InfraStacksSign In
Enterprise adoption system for Azure AI Foundry

From Enterprise Workflow
to Azure AI Foundry.

InfraStacks turns enterprise operating problems into governed Azure AI Foundry deployments inside the customer's Azure estate.Discovery. Architecture. Configuration. Validation. Deployment.

Enterprise AI SponsorsEnterprise ArchitectureProduction AI Programs

Azure commitments do not automatically become production AI

Enterprises already have the budget, the data, and the mandate. What they lack is a repeatable path from business process pain to a deployed Azure AI Foundry solution that survives validation.

Discovery Gap

Business-native

Stakeholders know the workflow problem. They do not know how to express it as models, prompt flows, search indexes, and guardrails.

to Foundry-native
THE PIPELINE WE OPERATE

InfraStacks

Discovery to Live

We discover use cases, structure them, compile Foundry configurations, validate against real examples, deploy into the customer subscription, and monitor adoption outcomes.

Validation Gap

80% to 99%

Most pilots die between a promising demo and a reliable business process. InfraStacks closes that gap with structured validation and versioned iteration.

demo-grade to production-grade

The InfraStacks delivery pipeline

Every stage moves an enterprise use case from unstructured stakeholder knowledge to a deployed Azure AI Foundry solution.

Raw Context

Discovery

Structured stakeholder interviews that capture business process detail without forcing customers to think in models or infrastructure.

Structured

Use Case

Formalized representations of the workflow, data requirements, success criteria, guardrails, and Azure service mapping.

Readable

Architecture

Customer-readable deployment plans that explain triggers, data flow, model selection, outputs, review gates, and expected cost.

Executable

Configuration

Deterministic compilation into Azure AI Foundry resources, Bicep templates, prompt flows, search, and safety configurations.

Tested

Validation

Real-example testing with customer review, versioned refinement, and measurable graduation from pilot quality to production readiness.

Deployed

Live

Deployment into the customer Azure subscription with monitoring for adoption, latency, usage, and outcomes over time.

What InfraStacks produces

The output is not a slide deck. It is a set of Azure AI Foundry configurations and deployment assets ready to validate and run.

Microsoft AI Foundry model deployment

The right model, parameters, safety profile, and capacity plan for the customer use case.

Prompt flow definition

Orchestration logic, system prompts, tool definitions, and business rules encoded for the workflow.

Azure AI Search configuration

Data sources, chunking, embeddings, and semantic retrieval settings for enterprise knowledge access.

Content safety rules

Input and output guardrails aligned to the tolerance and review gates of the business process.

Bicep deployment template

Versioned infrastructure-as-code ready to deploy the solution into the customer Azure subscription.

Monitoring workbook

Consumption, latency, accuracy, and cost visibility after the solution is live.

Built for the enterprise teams that make AI programs real

InfraStacks aligns executive sponsors, operating owners, and control teams around one motion: converting committed Azure spend into governed Microsoft AI Foundry deployments.

Enterprise AI Sponsors

Own the AI mandate, Azure budget, and production accountability, but need a repeatable operating model that turns candidate workflows into approved deployments.

CIOs, CTOs, Heads of AI, platform leaders, and digital transformation executives

Enterprise Function Leaders

Own high-value operating processes and need a structured way to translate them into governed AI systems with clear inputs, outputs, review gates, and ROI.

Finance, operations, procurement, support, claims, compliance, and shared-services teams

Platform, Risk & Control Teams

Define the architecture, governance, review gates, and operational controls required before a Microsoft AI Foundry solution can be approved for production.

Enterprise architecture, platform engineering, security, risk, legal, compliance, audit, and review stakeholders

Catalog pillars for repeatable Foundry deployment

Start with structured enterprise workflows that can be validated, approved, and scaled into a broader catalog of production use cases.

Document Intelligence

Structured document workflows

Invoice processing
Contract review
Claims intake

Knowledge Operations

Retrieval and reasoning workflows

Policy Q&A
Internal copilots
Research assistants

Service Operations

High-volume response workflows

Case triage
Support drafting
Escalation routing

Risk & Compliance

Review and control workflows

Audit evidence review
Control testing
Compliance checks

Finance Operations

Back-office decision workflows

AP coding
Expense audit
Vendor review

Industry Workflows

Vertical process families

Healthcare review
Insurance claims
Public sector casework
Built to encode tens to hundreds of repeatable enterprise workflows into Azure AI Foundry configurations

Ready to move an enterprise process into production AI?

Bring the operating process, the executive sponsor, and the Azure account context. InfraStacks brings the discovery method, the Foundry architecture, the validation model, and the deployment assets.

Start Enterprise Discovery

We'll focus on the use-case portfolio, data boundary, review model, Azure services, and what has to be true for production approval.