Enterprise AI
From AI to SI on secure foundations.
Move AI workloads from prototype into governed production by connecting model access, enterprise data and application controls. Design for identity, network isolation, evaluation, operational visibility and accountable consumption from the start.
Problems to solve
- A successful demo has no repeatable deployment, evaluation gate or owner for production incidents.
- RAG responses can cross user or tenant boundaries when document permissions are not enforced during retrieval.
- Model endpoints, indexes and tools expose data paths or credentials that are difficult to constrain and audit.
- Quality regressions, token usage and model changes are not visible enough for operational or financial decisions.
Capabilities
- Azure AI Foundry and Azure OpenAI deployments with environment and model lifecycle controls.
- Retrieval-Augmented Generation (RAG), search/index integration and data access filtering.
- AI agents and MCP tool integrations where bounded tool use fits the workflow.
- AI Landing Zones, private networking, managed identity and secret management.
- Security controls, evaluation, observability, production deployment and AI FinOps.
Architecture concerns
- Choose model endpoints and deployment patterns; isolate environments and route access through approved networks.
- Connect model, search and data services with private endpoints, DNS, managed identities and Key Vault references.
- Apply document permissions and tenant filters at retrieval time; define ingestion, deletion and index refresh paths.
- Constrain agent and MCP tools with explicit allowlists, least-privilege authorization, input validation and audit logs.
- Version prompts and datasets; evaluate groundedness, relevance, safety and task behavior before promotion.
- Capture traces, model and token usage, failures and cost allocation while protecting sensitive prompt data.
Typical deliverables
- AI workload reference architecture, data-flow diagram, threat considerations and architecture decisions.
- Infrastructure-as-Code for network, identity, logging and AI service configuration.
- Deployable RAG or agent reference implementation with a repeatable build and release workflow.
- Evaluation datasets and gates, operational dashboards, incident guidance and usage/cost controls.
Related engagements: Secure AI Landing Zone and AI Production Accelerator.