Data exists but meaning is inconsistent
Measures, customer identities, product hierarchies, and lifecycle definitions vary by report or source system.
Microsoft Fabric + AI
Make governed business data usable by people, analytics, and AI in Microsoft Fabric
Build governed OneLake, lakehouse, warehouse, semantic-model, and Fabric data-agent foundations that make analytics and AI easier to use securely.
- Best suited for
- Microsoft data teams building a governed analytics and AI foundation
- Engagement shape
- Architecture, implementation, migration, and enablement
The service in practice
Start with the work that needs to improve
AI cannot compensate for unclear business definitions, duplicate entities, fragile pipelines, or uncontrolled access. Microsoft Fabric can simplify the data estate, but the value comes from designing OneLake, lakehouse, warehouse, semantic, security, and operating patterns as one system.
Capilano AI helps Microsoft teams prepare trusted data products and semantic context for reporting, copilots, agents, and operational decisions—without separating AI ambitions from data engineering reality.
Why this work matters
Replace operational friction with a service your team can trust
What is getting in the way
Fabric adoption grows without architecture
Workspaces, capacities, lakehouses, warehouses, notebooks, pipelines, and semantic models multiply without clear boundaries.
AI access is broader than data readiness
Conversational experiences expose stale, duplicated, ambiguous, or permission-sensitive information faster than conventional reports.
What the engagement should change
A coherent Fabric foundation
Capacity, workspace, OneLake, lakehouse, warehouse, semantic, deployment, and ownership decisions documented and implemented.
Trusted data products for AI
Curated entities, measures, lineage, quality rules, access policies, and semantic context prepared for agent use.
An operable analytics platform
Monitoring, cost visibility, release practices, incident ownership, documentation, and enablement for the internal team.
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.
Fabric data agent readiness
Prepare semantic models, instructions, access, and evaluated business questions for conversational analytics.
OneLake consolidation
Reduce unnecessary copies while defining trusted zones, shortcuts, ownership, retention, and access.
Analytics modernization
Move fragmented pipelines and reports into governed Fabric data products and semantic models.
AI grounding across structured data
Give agents controlled access to trusted operational and analytical context alongside documents.
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.
Fabric capacity and workspace architecture
OneLake, lakehouse, and warehouse design
Semantic models prepared for AI
Fabric data agents and conversational analytics
Purview-aligned security and governance
Deployment pipelines, monitoring, and cost controls
Questions to resolve early
Frequently asked questions
Do we need to migrate every data workload to Fabric?
No. We identify which workloads benefit from Fabric and where existing platforms should remain. The target architecture can use shortcuts, federation, integration, or phased migration.
Can Fabric data be used by custom AI agents?
Yes, through supported semantic, SQL, API, search, or agent patterns. The right path depends on the question, permissions, freshness, query control, and required evidence.
How do you approach Fabric governance?
We align capacities, domains, workspaces, data products, access, deployment, lineage, quality, sensitivity, ownership, and monitoring with how the organization actually operates.
Can you review an existing Fabric implementation?
Yes. We can assess architecture, capacity, workspace sprawl, pipelines, models, security, cost drivers, operations, and AI readiness, then prioritize improvements.
Apply it in context
Connect the service to an operating job
Use case
Enterprise search and grounded RAG
Give teams permission-aware answers grounded in approved company content, with citations, freshness controls, and measurable retrieval quality.
Use case
Data foundations and integration for AI
Connect governed operational and analytical data to AI workflows through reliable ELT, APIs, semantic models, and ownership controls.
Field notes
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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.