, tool integration, identity, security, evaluation, observability, deployment pipelines and accountable operations. It defines how these components should work together for the organisation’s workloads and constraints rather than treating each AI proof of concept as an isolated build.

The service can include workload and non-functional requirement discovery, current-state platform review, data and knowledge-flow analysis, model and agent access patterns, RAG and integration architecture, identity and network boundaries, security and privacy controls, evaluation and observability design, deployment and release patterns, platform decision criteria, target reference architecture, decision records and a phased implementation roadmap. Final scope is agreed during discovery.

Common triggers include multiple disconnected AI pilots, different teams buying model access independently, uncertainty about RAG or agent patterns, inconsistent security controls, rapidly rising inference or platform cost, a move from prototypes to production, multi-cloud or hybrid constraints, or a need to standardise how AI workloads are evaluated, deployed, monitored and governed.

The engagement is requirements-led. Existing and preferred technologies can be assessed, but architecture decisions should be based on workload fit, security, integration, governance, operational ownership, portability, cost visibility and implementation constraints. Vendor-specific recommendations are made only where the scope requires them.

Depending on the client estate, the assessment can consider managed AI platforms such as Microsoft Foundry, Amazon Bedrock, Google Vertex AI, Databricks AI capabilities and Snowflake Cortex AI, as well as approved model APIs, private or self-hosted model endpoints, vector and search services, API gateways, event platforms, data platforms, identity services, secrets management, observability, evaluation and CI/CD tooling. Availability and fit are validated for the relevant region and workload.

The architecture can define model access and routing, prompt and context handling, retrieval and grounding, knowledge ingestion, tool and API access, agent orchestration, conversation state, human approval points, evaluation, safety checks, tracing, logging, rate limits, fallbacks and lifecycle controls. The exact pattern depends on the business process and risk profile.

The design can address identity and least-privilege access, network boundaries, secrets, data classification, prompt and response handling, logging, retention, model and third-party risk, evaluation, human oversight, incident and rollback paths, and evidence requirements. Frameworks such as NIST AI RMF and ISO/IEC 42001 can inform governance where relevant, but the service does not by itself certify compliance or replace legal, regulatory or specialist assurance advice.

Typical deliverables can include a current-state architecture and gap assessment, workload profile matrix, target reference architecture, component and integration diagrams, model and agent access patterns, data and RAG flows, security and control architecture, evaluation and observability blueprint, architecture decision records, platform option criteria, operating responsibilities, risk and dependency register, and a phased implementation roadmap.

Yes. A focused review can test an existing reference architecture, cloud or platform proposal, systems-integrator design, RAG pattern or agent architecture against business requirements, non-functional requirements, security boundaries, data dependencies, evaluation needs, operability, cost drivers and implementation risks. The review scope and evidence required are agreed before work starts.

Implementation is not automatically included in an architecture engagement. DataConsultant can separately scope implementation support, architecture assurance, platform configuration, integration, data engineering, RAG enablement, governance setup, testing, observability, knowledge transfer or managed operations. Responsibilities and acceptance criteria should be agreed before implementation begins.

A reliable duration is confirmed after scoping. Timing depends on the number and complexity of workloads, existing platforms and cloud environments, stakeholder availability, security and regulatory review, data and integration dependencies, the depth of platform comparison required, evidence quality and whether detailed implementation planning is included.

DataConsultant does not publish a fixed fee for this AI Platform Architecture service. Pricing is scope-led and confirmed through a Request a Quote process after the workloads, environments, stakeholder groups, architecture depth, platform options, security and governance requirements, workshops, deliverables, review cycles and implementation-assurance needs are understood.

Useful inputs include priority AI use cases, current proofs of concept, cloud and platform inventories, architecture diagrams, data and knowledge sources, API and integration maps, identity and network patterns, security and privacy policies, risk or audit findings, model-provider arrangements, expected volumes and latency needs, cost information, deployment processes and access to accountable business and technology stakeholders.