AI implementation.
Production-grade enterprise AI on Microsoft Azure. Architected, deployed, integrated with your identity and data, and handed over operable. The work that turns a vendor demo into a system your business actually runs on.
How do you deploy Azure OpenAI Service and enterprise AI to production?
Azure OpenAI Service deployment.
An eight-week structured program that takes an organization from "we want to use AOAI but the architecture is unclear" to "we have a production Azure OpenAI deployment with identity, data, and cost guardrails in place." Delivered as Terraform in your repository, not a console-deployed sandbox someone will struggle to extend.
Discovery and use case shaping.
Inventory the AI use cases your business actually wants, classify the data they touch, and decide which workloads belong in Azure OpenAI versus ChatGPT Enterprise, Claude Enterprise, or Microsoft 365 Copilot. The output is a one-page deployment plan that drives every subsequent decision.
Architecture and provisioning.
Azure OpenAI resource provisioning with private endpoint and VNet integration, region selection driven by data residency requirements, model selection mapped to actual use cases, content filtering configured, and quota planning done before you hit a token wall in week six.
Identity, access, and data integration.
Identity-bound access via Entra ID with role-based permissions on the AOAI resource, integration with Azure AI Search or your existing data sources for retrieval-augmented generation, and a sanctioned path for builders so internal teams stop routing around the platform.
Monitoring, cost, and operations.
Token-level cost dashboards in Azure Monitor, per-team usage breakdowns, budget alerts wired before they bite, and operations playbooks for model upgrades, regional failover, and quota expansion. The platform becomes legible to finance, not just engineering.
Adoption enablement.
Builder training on patterns that work in production (prompt structuring, embedding strategy, evaluation), business-user training on what the platform actually does and where its limits are, and handover documentation. The platform continues running after we leave because the people running it know how.
- Azure OpenAI deployment as Terraform in your repository
- Private networking, identity-bound access, content filtering
- Cost and usage dashboards with per-team breakdowns
- Retrieval-augmented generation pattern with Azure AI Search
- Operations runbooks for upgrades, failover, and quota
- Training materials for builders and business users
ChatGPT Enterprise and Claude Enterprise integration.
For organizations that have chosen a vendor enterprise AI platform and need it integrated correctly with Entra single sign-on, the data sources users actually need, and the controls procurement and legal asked for. Less common than AOAI work, more common than people expect.
- SCIM provisioning and Entra single sign-on configuration
- Allowed data connector inventory and DLP policy alignment
- Workspace, project, and seat governance model that scales
- Cross-platform routing: which workloads belong on AOAI versus ChatGPT Enterprise versus Claude Enterprise versus M365 Copilot
- Vendor management playbook for ongoing model updates
Retrieval-augmented generation on Azure.
A focused engagement to build a production RAG pipeline on Azure: documents in, grounded answers with citations out. Useful when an internal knowledge base, contract corpus, or policy library needs to be queryable by employees without the typical hallucination tax.
- Document ingestion and chunking pipeline (Azure AI Search, Document Intelligence)
- Embedding model selection and re-indexing cadence
- Grounded prompt patterns with inline citations
- Evaluation harness measuring answer accuracy on a representative test set
- Cost model and quota planning for steady-state operation
AI platform operations.
Run the AI platform on retainer once it is stood up. Useful for organizations that have a production AOAI or enterprise AI deployment and need ongoing operation, model migration, and use case onboarding without a dedicated full-time hire.
- New use case intake and onboarding through the sanctioned path
- Quarterly model and feature review (vendor releases, deprecations, migrations)
- Monthly cost and usage reports with optimization recommendations
- Vendor coordination with Microsoft, OpenAI, Anthropic, and downstream platforms
- Quarterly architecture review as the workload grows
Standing up AI on Azure and want it production-ready?
Reach out for a scoping conversation about the Azure OpenAI deployment program or a more targeted engagement.
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