Microsoft Solutions Partner for Data & AI
Azure AI Foundry Consulting Services
Specialists
Azure AI Foundry implementation experience
Microsoft Solutions Partner
Built across the major agent ecosystems
Copilot Studio
Azure AI Foundry
AWS Bedrock
Google Vertex AI
OpenAI
LangChain / LangGraph
AI Foundry Challenges That Hold Teams Back
01
Are teams building AI apps without a shared platform?
02
Is prompt work happening without versioning, testing, or governance?
03
Are AI costs hard to explain across projects, teams, and models?
Want to use open-source models alongside OpenAI but no strategy?
What We Do
Build an AI Platform, Not Isolated Projects
01
A Shared AI Foundation
Set up AI Foundry as a reusable organisational platform for models, prompts, evaluation, and governance.
02
Evaluation before production
Test accuracy, groundedness, relevance, and safety before AI reaches users.
03
Right model for each task
Choose models based on task requirements, performance, cost, latency, and deployment needs.
Production-Ready RAG Pipelines
Build reliable, monitored, and maintainable RAG pipelines designed beyond the proof-of-concept stage.
05
AI Costs Under Control
Track usage and spending with dashboards and alerts before model consumption scales unexpectedly.
06
Responsible AI built in
Our Services & Capabilities
01
Model Catalog & Selection
Configure Azure AI Foundry’s model catalog and select models based on use case, performance, cost, and latency.
- Model catalog
- Azure OpenAI
- Open-source models
- Model selection
02
Prompt Engineering & Management
Design, test, and version prompts while using Prompt Flow to create repeatable multi-step AI workflows.
- Prompt engineering
- Prompt Flow
- Versioning
- Testing
03
RAG Orchestration
Build RAG pipelines covering ingestion, embeddings, Azure AI Search, retrieval, and grounding to connect AI with business data.
- RAG
- Embeddings
- AI Search
- Retrieval
- Grounding
04
Evaluation & Red-Teaming
Evaluate AI outputs for accuracy, groundedness, relevance, and safety before production deployment.
- Evaluation
- Accuracy
- Groundedness
- Red-teaming
- Safety
05
Fine-Tuning
Fine-tune models with domain-specific data when prompt engineering alone cannot deliver the required accuracy or consistency.
- Fine-tuning
- Domain data
- Specialised tasks
- Model optimisation
06
AI Agents
Build agents that reason, plan, use tools, and execute multi-step tasks with memory and orchestration.
- AI agents
- Tool calling
- Memory
- Orchestration
07
Responsible AI & Content Safety
Configure content filtering, safety policies, and governance controls to support compliant, trustworthy AI applications.
- Content filtering
- Safety policies
- Governance
- Responsible AI
08
Deployment & Monitoring
Deploy AI applications with endpoint management, usage monitoring, latency tracking, and cost governance for production operations.
- Deployment
- Monitoring
- Latency
- Usage
- Cost governance
Technology Stack & Technical Capabilities
01
AI Foundry Platform Setup
Configure projects, model deployments, Entra ID access, RBAC, networking, logging, environment separation, and deployment readiness.
- Project setup
- Model deployment
- Security
- Networking
- Foundation
02
Model Strategy & Catalog Configuration
Configure Azure OpenAI and open-source models with selection guidelines aligned to task requirements and cost.
- Model catalog
- GPT-4o
- Open-source
- Selection guidelines
- Cost optimisation
03
Prompt Engineering & Prompt Flow
Build versioned, tested Prompt Flows connecting prompts, retrieval, tools, and business logic into repeatable pipelines.
- Prompt flow
- Multi-step
- Versioned
- Tested
- Repeatable pipelines
04
RAG Pipeline Development
Build production RAG covering ingestion, chunking, embeddings, Azure AI Search, retrieval, grounding, evaluation, and refresh workflows.
- RAG pipelines
- Embeddings
- AI Search
- Data ingestion
- Grounding
05
AI Evaluation & Red-Teaming
Implement automated and manual evaluation for accuracy, groundedness, relevance, coherence, and safety before production.
- Evaluation metrics
- Automated evaluators
- Red-teaming
- Safety testing
06
Fine-Tuning & Custom Models
Fine-tune Azure OpenAI or open-source models using domain data when prompt engineering alone is insufficient.
- Fine-tuning
- Domain data
- Custom models
- Improved accuracy
AI Agent Development
Build AI Foundry agents with multi-step reasoning, tool calling, API integration, and autonomous task execution.
- AI agents
- Tool calling
- Multi-step
- Autonomous
- Orchestration
Responsible AI & Cost Governance
Configure content safety, responsible AI controls, cost dashboards, and usage alerts for governed AI operations.
- Content safety
- Governance
- Cost monitoring
- Usage alerts
- Responsible AI
Our AI Foundry Methodology
Free consultation & assessment
We assess your AI maturity, current Azure AI usage, and goals — and recommend the right AI Foundry engagement. No cost, no obligation.
Platform architecture & setup
We design the AI Foundry architecture: project structure, model deployments, security, networking, and connections to your data sources.
Model strategy & prompt engineering
We configure model selection, build prompt flows for your use cases, and establish prompt versioning and testing practices.
RAG pipelines & data integration
We build production RAG pipelines: data ingestion, embedding generation, Azure AI Search indexes, and retrieval configuration.
Evaluation & responsible AI
We implement evaluation metrics, automated evaluators, red-teaming, content filtering, and responsible AI governance.
Deployment, monitoring & handoff
We deploy the configured platform and workloads, validate access and monitoring, document architecture and operating procedures, configure cost alerts, and train your team on governance, evaluation, and release practices.
Buyer Scenarios & Recommended AI Foundry Engagements
| Your situation | What we deliver | Engagement type |
|---|---|---|
“Starting enterprise AI, need the platform” | Full AI Foundry platform setup | Platform programme |
“Building RAG apps, need production pipelines” | RAG pipeline architecture | RAG build |
“AI in production, no evaluation” | Evaluation framework + red-teaming | Evaluation sprint |
“Azure OpenAI costs growing, no visibility” | Cost governance + monitoring | Cost governance setup |
“Want to use open-source models too” | Model strategy + catalog config | Model strategy + catalog config |
“Need AI agents, not just Q&A” | AI agent development on Foundry | Agent build |
“No responsible AI framework” | Content safety + governance | Responsible AI sprint |
“Prompt engineering is ad-hoc” | Prompt flow + versioning + testing | Prompt flow + versioning + testing |
Case Studies
Financial services company, 3 AI projects underway.
Three AI teams building independently — no shared models, no evaluation, no cost governance. Duplicated effort and inconsistent quality.
Set up AI Foundry as the shared platform: model catalog, prompt management, evaluation framework, content filtering, and cost dashboards.
Healthcare provider, clinical knowledge assistant.
RAG POC worked in testing but was unreliable in production — groundedness issues, slow retrieval, and no evaluation.
Rebuilt RAG pipeline on AI Foundry: optimised embedding strategy, improved AI Search indexing, added evaluation metrics, and configured content filtering for clinical data.
Technology company, internal operations tool.
Wanted AI beyond Q&A — agents that could query databases, call APIs, and generate reports autonomously.
Built AI agents on AI Foundry: multi-step reasoning, tool calling (SQL, APIs, calculations), and prompt flow orchestration.
Industries We Serve
Financial Services
AI platform for compliance-sensitive AI development
Healthcare
Technology
Product AI platform and multi-model strategy
Insurance
Document AI and claims automation platform
Manufacturing
Operational AI and quality inspection models
Professional Services
Retail
Product AI, personalisation, and recommendation models
Government
Responsible AI with governance and auditability
From a focused RAG pipeline build to an enterprise AI platform serving multiple teams and projects.
Insights
AI Foundry Insights & 2026 Platform Guidance
In 2026, organisations building AI applications without a platform are repeating the mistakes of early cloud adoption: every team doing their own thing, no shared infrastructure, no governance, and costs growing invisibly. Azure AI Foundry is the answer: one platform for model management, prompt engineering, RAG orchestration, evaluation, and responsible AI. The organisations getting AI right are those that set up the platform FIRST and build applications ON it — not those building one-off AI projects in isolation.
01
Platform enables speed — a team building on AI Foundry starts a new RAG app in days; a team starting from scratch spends weeks on infrastructure
02
03
Do we need AI Foundry or can we just use the Azure OpenAI API directly?
Frequently Asked Questions
What is Azure AI Foundry?
Microsoft’s unified platform for building, evaluating, and deploying enterprise AI applications — formerly known as Azure AI Studio. It provides model catalog (OpenAI + open-source), prompt engineering, RAG orchestration, evaluation, fine-tuning, AI agents, content filtering, and deployment management in one hub.
How is AI Foundry different from just using Azure OpenAI?
What models are available in AI Foundry?
Azure OpenAI models (GPT-4o, GPT-4, GPT-4o-mini, embeddings), open-source models (Llama, Mistral, Phi), and the ability to bring custom/fine-tuned models. The model catalog lets you compare, test, and deploy the right model for each task.
What is prompt flow?
AI Foundry’s visual pipeline builder for AI applications — connects prompts, data retrieval (RAG), tool calls, and business logic into repeatable, testable, versionable workflows. The production way to build AI pipelines.
How much does AI Foundry consulting cost?
Most AI Foundry consulting engagements are estimated from the expected delivery effort at $400–$600 per day. Azure consumption is separate and usage-based. A focused setup or assessment may take a few weeks, while a full platform programme depends on scope, integrations, governance needs, and production-readiness requirements.
What is AI evaluation in AI Foundry?
Testing AI outputs before production: accuracy (correct answer?), groundedness (based on provided data?), relevance (answers the question?), coherence (well-formed?), and safety (appropriate?). AI Foundry provides built-in and custom evaluators.
What is fine-tuning and when do we need it?
Training an AI model on your domain-specific data to improve accuracy for specialised tasks. Needed when prompt engineering alone doesn’t produce accurate enough results — typically for domain-specific terminology, formatting, or classification tasks.
What are AI agents in AI Foundry?
AI applications that reason through multi-step tasks: they plan, call tools (APIs, databases), and combine results. Built using AI Foundry’s agent framework with Semantic Kernel orchestration.
Book My Free AI Foundry Consultation
Tell us the task you have in mind. We’ll assess whether an agent suits it, at what autonomy level, and what the first step should be.
- No cost, no obligation
- Typically responds within 4 business hours
- Confidential — your data isn't shared
- Microsoft Solutions Partner for Data & AI
Typically responds within 8 business hours · Confidential