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Value

Right-sized Azure footprint

We pick the smallest set of Azure services that meets the decision you need to make — and skip the ones that look impressive but don't pay back.

Cost-aware by default

Capacity, storage tiers, and idle resources reviewed against your bill, not against a reference architecture diagram.

Analytics-grade data platform

Fabric, Synapse, Data Lake, and SQL services composed into a platform your analysts and AI workloads can actually trust.

AI services applied to real work

Azure OpenAI, AI Search, and Document Intelligence wired into your real document libraries and process backlog — not a sandbox demo.

Make better decisions using data

Azure is broad. The list of services keeps growing, the pricing pages keep changing, and the path from “we have data somewhere” to “we have a trustworthy analytics platform” rarely matches the reference architecture diagrams.

Canopy Analytic helps teams pick the right Azure services for the work that actually has to happen — and skip the ones that look impressive in a deck but don’t pay back in your environment.

Where we help

  • Data platform design across Fabric, Synapse, Data Lake, and SQL services
  • Cost-aware architecture: right-sized capacity, predictable bills, no idle resources
  • AI services (Azure OpenAI, AI Search, Document Intelligence) applied to real document libraries
  • Automation built on Logic Apps, Functions, and Power Platform connectors

Azure is the substrate our analytics, AI, and applications work runs on. We choose services because they make sense for your decisions and your budget — not because they’re new.

Common questions

Do we need Microsoft Fabric, Synapse, or both?
Almost never both. We assess your data volume, latency needs, and existing licensing, then pick one. Most mid-market Azure shops land on Fabric; heavier transformation pipelines still favor Synapse.
Can you work inside our existing Azure tenant and subscriptions?
Yes. We engage as collaborators in your tenant under your governance, IAM, and policy rules. We don't require new subscriptions or a parallel environment.
How do you keep Azure spend predictable?
Right-sized capacity SKUs, autoscale limits, lifecycle policies on storage, and a monthly review cadence against your actual bill — not the projected one.
What if we're already on AWS or GCP for some workloads?
We don't force a migration. Azure earns each workload it gets. We're happy to integrate with cross-cloud data and identity rather than rip-and-replace.