Enterprise platform migration
Move critical systems to the cloud with a plan you can verify first.
Cutover reads your applications, data and infrastructure, drafts the migration plan, and generates the target environment. Your team approves each step before anything changes.
Runs on synthetic sample data. No sign-up needed.
Discovery never writes to your systems
Every finding links to its source
Named sign-off before any change
Azure, AWS and Google Cloud targets
Watch it work
A migration in five scenes.
Follow one application from read-only discovery to an approved cutover. Pause, step through, or jump to any scene.
How it works
From legacy system to approved plan in four steps.
Connect read-only
Point Cutover at a pinned source commit and read-only access to infrastructure. Nothing is changed.
Analyze
Map the code, data model, interfaces and deployment. Each finding is marked verified or inferred.
Plan and compare
Generate target designs for Azure, AWS or Google Cloud, with Terraform and a cost range for each.
Approve and execute
A named approver signs the plan. Staging applies first, and production waits for explicit sign-off.
The problem
Migrations stall because nobody fully trusts the plan.
Legacy applications have years of undocumented behavior. Data models drift. Interfaces break in ways no one tested. Teams spend months on discovery, and the cutover still feels like a leap of faith.
Discovery takes months
Engineers manually trace code, configs and integrations to build an inventory that is out of date by the time it is finished.
Plans aren't evidence-based
Architecture decks describe what the system should do, not what it actually does.
Risk is hard to sign off
Security, compliance and business owners need proof of what changes, who approved it, and how to roll back.
See the difference
Before and after the cutover.
Drag the handle to compare the legacy footprint with the managed target. The target is generated from the same analysis, not drawn by hand.
Efficiency
Less effort and calendar time per migration.
These figures come from an illustrative model of one mid-size application with interfaces and a database. They are planning assumptions, not measured results. Adjust the inputs to match your own environment, and validate them in a pilot.
Effort by phase, hours
Hours saved
Before: traditional effort
Manual discovery and hand-written plans
- Engineers trace code, configs and interfaces by hand
- Architecture and Terraform written from scratch
- Compliance evidence assembled at the end
- Approvals wait on documents that go stale
After: with Cutover
Analysis first, people on the decisions
- Read-only analysis maps the system in hours, not months
- Target designs and Terraform generated for review
- Evidence and plan hashes recorded as work happens
- Approvers review a current, signed plan
Assumptions by phase, per application
Person-hours, traditional vs with Cutover. Testing and cutover execution stay largely manual, which is why they show the smallest gains.
| Phase | Traditional (h) | With Cutover (h) | Reduction |
|---|---|---|---|
| Total |
Illustrative model. Results depend on application size, language, interface count, data volume and existing documentation. Measure your own baseline before relying on these numbers.
The platform
One system of record for every migration.
Application intelligence
Framework detection, API routes, client-facing screens and role-based views, drawn from the code.
Data migration planning
Schema history, table groups and data-flow maps, with redaction applied before any sample is analyzed.
Integration mapping
Find API clients, message feeds and file transfers, then decide which ones move, change or retire.
Infrastructure drift
Compare what is running with what the infrastructure code declares, and flag the gaps before they break a cutover.
Target generation
Terraform for managed containers or Kubernetes, with secrets, identity and private networking included.
Audit trail
Who approved what, when, and against which plan hash. Export a signed report for auditors and boards.
Industries
Built for the systems that can't afford downtime.
Each sector has its own regulations, legacy platforms and risk profile. Explore how Cutover fits yours.
Healthcare
EHR-adjacent apps, HL7 and FHIR interfaces, HIPAA-aligned controls.
Coming soonFinancial services
Core banking, payments and reporting systems under PCI DSS and SOX controls.
Coming soonGovernment
Agency workloads planned against NIST 800-53, FedRAMP and StateRAMP baselines.
Coming soonInsurance
Policy and claims platforms with state-by-state regulatory evidence.
Coming soonEducation
Student information, learning platforms and research systems under FERPA.
Coming soonEnergy and utilities
Customer billing, meter data and enterprise systems, with NERC CIP scope boundaries.
Coming soonRetail and e-commerce
Storefront, order and point-of-sale platforms, with peak-season cutover windows.
Coming soonManufacturing
ERP, quality and supply chain systems, scoped to IT and away from plant-floor control.
Security and governance
Designed for regulated workloads from day one.
- Read-only discovery by default, with source access pinned to a commit
- Sensitive samples redacted before any analysis runs
- Named approvals, plan hashes and an immutable audit log
- Terraform output you can review, version and run yourself
- Control mapping for HIPAA, PCI DSS, SOX and NIST 800-53 (confirm scope with your compliance team)
Your data stays yours
Deployment options and data residency are agreed during discovery. We do not train models on customer code or data.
See it in five minutes
Walk through a real migration plan.
The demo takes you from sign-in to a signed plan, using a sample patient portal. Compare Azure, AWS and Google Cloud targets side by side.