Azure AI Foundry Enablement & Solutions

Stand up Azure AI Foundry capabilities and deliver production-ready AI solutions - platform setup, security, model selection, evaluation, and operational handover.

Azure AI Foundry is Microsoft’s platform experience for building and operating AI solutions, including working with models, building AI apps and agents, and applying safety and responsible AI concepts. Many organisations want to move from experimentation to a repeatable delivery model - with standardised environments, access controls, evaluation, and guardrails.

LW IT Solutions enables Azure AI Foundry as a delivery platform and then uses it to build solutions. We implement a practical foundation (environment design, access controls, networking approach as required, and governance), then deliver pilots and production workloads such as RAG-based chat with data, automation/agent workflows, and integration patterns. You receive a working capability, a documented operating model, and a backlog for continuous improvement - so the platform remains reliable and scalable after go-live.

Talk through your requirements and leave with a clear next-step plan.

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Service Overview

Highlights

Azure AI Foundry setup designed for real delivery, not demos
Governed access and environment structure aligned to your organisation
Built-in evaluation and testing patterns for AI changes
Support for RAG, copilots, agents, and automation scenarios
Clear operational handover for ongoing ownership and support

Business Benefits

Faster move from experimentation to production using a standardised AI delivery platform
Reduced risk through controlled access, governance, and evaluation built into the platform
Clear operating model for building, releasing, and supporting AI solutions
Improved reliability and trust through defined testing and monitoring of AI behaviour
Reusable foundation that supports multiple AI use cases without rework

Typical use cases

Moving from ad-hoc AI experiments to a managed delivery platform
Building RAG-based chat or assistant solutions using internal data
Developing AI agents or automation workflows with approval controls
Need for governance and evaluation before scaling AI usage
Multiple teams requiring a shared AI foundation and operating model

Objectives & deliverables

What Success Looks Like

Establish Azure AI Foundry as a controlled platform for AI delivery
Enable teams to build and operate AI solutions with clear guardrails
Reduce risk through evaluation, approval, and monitoring processes
Deliver early value through pilots that can scale into production
Create a repeatable model for future AI solutions and use cases

What You Get

Configured Foundry foundation aligned to your scope and operating model
Delivery artefacts: architecture overview, configuration decisions, and governance documentation
A working pilot or MVP solution (if included) with acceptance criteria and measurable outcomes
Evaluation and test patterns: what is tested, how changes are approved, and how drift is monitored
Handover pack: runbooks, ownership model, and a backlog for improvements and scale-out

How It Works

Discovery - confirm outcomes, use cases, constraints (data, security, compliance), and target operating model.
Foundation enablement - implement Foundry environment, access controls, governance, and safety/evaluation patterns.
Pilot build - deliver a scoped solution with measurable success criteria and stakeholder review.
Hardening - improve reliability, governance, monitoring, and change control for production readiness.
Handover - document runbooks, train owners, and establish cadence for continuous improvement.

Engagement Options

Foundation Enablement - Stand up Azure AI Foundry with governance and operating model
Pilot Delivery - Build a scoped AI solution on top of an existing or new Foundry foundation
Platform and Solutions - Foundation enablement plus delivery of one or more AI workloads
Advisory Support - Design review and guidance for teams building on Azure AI Foundry

Common Bundles

Customers who use this service often bundle with these services

AI Safety, Governance & Risk
Implement practical AI safety and governance with policies, approvals, logging, data boundaries, and controls that reduce operational and compliance risk.

Prompt Evaluation & Testing
Prompt evaluation and testing service defining acceptance criteria, golden datasets, regression checks and quality metrics to control AI outputs.

RAG / Chat with Your Data
Build governed RAG chat with your data solutions using secure retrieval, permissions-aware context, and measurable answer quality controls.

API & System Integrations
Design and implement API integrations connecting business systems with secure authentication, retries, logging, and supportable middleware patterns operations.

Local & Private LLM Infrastructure
Design and run local or private LLM infrastructure with controlled access, network isolation, predictable costs, and integration-ready platforms.