New features take longer, integrations feel risky, and the architecture can’t support where the product needs to go? Relevant modernizes legacy systems up to 50% faster with AI, and senior engineers review every change to keep quality, security, and stability intact. The work happens in production, phase by phase, with no full rewrite.
We move your infrastructure from on-premise to cloud safely and predictably, backed by more than a decade of engineering experience.
Relevant redesigns legacy architecture into a structure that supports your current scale, integrations, and growth roadmap.
We refactor the codebase itself, cutting technical debt and turning aging code into a maintainable foundation your team can move fast on.
Our team migrates data, integrations, and user-facing functionality without disruption. The business keeps running through modernization.
We rebuild the parts that hold scale back, so the modernized system performs under the load your customer base now puts on it.
We modernize how the system is maintained and replace manual processes with the test, monitoring, and CI/CD layers that legacy systems often lack.
Full assessment of your legacy codebase: structure, dependencies, data models, integrations, performance bottlenecks, and the maintenance burden your team absorbs every sprint.
Audit findings turn into a phased modernization plan: which subsystems get rebuilt first, which stay stable, what the cutover sequence looks like, and where business risk sits. You see milestones, dependencies, and a phased budget before any redesign begins.
Our senior engineers define the target architecture, including service boundaries, data flows, integration patterns, and infrastructure choices. Then, they pressure-test it against real workload assumptions before code is written.
We refactor the codebase against the new architecture and migrate data, integrations, and functionality in coordinated waves, delivering up to 50% faster cycle times and up to 20% fewer bugs than traditional approaches.
Users, data, and traffic migrate to the modernized system in stages, with rollback paths and monitoring at every checkpoint. Each phase is gated by acceptance criteria and regression tests. The legacy system stays live until the modernized one is proven under real load.
You have a new architecture, a refactored codebase, migrated data and integrations, and the test, CI/CD, and monitoring layers that keep the system maintainable. Your team can move forward on the modernized system, or continue with the Relevant team through an ongoing AI engineering retainer.
13+ years of building production software means our team knows how to work in undocumented, patched, and decades-old codebases and modernize them without losing what already works.
Our team has delivered 246 projects across industries and stacks, including numerous legacy modernization engagements. The patterns we recognize in your codebase are the ones we have already refactored, migrated, and shipped before.
Your system stays in production, your data stays where it is. We restructure the parts that hold growth back without putting the rest at risk. A rewrite is recommended only when the audit shows that refactoring won’t get you there.
GDPR, HIPAA, and SOC 2: we identify which apply during the audit and integrate them into the modernized architecture.
Migration and cutover calls need experience. You work with architects and tech leads who’ve made them on production systems before.
With 96% retention, the engineers who audit your system, redesign the architecture, and execute the migration are the ones who stay. No context lost mid-engagement.
At Relevant, legacy modernization is an engineering engagement that refactors and modernizes an existing production system, its architecture, codebase, data layer, and integrations, while keeping it operational throughout the work. Senior engineers deliver the modernization using AI-assisted analysis and refactoring tools to accelerate audit, refactor, and migration cycles.
Our goal is to reduce technical debt, improve scalability and performance, and restore the delivery velocity your legacy system is currently absorbing. As a result, you get a modernized architecture, a refactored codebase, migrated data and integrations, and the test, CI/CD, and monitoring layers required to keep the system maintainable.
We treat data migration as a separate workstream from code refactoring, with its own architecture, tooling, and verification process. For systems with strict consistency requirements, we use dual-write patterns, change data capture, or shadow databases, depending on which approach best aligns with the system’s actual consistency guarantees and downtime tolerance.
Every migration phase is verified against three checks: data integrity comparison between legacy and modernized stores, replay of recent production transactions through the modernized data layer, and audit trail validation against the original system’s records. The migration is committed only when all three pass. If any check fails, the migration phase is rolled back, and the issue is resolved before the next attempt.
Our team has worked across legacy stacks including older Java and .NET versions, classic ASP, deprecated PHP and Ruby frameworks, Perl, and uncommon database systems. The modernization approach for deeply outdated stacks is different from modernizing a current-but-aging system: we typically use a strangler-pattern migration, where new functionality is built in the target stack and traffic is incrementally routed away from the legacy system, rather than refactoring legacy code that no one will maintain afterward.
Our team uses three techniques in parallel:
We document hidden business as it is discovered, and the modernized system preserves the behavior even when the code structure changes. Critical paths receive characterization tests — automated tests that capture the legacy system’s exact behavior before any refactoring touches the code. The tests then verify that the modernized version behaves identically.
The audit explicitly reports the current state of test coverage, and the modernization plan includes a test buildout track that runs in parallel with the refactoring work.
For critical paths in systems with low or no coverage, we develop characterization tests that mimic the legacy system’s behavior before refactoring begins. The full test suite, including unit, integration, and end-to-end, becomes part of the deliverable, sized to give your team confidence in future changes.
We modernize incrementally. Before touching a critical flow, we wrap it in characterization tests that capture how the system behaves today, so we can prove the modernized version behaves the same. From there, every change ships through safeguards that keep you in full control at each step:
Yes, and for most clients, that is non-negotiable. We separate the work into parallel tracks so the business keeps moving while the foundation improves underneath it. One track stabilizes core architecture, another raises test coverage to make future changes safe, and a third keeps business-critical feature development going wherever the code allows it.
We decide during the audit, module by module, because the right answer is rarely the same across a whole system. Each part is weighed on four factors: the business value it carries, the risk it poses today, its maintainability, and the cost of each path. The decision follows the evidence:
AI does the time-consuming analysis; senior engineers make the decisions. On a legacy system, the slowest part is understanding what the code does, what depends on what, and what breaks if you change it. AI accelerates exactly that work, which is how we compress an audit from months to weeks.
Specifically, AI supports:
It depends on system size, risk, test coverage, integrations, and data complexity. We begin with an audit that takes a week or more, using AI-assisted analysis to map the system and produce a realistic modernization roadmap with scope, priorities, timeline, and risk areas.
Logging, metrics, tracing, and alerting flow into whichever tools your team already uses — Sonar Cube, Snyk, Datadog, New Relic, Grafana, ELK, custom — and incident response runbooks are updated to reflect the modernized system’s failure modes during the rollout phase.
The modernized system also inherits whatever deployment, access control, and change management processes your operations and security teams require. We follow your existing playbooks, so the team responsible for the system after modernization doesn’t have to learn new tooling alongside the new architecture.
Relevant’s AI Delivery Framework™ applies AI at each stage of modernization, with a clear role: dependency mapping during the audit, architecture stress testing, characterization test generation during refactoring, and data mapping during migration. At the same time, our senior engineers make a decision, while the framework gives them better information to make it with.
We have a 4.9 Clutch rating based on third-party validation from verified clients who highlight transparency, communication, and delivery quality.
Book a call with our experts to discuss your maintenance load, technical debt, and the parts of your system that are holding back your roadmap. Get a modernization plan that frees engineering capacity for new work.
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