Enterprise Generative AI Platforms Built for Governed, Reusable AI

Scope can include readiness assessment, workload segmentation, reference architecture, model gateways, retrieval services, prompt and agent management, data connectors, identity, policy controls, evaluation, observability, CI/CD, operating model, documentation, training and managed support. The final scope is agreed after discovery.

An application solves a defined user or business problem. A platform provides reusable shared capabilities—such as model access, retrieval, evaluation, security, logging and deployment patterns—that can support multiple applications. Application-specific design, data, testing and ownership are still required.

A shared platform is usually justified when several teams need similar model, data, security and operational capabilities; when risk or cost must be controlled centrally; or when repeated pilot work is slowing delivery. A single approved SaaS product may be more appropriate for a narrow low-risk need.

Yes, where multi-model access is justified by workload, quality, availability, residency, commercial or risk requirements. The architecture can support approved model catalogues and routing, but each provider still requires technical, contractual, security and governance review.

Yes. Work can include source onboarding, chunking, metadata, embeddings, vector or search indexes, permission-aware retrieval, reranking, citations, freshness, evaluation and monitoring. Results depend heavily on source quality, access rules, content ownership and evaluation design.

Controls may include grounded retrieval, prompt constraints, structured outputs, evaluation datasets, adversarial tests, confidence or abstention rules, citations, human review, monitoring and fallback paths. These measures reduce risk but cannot guarantee that generative models will always be correct.

Typical controls include enterprise identity, least privilege, approved model access, private networking where appropriate, encryption, secrets management, logging, data-loss prevention, secure development, vulnerability management, administrative monitoring and incident response. Final requirements depend on risk and architecture.

The design can map data categories, purpose, lawful use, retention, model-provider processing, training-use terms, cross-border transfer, storage, deletion and regional deployment options. Legal conclusions and regulatory interpretations should be confirmed by authorised privacy and legal specialists.

Relevant references may include NIST AI RMF, ISO/IEC 42001, ISO/IEC 23894, ISO/IEC 27001, privacy frameworks, secure software-development practices, OWASP guidance for LLM applications and internal enterprise architecture or model-risk standards. Applicability varies by organisation and jurisdiction.

There is no reliable fixed duration without discovery. Timing depends on existing capability, number of environments and integrations, data readiness, stakeholder decisions, vendor procurement, security review, evaluation depth, regulatory requirements and whether the scope includes production implementation and transition.

Pricing is influenced by assessment depth, architecture complexity, number of workloads and integrations, implementation effort, controls, evaluation, environments, onsite needs, support model and knowledge transfer. Cloud, model and software consumption are usually separate from consulting fees.

Yes, subject to available model and platform capabilities. Deployment patterns are selected according to data sensitivity, latency, scale, residency, security, operational skills, vendor support and total cost. Some model services may impose constraints that must be assessed.

Managed support can include agreed monitoring, service reporting, evaluation runs, model and prompt change support, incident triage, onboarding assistance, cost reviews and improvement planning. The client retains accountable business, risk, policy and platform ownership unless contracts explicitly state otherwise.

Clients typically provide sponsors, product and platform owners, priority workloads, architecture and security standards, data-source access, policies, vendor information, environments, domain reviewers, acceptance criteria, procurement support and timely decisions. Missing inputs are documented as dependencies or limitations.

Review experience across platform architecture, AI engineering, enterprise data, evaluation, security, governance and operations. Ask for a clear delivery method, responsibility boundaries, evidence approach, technology neutrality, documentation, knowledge transfer, limitations and transparent commercial assumptions.