Modernize any codebase.
In hours, not quarters.
Point Modernize-X at a repository. Get an AI-graded readiness score across 15 dimensions, one-click CVE and runtime upgrades, and guided migration from legacy stacks — ready to merge on day one.
Assess, upgrade, or migrate — every path ends in a PR.
A defensible score for every repo, in three minutes.
Static analysis plus targeted LLM evaluators grade your code across 15 cloud-relevant dimensions. Every dimension ships a ranked tip list with one-click 'Resolve with AI' fixes.
- Static analysis + CVE/SBOM resolution
- Architecture, data, API & test signals
- AWS Bedrock review + composite scoring
- Multi-cloud TCO with nightly pricing
The signals that decide whether code is ready to modernize.
Every Modernize-X assessment scores against fifteen dimensions and rolls them up into a composite readiness number — the same one your IC, CTO, or platform lead will ask about.
Dependencies
Direct + transitive graph, drift, deprecations
Security / CVEs
SBOM + advisories, exploitability ranking
Code Quality
Complexity, duplication, hotspots, smells
Architecture
Layering, coupling, dependency cycles
Tests
Coverage, flakiness, golden-path gaps
Docs
READMEs, ADRs, runbooks, ownership
Build / CI
Pipeline hygiene, cache hits, time-to-green
Deployment
Release cadence, rollback, env parity
Performance
Hot paths, N+1s, cache effectiveness
Observability
Logs, metrics, traces, SLOs
Cost
Compute / storage / egress posture vs. peers
Compliance
Framework posture scoring across common controls
Data Layer
Schema fitness, migrations, hot tables
APIs
Contracts, versioning, backwards compat
DX
Onboarding time, local dev, feedback loops
More dimensions
On the roadmap: licensing posture, accessibility, ML lineage, and custom dimensions you define.
Request dimension →Four steps from main to mergeable.
Send us a codebase. We run the assessment. We walk you through the findings. You pick what ships.
- 01
Share the codebase
Send us a GitHub, GitLab, or Bitbucket repo — or a tar.gz of a private one. Everything under NDA.
- 02
We run the assessment
Our team runs the 9-phase Modernize-X pipeline and scores against fifteen dimensions. You don't have to babysit it.
- 03
We walk you through it
We share the findings, ranked by impact. Engineers drill in; stakeholders read the exec summary.
- 04
Pick a path forward
Targeted upgrades, a migration plan, or a phased modernization roadmap — whichever fits the codebase.
Bring us a codebase. We'll walk you through the assessment.
Tell us about your repo and what you're trying to modernize. Our team runs the Modernize-X pipeline against it and walks you through the findings — composite readiness score, structured report, and a recommended upgrade or migration path.
Questions teams ask before running a scan.
Short answers from the team building Modernize-X at San Data Systems.
Modernize-X generates comprehensive technical assessment reports in 2-3 hours for typical mid-market targets, compared to 1-2 weeks for traditional manual code review. Our engine analyzes codebases, infrastructure, dependencies, security vulnerabilities, and compliance indicators in parallel, producing structured findings that your technical diligence team validates and investigates further. Teams typically complete their validation and deliver IC-ready materials within 1 week vs 2-3 weeks traditional, while preserving the ability for experts to drill into high-risk areas identified by the assessment.
Modernize-X analyzes applications built on all major languages (Python, Java, JavaScript, Go, .NET, PHP, Ruby) and frameworks. We support migration planning to AWS, Azure, GCP, and hybrid environments. Our VM analysis works with VMware, Hyper-V, and physical infrastructure. Integration with Docker, Kubernetes, Terraform, and all major CI/CD platforms.
Yes. Modernize-X integrates with virtual data rooms (Intralinks, Merrill DataSite, ShareVault), CRM systems (Salesforce, HubSpot), and collaboration tools (Slack, Teams, Asana). We provide REST APIs for custom integrations and support SSO (SAML, OKTA, Azure AD) for enterprise authentication.
From startup MVPs (10K lines) to enterprise applications (10M+ lines). Our distributed analysis engine scales horizontally to handle codebases of varying complexity. Report generation typically takes 2-3 hours for single-repository applications and 4-8 hours for complex multi-repository architectures, depending on codebase size and technology diversity. Larger engagements benefit from phased assessment approaches.
M&A advisory firms conducting technical due diligence, private equity firms evaluating portfolio acquisitions, corporate development teams assessing acquisition targets, and portfolio companies planning post-merger integrations. Our platform is designed for investment professionals who need rapid, structured technical insights to inform deal decisions and integration planning.
Generated Terraform/CloudFormation templates, Dockerfiles, Kubernetes manifests, CI/CD pipeline configurations (GitHub Actions, GitLab CI, Jenkins), database migration scripts, API contract mappings, monitoring & alerting setup, and rollback procedures. All outputs follow infrastructure-as-code best practices and serve as customizable starting points for your DevOps team to review, test, and refine before production deployment.
Our TCO models integrate real-time pricing APIs from AWS, Azure, and GCP. We model compute, storage, networking, data transfer, managed services, and operational overhead. Includes sensitivity analysis for usage variability and reserve instance discounting.
Modernize-X analyzes VM infrastructure and application architecture to generate containerization roadmaps with Dockerfiles, Kubernetes manifests, and infrastructure-as-code templates. Our engine identifies application dependencies, stateful vs. stateless services, data persistence requirements, and inter-service communication patterns to recommend migration sequencing and refactoring opportunities. This includes identifying tight coupling that may benefit from service decomposition, estimating compute/storage right-sizing for containers, and flagging compatibility issues with legacy dependencies. Generated artifacts provide your DevOps team with a comprehensive starting point that typically accelerates planning phases from 2-3 weeks to 3-5 days. All templates require expert review, testing, and customization before production deployment, ensuring your team maintains control over architecture decisions while reducing manual discovery work.
Modernize-X generates data migration sequencing plans based on application dependency analysis and database schema assessment. This includes identifying data movement strategies (lift-and-shift vs. schema transformation), estimating data transfer timelines and bandwidth requirements, recommending migration tooling (AWS DMS, Azure Data Factory, custom ETL scripts), and modeling cutover approaches using replication patterns or blue-green deployment strategies. For M&A scenarios with aggressive integration timelines, we highlight high-risk data dependencies and integration complexity early in due diligence, allowing your technical team to factor data migration effort into deal structuring, integration planning, and resource allocation. All migration plans serve as starting points for your data engineering and application teams to validate, test, and refine based on business requirements and risk tolerance.
Yes. Our analysis engine evaluates application architecture patterns to identify refactoring opportunities for cloud-native deployment. This includes detecting monolithic components that could benefit from service decomposition, identifying stateful services requiring persistence layer redesign, flagging hard dependencies on legacy infrastructure, and recommending API contract modernization for microservices patterns. We provide refactoring complexity scoring (low/medium/high) based on factors like code coupling, database transaction patterns, and session state management. For PE Operating Partners evaluating portfolio company modernization, this allows prioritization of refactoring efforts—tackling low-complexity services first to build operational confidence before addressing high-risk core systems. Refactoring recommendations include estimated effort ranges and serve as discussion frameworks for your engineering team to evaluate trade-offs between lift-and-shift speed vs. cloud-native optimization benefits.