Tepia integrates Azure OpenAI into existing business applications for companies that run on Microsoft, deploying models inside your own Azure subscription with private endpoints, Entra ID managed identities and region selection your security team controls. Typical builds ground answers in SharePoint, Dynamics 365 and SQL data through Azure AI Search.
For Microsoft centered companies, Azure OpenAI is less a model choice than a governance choice: the same models, inside the controls IT already trusts.
Tepia places the Azure OpenAI resource behind a private endpoint in your virtual network, so model traffic never crosses the public internet.
Read: OpenAI API Integration Services
Tepia deploys models in the Azure region your data policies require and documents the deployment type and quota for each workload.
Read: Microsoft Copilot Studio vs a Custom Chatbot
Apps call the model with Entra ID managed identities and role based access, removing API keys that can leak from config files or repos.
Read: Microsoft 365 Copilot Extensions and Integrations
Tepia connects SharePoint, Dynamics 365 and Azure SQL through Azure AI Search with security trimming, so answers respect existing access.
Industry figures Tepia plans around when scoping Azure OpenAI work.
McKinsey's 2025 State of AI survey found 78 percent of organizations use AI in at least one business function.
Microsoft's 2024 Work Trend Index reported that 75 percent of knowledge workers already use generative AI at work.
Tepia clients report about 90 percent customer retention, which depends on AI features that pass IT review the first time.
If your company signs in with Microsoft Entra ID, stores files in SharePoint, runs customer operations in Dynamics 365 and hosts applications in Azure, your security team has already approved a set of controls. Azure OpenAI lets an AI feature live inside those controls. The models are deployed as resources in your own subscription, governed under your existing Microsoft agreement, monitored with Azure Monitor and reviewed with the same tools as every other workload.
Tepia sees this pattern most in regional health systems, manufacturers, insurance carriers, law firms and public sector contractors. A claims team wants a summary of each new file. A plant wants maintenance staff to search equipment manuals stored in SharePoint. A service organization on Dynamics 365 wants agents to see a suggested reply grounded in the customer’s case history.
In each case the model is not the hard part. The work is wiring it into identity, networking, data access and logging so the feature passes architecture review and stays compliant after launch. That is what Tepia scopes first.
Tepia brings this matrix to the first meeting with your IT and security leads, then fills it in with your actual policies:
| Requirement from IT | Azure control | How Tepia implements it |
|---|---|---|
| No model traffic over the public internet | Private endpoints and virtual network integration | Public network access disabled on the resource, app services connected through your VNet |
| No long lived API keys | Entra ID with managed identities and Azure RBAC | Each app gets its own identity with only the roles it needs |
| Data stays in an approved geography | Region and deployment type selection | Deployments placed in approved regions and recorded in the architecture document |
| Harmful content is blocked | Configurable content filters | Filter levels set per deployment and tested with your edge cases |
| Every call is traceable | Diagnostic settings to Log Analytics | Request logs, latency and errors in dashboards your operations team already uses |
| Predictable capacity at peak | Quota per region, standard and provisioned throughput deployments | Load estimate per feature and a deployment plan sized to it |
| Secrets are managed centrally | Azure Key Vault | Any remaining connection secrets stored and rotated in Key Vault |
Infrastructure is defined in Bicep or Terraform so the same setup can be reviewed, reproduced in a test subscription and promoted to production. Our cloud architecture team owns that layer.
A model that only knows public information is of limited use to an enterprise team. Tepia grounds Azure OpenAI responses in your own data, using the services already in your tenant.
A service agent asking whether a customer’s compressor is under warranty gets an answer built from the Dynamics asset record and the warranty terms in SharePoint, with both sources cited. For integration detail see Microsoft Dynamics 365 integration.
Tepia delivers Azure OpenAI features inside the tools your teams open every day, rather than adding another portal to remember.
Because the backend is shared, Tepia builds the AI logic once and exposes it in each surface, so prompts, logging and permissions stay consistent wherever a user asks the question.
Tepia runs its six phase process with your IT team involved from the start and a US based project manager coordinating both sides.
Typically Discovery takes 1 to 3 months, often driven by security review, and development and QA 2 to 6 months. See our process.
Microsoft offers several ways to put AI in front of staff, and Tepia helps you pick the one that fits rather than defaulting to a build.
Many clients combine them, for example extending Copilot with a plugin that calls the same backend Tepia built for their app. Compare options in Copilot Studio vs a custom chatbot and Microsoft 365 Copilot extensions, then talk to Tepia about your tenant.
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