NewNew: The enterprise guide to Agentic AI — 24 min read.

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6–10 wk
First copilot in production
+40%
Employee copilot adoption
PTU-tuned
Predictable Azure spend
Purview
Data classification enforced
Certified partner platforms
  • Microsoft Azure AI Implementation | pronix.ai
The enterprise challenge

Microsoft shops need AI that respects their identity, data and controls.

Azure AI is the fastest path for enterprises already standardized on Microsoft 365, Dynamics, Fabric and Entra — but the governance story only works if identity, retrieval and monitoring are wired correctly.

  • Grounded on your tenant
    Azure AI Search over SharePoint, Dataverse and Fabric — with Entra-based permission filtering at query time.
  • Model choice
    GPT-4o, o-series, Phi, Llama and Mistral on a single Foundry runtime, with content safety and evaluations built in.
  • Governance by default
    Purview classification, Defender for Cloud posture and Foundry evaluations — the artifacts your CISO and MRM teams expect.
Capabilities

Full-lifecycle Azure AI delivery.

01
Azure AI Foundry

Multi-model apps, agents and evaluations with content safety and prompt shields.

02
Azure OpenAI Service

GPT-4o, o-series and embeddings with regional data residency and PTU capacity planning.

03
Azure AI Search (RAG)

Hybrid vector + keyword search over SharePoint, OneDrive, Dataverse and Fabric OneLake.

04
Agent orchestration

Assistants, tools and multi-agent workflows integrated with Logic Apps and Power Platform.

05
Identity & governance

Entra ID, Purview labels and Defender for Cloud AI posture across every deployment.

06
Managed operations

Prompt regression, PTU tuning and model refresh under one SLA.

Microsoft Azure AI Implementation | pronix.ai-certified talent

Need certified Microsoft Azure AI Implementation | pronix.ai talent?

Deploy specialists who have delivered Microsoft Azure AI Implementation | pronix.ai implementations across enterprise environments — from design and build through steady-state operations.

  • Certified engineers
  • Solution architects
  • Conversation designers
  • Delivery leads
How we deliver

A six-step model, from assessment to managed operations.

Every engagement follows the same rhythm — so business, IT and delivery stay aligned from opportunity to outcome.

01
Assess

Use cases, data estate and identity model.

02
Design

Foundry, OpenAI and AI Search architecture.

03
Pilot

One grounded copilot with evaluations.

04
Implement

Agents, retrieval and Purview controls.

05
Scale

LOBs, languages, tenants, regions.

06
Operate

Managed prompts, PTU and model refresh.

Where it lands

Use cases already in production with enterprise clients.

Enterprise copilots

Employee and service copilots grounded on SharePoint, Dataverse and Fabric with Entra permission filtering.

Agentic workflows

Multi-step agents that call Logic Apps, Dynamics 365 and Power Platform with human-in-the-loop.

Regulated RAG

Advisor and knowledge assistants with Purview labels, evaluations and content safety enforced.

Migration from ChatGPT sprawl

Consolidate shadow AI onto governed Azure OpenAI with cost, safety and identity controls.

Industry patterns

Industries where this ships fastest

  • Financial Services
  • Healthcare Providers
  • Health Payers
  • Insurance
See industry solutions →
Runs on

Grounded on your data. Governed on day one.

Every platform we implement is only as good as the retrieval, connectors and controls behind it. These are the horizontal solutions we ship with every engagement.

Not sure where to start? Score your organization in 10 minutes.Take the AI Readiness Assessment →
Frequently asked

Questions buyers ask us first.

How does Azure AI compare to OpenAI direct?
Azure OpenAI runs the same models with Microsoft's enterprise agreements, regional data residency, private networking and Entra identity — the standard for regulated enterprises. Direct OpenAI wins on model recency and API velocity.
Do we need Azure AI Search for RAG?
In most enterprise deployments, yes — it provides hybrid retrieval and permission-aware filtering. We evaluate alternatives (Fabric, third-party vector DBs) where they fit better.
How do you handle PTU vs pay-as-you-go?
We size PTU capacity against your P95 concurrency, keep pay-as-you-go for burst, and monitor cost per resolved interaction to keep the unit economics honest.
Next step

Book a working session with our microsoft azure ai implementation team.

30 minutes. Your architecture, your data, your KPIs. You leave with a concrete pilot outline and a business case worth defending.