CloudReadyAI automates the assessment phase of modernization by turning raw infrastructure data into migration intelligence: readiness signals, dependency clarity, cost scenarios, and a plan you can defend.
Most assessments are interview-driven and document-heavy—so risk shows up late, costing gets rebuilt, and teams spend weeks aligning on assumptions instead of moving.
When discovery and evaluation are manual, failure is delayed — not avoided. Data is incomplete, dependencies are inferred, and outputs expire the moment reality changes.
CloudReadyAI ingests your infrastructure data, validates it, maps relationships, and generates evidence-backed insights and recommendations that stay current as inputs change.
CloudReadyAI is designed to work with incomplete environments. You can start with a few slices and expand coverage. The system tracks completeness and flags what’s missing before you commit to a plan.
Servers
CPU/RAM, OS, environment, ownership, tags.
Storage
Volumes, capacity, performance tiers, attachments.
Applications
App inventory, criticality, owners, lifecycle.
Databases
DB engines, versions, hosting, constraints.
Dependencies
App-to-app and app-to-server relationships.
Utilization
CPU/memory trends to support right-sizing.
Network
Subnets, security zones, routing constraints.
Business metadata
Cost centers, priorities, compliance, timelines.
Coverage-aware outputs
If you only have servers + storage, outputs reflect that and highlight what’s missing.
Validation before analysis
Detect broken references, duplicates, missing required fields, and stale exports early.
Traceability
Every recommendation links back to the data slices that support it.
The platform is a pipeline: ingest → normalize → analyze → cost → recommend. Each step produces artifacts you can use immediately—without needing to show the tool UI.
Bring your environment in—fast—without scheduling multiple SME interviews.
The goal is simple: produce usable, defensible deliverables that reduce rework. These can be refreshed as new data arrives.
Readiness & data quality findings
What’s missing, inconsistent, or risky—before you lock a strategy.
Dependency-informed migration waves
Group workloads into waves based on relationships, owners, and constraints.
TCO snapshots with assumptions
Cost scenarios that show what drives the numbers and what inputs support them.
Modernization recommendations
Rehost/refactor/retain/retire signals tied to evidence and coverage.
Architecture-ready diagram inputs
Structured outputs that support diagram generation and target-state design.
Stakeholder-ready reporting
Summaries designed for both technical and executive alignment.
We’ll confirm what data you have, what you can expect from each slice, and which outputs will be defensible given your current coverage.
Short answers that help buyers and technical teams understand what’s required and what they’ll get.
Start with what you already have. Many teams begin with server inventory (CPU/RAM/OS) and expand into apps, databases, and dependencies as they gain access to exports or source systems.
That’s normal. CloudReadyAI tracks coverage and flags gaps, duplicates, and broken references early. Outputs are coverage-aware so you don’t overstate certainty.
Spreadsheets and decks are static and go stale. CloudReadyAI treats the assessment as a pipeline: ingest → validate → analyze → cost → recommend, and results can be refreshed as data changes.
Readiness findings, dependency-informed migration waves, cost snapshots with assumptions, and recommendations tied to evidence. The exact outputs depend on the slices and completeness of your data.
The roadmap supports private/isolated deployments based on customer requirements. For now, the private preview focuses on validating inputs, outputs, and workflow fit for your environment.
