LEGACY MODERNIZATION SERVICES
Sanciti AI provides legacy modernization services and tools that read your existing code, plan the migration in waves and prove the new system behaves like the old one. Enterprise clients have modernized COBOL, Java and .NET applications at 50% lower cost than manual estimates. Sanciti AI reads the codebase directly. Maps every dependency. Extracts the business logic that only exists in the code. Then builds the roadmap from evidence, not estimates. COBOL, J2EE, .NET, 4GL and mainframe estates to cloud-native. One governed pipeline. Your keys, your models.
LLM-agnostic · Secure by design · OWASP & NIST aligned · Audit-grade traceability
Faster modernization cycles
Lower Enterprise QA costs
Faster deployments
Automated test coverage
Fewer production defects
Trusted across industries
Enterprise teams in regulated, scale-conscious industries rely on Sanciti AI
Sanciti LEGMOD
Transform legacy business and technology systems into modern, secure, scalable architectures, faster and safer. LEGMOD plans the modernization wave, then delegates execution to RGEN, CodeGen, and TestAI with continuous human and governance oversight.
Legacy modernization doesn’t fail because of technology it fails because of incomplete system knowledge, hidden dependencies, and manual guesswork. LEGMOD replaces uncertainty with AI-driven intelligence, giving leaders the clarity they need to modernize at scale.
Five agents orchestrate the full SDLC: requirements, code generation, testing, and deployment, with governance and traceability at every handoff.
Reverse-engineers legacy code and artifacts to extract requirements, use cases, and test cases with full traceability.
Generates 80–90% complete, secure, story-aligned code, including backend, frontend, APIs, and deployment scripts.
Automated, fast, high-quality testing with autonomous execution, self-healing tests, and 90%+ coverage.
Packages validated builds into production-ready bundles with compliance gates, rollback, and audit evidence.
Handles live operations after go-live: triage, diagnosis, and run book generation, with knowledge captured on every incident.
Automates documentation, test case generation, code creation, and performance scripting, reducing 40–50% of manual dev effort.
Built-in security aligned with OWASP and NIST, deployed in a single-tenant VPC. Every output security-scored at generation.
Learns your codebase, architecture patterns, naming conventions, and documentation standards. Continuously improves, compounding ROI over time.
Works with GitHub, JIRA, SharePoint, Confluence, Eclipse, IntelliJ, VS Code & CI/CD pipelines and fits into your existing workflows.
WHAT WE MODERNIZE
Not just language conversion. Full business logic preservation with architecture redesign for modern platforms.
COBOL on Mainframe
→ Java / Spring Boot
Batch to microservices. JCL, CICS, VSAM mapped to modern equivalents. The mainframe logic survives. The platform doesn’t.
Heavyweight app servers gone. EJBs and Servlets become lightweight Spring services on Kubernetes. Container-ready at generation.
Windows-only to cross-platform. Linux containers. Entity Framework migrations. WCF to gRPC. IIS dependency removed.
End-of-life Microsoft tech to modern C# with Blazor, React, or Angular. COM objects mapped to managed code.
PowerBuilder, Progress OpenEdge, Informix 4GL, Oracle Forms. Complex data-bound UIs preserved in modern front ends.
Informatica, DataStage, SSIS, Ab Initio. Transformation logic preserved in cloud-native pipelines. Scheduling modernized.
Legacy modernization is the process of rebuilding enterprise systems on technology that actually gets security patches, runs in cloud infrastructure, and doesn’t require a six-month hiring cycle every time someone retires. It goes well beyond moving servers. The code changes. The framework changes. A monolith becomes services. COBOL becomes Java. The business logic stays, but everything holding it back gets replaced.
The tricky part isn’t the conversion. It’s knowing what to convert. Enterprise applications accumulate business rules over decades. Some of those rules exist in documentation. Most don’t. They live in exception handlers, stored procedures, batch job sequences, and conditional paths that only fire under specific data conditions. Miss one during modernization and it surfaces as a production defect three months after go-live. That’s why code-level extraction through RGEN matters more than any workshop or stakeholder interview. The code doesn’t forget what the documentation never captured.
Legacy modernization services from Sanciti AI cover the full scope: portfolio assessment, wave planning, code-level extraction, architecture design, code generation, testing, deployment, and post-go-live support. Each phase feeds the next through a single governed pipeline. Requirements extracted from COBOL flow directly into Java generated by CodeGen, tested by TestAI, and deployed through Deploy. Nothing falls through the cracks between phases because there are no handoffs between disconnected tools.
Enterprise teams evaluating legacy modernization services tend to focus on the conversion step because it’s the most visible. But the assessment is where the ROI lives. Get the assessment wrong and every downstream phase inherits that error. Get it right, from the code, and the plan holds. The timeline holds. The budget holds. That’s not a pitch. It’s what code-level extraction makes structurally possible in a way that manual approaches can’t replicate at portfolio scale.
Without a tool, modernization runs on consulting hours. Analysts read code manually. Developers write the new version line by line. Testers build scripts by hand. Cost scales linearly with application size. A 500,000-line COBOL mainframe? That’s 18 months and a specialized team of contractors who charge a premium because there are fewer of them every year.
A legacy modernization tool breaks that linear curve. RGEN extracts the full functional blueprint from a 500,000-line codebase in weeks. CodeGen generates 80-90% of the target code. TestAI builds the test suite without a QA team writing each case. The economics shift because the tool handles volume and humans handle decisions. That matters for mainframe environments especially, where the legacy skill pool shrinks annually and the applications are large, deeply integrated, and business-critical.
Sanciti AI combines tool automation with service delivery. The platform handles extraction, generation, testing, and deployment. The delivery team handles enterprise context, governance, and program management. It’s the combination that works for legacy software modernization programs where the technology is only half the challenge. The other half is navigating the organizational and regulatory complexity that comes with changing systems that run the business.
Everywhere you work
The same enterprise-grade AI, with security, governance, and full context, wherever your teams build.
Proven outcomes
End-to-end SDLC automation replacing manual diagnostic planning with cross-team visibility.
Reverse-engineered and modernized a monolithic Java platform into scalable microservices.
Complete test automation and performance scripts, reaching 98%+ test coverage.
Different stakeholders across the organization see immediate value from a code-level modernization program. CTOs, engineering managers, security officers, and product owners all benefit from a governed, traceable approach to transforming legacy systems into modern platforms.
CTOs and CIOs use the portfolio assessment data to justify modernization budgets with numbers that came from the code, not from vendor estimates. Wave planning gives them a phased roadmap they can present to the board with cost-benefit data per wave. The conversation shifts from “we think it will take two years” to “wave one delivers these seven applications to production in Q1, here’s the projected maintenance reduction.”
Engineering managers see the change in hiring and retention. A team maintaining a COBOL codebase has limited options for new hires. The same team working on Spring Boot microservices can pull from a talent pool that’s orders of magnitude larger. Release cycles get shorter because modern stacks have mature CI/CD tooling that legacy platforms never supported natively. Peer review time drops by 35% when the generated code follows consistent standards instead of accumulating decades of inconsistent patches.
Security and compliance officers care about the governance layer. Every modernization decision traces from the original code through the requirement, the generated output, the test result, and the deployment record. Audit readiness stops being a quarterly scramble and becomes a natural byproduct of how the platform operates. For HIPAA, SOX, and NIST environments, that traceability is what separates a modernization program that passes review from one that generates findings.
Product owners care about what happens after modernization. Features that took three months on the legacy stack take three weeks on the modern one. Integrations with analytics platforms, mobile apps, and partner APIs become standard work instead of special projects. The application that used to be a constraint becomes a platform the business can build on.
Enterprise-grade compliance and certifications
FAQ
It’s the process of taking old enterprise systems and rebuilding them on technology that still gets security patches, runs in cloud infrastructure, and doesn’t require a six-month hiring cycle every time someone leaves. A COBOL application from 1998 becomes Java microservices on Kubernetes. The business logic stays. The technology underneath changes completely. Companies typically pursue it when maintenance eats more than half the IT budget for a given system, the vendor drops security patches, or the talent pool for the legacy stack dries up.
Assessment, planning, conversion, testing, deployment, and post-go-live support. But the assessment is where programs succeed or fail. Sanciti AI starts from code-level analysis. RGEN reads the codebase, maps dependencies, extracts business rules nobody documented. That feeds wave planning through LEGMOD. CodeGen handles conversion at 80-90% production readiness. TestAI validates at 90%+ coverage. Deploy packages for production with compliance gates. PSAM handles operations after go-live. One platform, one pipeline, full governance.
A platform that automates the heavy lifting. Reading code, extracting business logic, generating the modern equivalent, building test suites, packaging deployments. Without a tool, every step is manual. Developers read code line by line. Testers write scripts by hand. Sanciti AI handles 80-90% of it through agents that work together in one governed pipeline. The tool doesn’t replace decision-making. It replaces the grunt work so your architects and senior developers focus on design decisions and review, not extraction and documentation.
Depends. A single mid-size application, 200K to 500K lines, completes assessment in 2-3 weeks. Full modernization takes 3-6 months depending on target architecture and integration complexity. Portfolio programs run in waves. First wave ships to production within the first quarter. Later waves build on patterns from earlier ones. Timelines estimated from code-level analysis tend to hold. Timelines estimated from workshops and spreadsheets usually don’t, because they miss the hidden complexity that only shows up when someone actually reads the code.
No. Cloud migration moves an application from your data center to cloud VMs. Same code, different location. The COBOL still runs as COBOL. Modernization changes the application itself. COBOL becomes Java. Monolith becomes services. Batch becomes event-driven. Most enterprises combine both because the cloud move creates a natural window to modernize the stack at the same time. But they’re different scopes, different costs, different timelines.
An insurance carrier running claims processing on a COBOL mainframe. The system handles 3 million claims a year, works reliably, and nobody wants to touch it because the last developer who understood the batch scheduler retired four years ago. After modernization through Sanciti AI, the same claims logic runs as Spring Boot microservices. APIs feed a new mobile app. Real-time analytics replace nightly batch reports. The business rules are identical. The platform underneath is entirely different. For organizations evaluating legacy application modernization services or software modernization services, the claims processing example is representative of what modernization programs look like across industries.
That’s actually the strongest use case. RGEN reads the source code and produces the documentation that either never existed or stopped getting updated ten years ago. Business rules, dependency maps, user stories, technical specs, all generated from what the code does today. For applications where the original dev team left and the system has been patched by rotating contractors ever since, code-first extraction produces more accurate documentation than interviews with people who remember some of what the system does. The documentation becomes the foundation for the modernization plan, the test suite, and the compliance trail.
Rewriting starts from requirements documents and rebuilds everything. The problem is those documents describe version one. They don’t describe the 400 patches, 12 enhancement cycles, and 6 compliance updates that happened since. The rewrite team discovers missing functionality during UAT, which triggers rework. Sanciti AI starts from the code itself. RGEN extracts what the application actually does, including the undocumented behaviors. CodeGen generates the modern version from that extraction. The result preserves all existing functionality because it started from complete evidence rather than incomplete documentation. Enterprises see 50% lower costs compared to traditional rewrites because the extraction eliminates the discovery cycles that make rewrites expensive.
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A generative AI framework that unifies code analysis, generation, testing, and deployment, governed end to end. Enterprise teams reduce development effort by 40% and accelerate time to market by 25%.
The Agentic AI SDLC platform · A V2Soft Company · v2soft.com
© 2026 Sanciti AI LLC. All Rights Reserved. · Secure · Scalable · Smart · LLM-Agnostic
Full-service framework including:
Generates Requirements, Use cases, from code base.
Generates Automation and Performance scripts.
Code vulnerability assessment & Mitigation.
Production support & maintenance, Ticket analysis & reporting, Log monitoring analysis & reporting.
AI-Powered Legacy Modernization That
Accelerates, Secures, and Scales
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