The diagram is not the system
Actual identities, network paths, data copies, manual recovery steps, exceptions, and ownership differ from the intended architecture.
Cloud & Data Architecture Review
Make the next cloud and data decision with an independent view of risk, cost, and readiness
Review cloud platforms, data pipelines, quality, security, cost drivers, and operating risks, then define a practical modernization roadmap.
- Best suited for
- CTOs, CIOs, and data leaders planning a cloud or data modernization decision
- Engagement shape
- Architecture assessment and modernization roadmap
The service in practice
Start with the work that needs to improve
Architecture reviews are most useful before a costly commitment, after rapid growth, or when recurring incidents reveal that the documented design no longer matches the operating reality.
Capilano AI reviews cloud platforms, data flows, integration, security, reliability, delivery practices, cost drivers, and AI readiness. We separate urgent risk, structural debt, and optional improvement so leaders can act in the right order.
Why this work matters
Replace operational friction with a service your team can trust
What is getting in the way
Modernization choices are vendor-led
Platform recommendations arrive before workload evidence, operating capability, migration risk, and exit constraints are understood.
AI plans ignore data and operational debt
Agent and analytics ambitions depend on quality, permissions, semantics, integration, observability, and support foundations that have not been assessed.
What the engagement should change
A current-state evidence pack
Architecture, dependencies, data flows, controls, reliability, costs, ownership, and known gaps documented at decision-making depth.
A prioritized risk and opportunity register
Findings classified by consequence, likelihood, effort, dependency, and decision horizon.
A pragmatic target roadmap
Immediate safeguards, near-term modernization, enabling foundations, and later options with trade-offs made clear.
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.
Pre-modernization assessment
Establish what should be retained, repaired, replatformed, rebuilt, retired, or deferred.
AI and data readiness review
Assess whether identity, data, semantics, integration, governance, and operations support priority AI use cases.
Reliability and cost investigation
Trace recurring incidents, capacity issues, manual recovery, duplication, and avoidable cost to architectural causes.
Independent design review
Challenge a proposed target architecture before procurement, migration, or a major delivery commitment.
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.
Current-state cloud and data architecture review
Pipeline, quality, security, and governance assessment
Risk register and dependency map
Cost and capacity observations
Target architecture and trade-off analysis
Prioritized modernization roadmap
Questions to resolve early
Frequently asked questions
Is this review tied to a migration project?
No. The output may recommend targeted remediation, staged modernization, a migration, or retaining the current platform. The evidence determines the path.
What access is required?
Typically architecture and operational documentation, read-only configuration evidence where appropriate, cost and incident summaries, and interviews with platform, data, security, application, and business owners.
Will the review include cloud costs?
We examine major cost drivers, allocation visibility, capacity assumptions, waste patterns, and architectural trade-offs. This is an engineering and operating review rather than a financial audit.
Can you review a design prepared by another vendor?
Yes. We focus on assumptions, workload fit, security, data, integration, reliability, operability, cost, migration risk, skills, and exit constraints—not on defending a preferred platform.
Apply it in context
Connect the service to an operating job
Use case
Managed AI and automation operations
Monitor quality, incidents, cost, integrations, and change across production AI and automation workflows with accountable ownership.
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.