Promising pilots with no production path
Prototypes may demonstrate model capability without resolving identity, integration, evaluation, deployment, cost, or support.
AI Modernization on Microsoft Azure
Modernize AI on Microsoft Azure around governed knowledge, useful agents, and operable workflows
Modernize AI, automation, and knowledge workflows on Microsoft Azure with Foundry agents, Azure AI Search, Document Intelligence, Fabric, Copilot Studio, and production controls.
- Best suited for
- Microsoft 365, Azure, Fabric, and Power Platform estates moving from isolated copilots to governed AI systems
- Engagement shape
- Use-case assessment, target architecture, pilot, migration, and production rollout
Cloud-specific delivery
Architecture, implementation, evaluation, and operations designed for the platform you already run.
Microsoft FoundryAzure AI SearchAzure Document Intelligence
The service in practice
Start with the work that needs to improve
Microsoft Azure AI modernization is not a model-shopping exercise. It is the work of turning a useful experiment into a secure service with dependable data, controlled access, measurable quality, and an operating owner.
Capilano AI helps teams use Microsoft Foundry, Foundry IQ, Azure AI Search, Fabric, Copilot Studio, and Azure integration services to modernize a bounded workflow first, then establish the architecture and delivery practices needed to extend it responsibly across the business.
Why this work matters
Replace operational friction with a service your team can trust
What is getting in the way
A fragmented Microsoft 365, Fabric, Power Platform, and Azure estate
Data, documents, automations, and business logic sit in separate services with inconsistent ownership and controls.
Platform choices made before the workflow
Teams accumulate overlapping services because the operating outcome, evidence standard, and human role were never made explicit.
What the engagement should change
A defensible target architecture
A practical design for Foundry agents, governed knowledge, document processing, Fabric data, Power Platform, and Azure operations, grounded in your existing cloud, identity, data, and integration boundaries.
One use case moved into production
A focused workflow delivered with evaluation, approval, telemetry, release controls, and a clear handoff to operations.
A modernization sequence leaders can fund
Dependencies, risks, platform decisions, and follow-on opportunities organized into a phased roadmap rather than an unbounded transformation program.
Platform capabilities
Use the cloud as a delivery system, not a model catalogue
We select managed services around the workflow, identity model, data boundaries, quality target, and operating responsibility.
01Microsoft Foundry
Design and operate agents, model endpoints, tools, evaluations, and enterprise controls.
02Azure AI Search
Build permission-aware hybrid retrieval and citation-backed enterprise knowledge experiences.
03Azure Document Intelligence
Extract, classify, validate, and route invoices, forms, contracts, images, and records.
04Microsoft Fabric and OneLake
Ground analytics and AI in governed lakehouse, warehouse, semantic, and operational data.
05Copilot Studio and Power Platform
Modernize low-code copilots and workflows with environments, APIs, ALM, and governance.
06Entra, Purview, and Azure Monitor
Apply identity, data governance, audit, tracing, alerting, and accountable operations.
Where to apply it
Start with a bounded operating outcome
Each use case is scoped with data access, integration, evaluation, human approval, monitoring, and ownership from the beginning.
Build a service or operations agent
Create a Foundry agent that uses approved knowledge, calls business tools, and escalates decisions to people.
Process documents into business workflows
Combine Document Intelligence, agent review, and APIs to validate and post structured records.
Ground AI in Fabric and company knowledge
Connect agents to governed OneLake, semantic models, SharePoint, and Azure AI Search with citations and access controls.
Scale low-code automation safely
Move fragile desktop flows and isolated copilots into governed cloud services with ALM, telemetry, and ownership.
How we deliver
Evidence before scale. Ownership before launch.
The method is intentionally practical: reduce uncertainty early, build the full operating path, and leave the service with people who can run it.
See our delivery approach- 01
Define the operating outcome
Map the work, baseline, users, decisions, exceptions, risks, and evidence required to call the engagement successful.
- 02
Prove the difficult assumptions
Test the data, integrations, model or platform behavior, quality target, and human workflow before scaling the build.
- 03
Build the complete service
Implement identity, data, workflow, evaluation, telemetry, deployment, documentation, and recovery—not only the visible AI feature.
- 04
Release with an owner
Roll out in controlled stages, train the operating team, review production evidence, and convert confirmed failures into improvements.
Typical engagement
What your team receives
The exact scope follows the operating outcome and current environment. These are the core work products typically required to make the result useful and supportable.
AI use-case, estate, and dependency assessment
Microsoft Foundry agent and model architecture
Identity-aware grounding across SharePoint, Fabric, and Azure AI Search
Low-code, API, connector, and Copilot Studio modernization
Evaluation, tracing, safety, and release gates
Phased migration, ALM, enablement, and operating runbooks
Questions to resolve early
Frequently asked questions
Do we need to move everything to Microsoft Azure?
No. We start with the systems and constraints you already have. The target design can keep selected data or applications outside Microsoft Azure when that is the safer or more economical choice.
Can you modernize an existing pilot or low-code automation?
Yes. We review its workflow, prompts, tools, data access, failure modes, deployment method, and ownership, then decide what can be retained and what should be redesigned.
How do you choose the first use case?
We look for repeatable work with a measurable baseline, accessible data, a willing process owner, and a safe way for people to review or take over when needed.
What makes the result production-ready?
Production readiness includes identity, data protection, tested integrations, representative evaluations, telemetry, cost controls, release gates, incident handling, documentation, and an accountable owner.
Field notes
Read the implementation detail
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Bring us the workflow—not a finished AI specification
We will help clarify the operating outcome, difficult assumptions, delivery path, and evidence required for a responsible investment decision.