Azure AI Consulting Services

Enterprise AI Built on Azure: Azure AI Consulting Services

EPC Group develops enterprise AI applications on Microsoft Azure. We utilize Azure OpenAI Service, Azure AI Foundry, Azure Machine Learning, and Azure AI Services.

Our services include:

• Designing RAG architectures
• Fine-tuning models
• Implementing AI governance
• Deploying HIPAA-compliant and FedRAMP-aligned AI solutions

We serve healthcare, financial services, and government organizations.

Complete Azure AI ecosystem

Azure OpenAI Service

The Azure OpenAI Service provides businesses with access to OpenAI's models. This includes GPT-4o, GPT-4.1, o3, DALL-E 3, and Whisper.

These services function within Azure's security and compliance framework.

Key features include:

• Data at rest stays in the Azure geography of your resource; prompts and completions are processed in the region, data zone or global pool of the deployment type you choose.
• Microsoft signs a BAA for HIPAA-eligible deployments.

• Private deployment — your prompts and completions do not train shared OpenAI models.
• Provisioned throughput units (PTUs) for guaranteed capacity in production workloads.
• Azure Private Endpoints — data does not traverse the public internet.
• Content filtering and Azure AI Content Safety for output moderation.

Azure AI Foundry

Azure AI Foundry (formerly Azure AI Studio) is the enterprise platform for building, evaluating, and deploying AI applications at scale.

• Model catalog with 1,600+ models — OpenAI, Meta Llama, Microsoft Phi-4, Mistral, Cohere.
• Prompt Flow for visual AI application orchestration and testing.
• Built-in RAG pipelines with Azure AI Search integration.
• Responsible AI dashboards for fairness, interpretability, and error analysis.
• MLOps deployment with A/B testing, traffic splitting, and rollback.

Azure Machine Learning

Azure Machine Learning is the platform for training custom models and managing ML pipelines at enterprise scale.

• Managed compute clusters for distributed model training.
• Automated ML (AutoML) for no-code model training on tabular and time-series data.
• MLflow integration for experiment tracking and model registry.
• Batch inference pipelines for large-scale scoring workloads.

Azure AI Services

Azure AI Vision

• Image analysis — Object detection, scene description, and optical character recognition (OCR).
• Custom Vision — Train image classifiers and object detectors on your own labeled images.
• Medical imaging — Radiology image analysis and pathology slide processing for healthcare AI.

Azure AI Language

• Sentiment analysis — Customer feedback scoring and survey response classification.
• Entity recognition — Extract people, organizations, dates, and PHI from unstructured text.
• Custom Named Entity Recognition — Train entity extractors on domain-specific terminology.
• Summarization — Generate abstractive and extractive summaries of documents and conversations.

Azure AI Speech

• Speech-to-text — Real-time transcription for contact centers, clinical documentation, and meeting intelligence.
• Text-to-speech — Natural-sounding voice synthesis for IVR systems and accessibility tools.
• Speaker identification — Distinguish speakers in multi-participant conversations.

Azure AI Bot Service

• Build enterprise chatbots and virtual agents on Azure OpenAI and AI Language.
• Native integration with Microsoft Teams, Outlook, and SharePoint as bot channels.
• Copilot Studio, formerly Power Virtual Agents (Microsoft Learn, Microsoft Copilot Studio Agent inventory, accessed 2026-10-21), for low-code agent creation on the same platform.

Enterprise Azure AI use cases by industry

Healthcare

• Clinical documentation automation — AI-generated SOAP notes from physician-patient conversations.
• Medical image analysis — radiology and pathology AI using Azure AI Vision.
• Drug discovery acceleration — molecule property prediction with Azure Machine Learning.
• Patient risk prediction — readmission and deterioration models on EHR data.
• Virtual health assistants — patient-facing chatbots for symptom triage and appointment scheduling.

Financial services

• Fraud detection and prevention — real-time transaction scoring with Azure ML.
• Contract intelligence — clause extraction and risk flagging from loan and vendor agreements.
• Customer service automation — AI chatbots for account inquiries and complaint resolution.
• Regulatory document processing — automated extraction from regulatory filings and disclosures.

Government

• Document intelligence — automated processing of grant applications, permits, and forms.
• Public-facing virtual agents — citizen self-service chatbots for agency inquiries.
• AI-powered search — RAG systems on government knowledge bases for employee Q&A.
• FedRAMP-aligned deployments — all services in FedRAMP Moderate or High authorized Azure regions.

EPC Group Azure AI implementation approach

1. AI readiness assessment — Evaluate your data estate, compliance requirements, and use case priorities.
2. Architecture design — Design the Azure AI architecture with governance, security, and compliance controls built in.
3. Proof of concept — Build and validate a working prototype against real data in 2–4 weeks.
4. Production deployment — Deploy to production with monitoring, logging, and incident response in place.
5. Governance activation — Implement NIST AI RMF controls, Purview audit logging, and model risk monitoring.

Frequently asked questions

What Azure AI services does EPC Group implement?

EPC Group implements a range of Azure AI services. These include:

• Azure OpenAI Service
• Azure AI Foundry
• Azure Machine Learning
• Azure AI Search
• Azure AI Document Intelligence
• Azure AI Vision
• Azure AI Language
• Azure AI Speech
• Azure AI Content Safety
• Copilot Studio

We design complete enterprise AI architectures, not just individual service deployments.

Is Azure AI HIPAA compliant?

Yes, Azure OpenAI Service, Azure Machine Learning, and Azure AI Search are HIPAA-eligible services. This is true when they are deployed with a Microsoft Business Associate Agreement.

To ensure compliance, remember the following:

• PHI must remain in HIPAA-eligible Azure regions.
• Private Endpoints keep PHI off the public internet.
• Audit logging via Azure Monitor and Purview is required.

What is a RAG architecture?

RAG (Retrieval-Augmented Generation) is an AI architecture that grounds LLM responses in your enterprise documents. Azure AI Search retrieves the most relevant document chunks for each query.

Azure OpenAI GPT-4.1 generates responses based on retrieved content — with source citations. This prevents hallucination and keeps AI responses accurate to your data.

How long does an Azure AI implementation take?

A focused proof of concept, which includes one use case on defined data sources, takes 2–4 weeks.

A production Azure AI deployment, featuring governance, security hardening, and monitoring, requires 8–16 weeks.

Enterprise-wide AI programs that involve multiple use cases typically run for 6–12 months.

What does Azure AI consulting cost with EPC Group?

Azure AI readiness assessments are priced after a scoping call. Proof-of-concept builds are scoped after a discovery call.

Full production deployments, which include governance and compliance controls, are scoped as a fixed fee quoted after a scoping call. The fee depends on:

• Scope
• Use case complexity
• Regulatory requirements

Build your enterprise Azure AI solution

Talk to a senior Azure AI architect about your use case and compliance requirements. Call (888) 381-9725 or request a 30-minute discovery call.