Top AI Legacy Modernization Companies in the USA for Enterprises

Legacy modernization has changed considerably in the last few years. What was once largely a cloud migration or application re-platforming exercise now sits directly alongside enterprise AI strategy.

That is because most production AI systems eventually need access to the applications, workflows, business rules, and data that already run the organization. If those assets are trapped inside tightly coupled monoliths, unsupported frameworks, mainframes, proprietary databases, or poorly documented integrations, deploying AI at enterprise scale becomes significantly harder.

Research published by Thoughtworks and IDC in 2026 illustrates the gap. Around 90% of large enterprises surveyed were already using AI in application operations, yet only 12% had reached what the research describes as continuous, AI-driven operations.

This is why choosing between legacy modernization companies now requires looking beyond cloud certifications and development headcount. The right partner needs to understand existing enterprise architecture, preserve critical business logic, modernize integrations and data flows, and determine where AI can safely accelerate the work.

Below are some of the companies worth considering for AI-led legacy system modernization in the USA.

What Should You Look for in an AI-Led Legacy Modernization Company?

AI does not eliminate the engineering complexity of modernization. It changes how efficiently parts of that complexity can be handled.

Generative and agentic AI can assist with legacy code comprehension, documentation generation, dependency analysis, business-rule extraction, code conversion, test generation, and migration planning. Several companies now incorporate generative and agentic AI across application discovery, analysis, transformation, testing, and migration workflows.

The underlying architecture decisions still matter. A modernization partner should be able to determine whether an application should be retained, rehosted, replatformed, refactored, rearchitected, rebuilt, or retired.

For this shortlist, we considered depth across AI engineering, application modernization, legacy system integration, cloud architecture, data modernization, enterprise software engineering, and the ability to execute phased transformations without unnecessarily replacing systems that still deliver business value.

Top AI Development Companies for Legacy Software Modernization in the USA

1. Seasia Infotech

Seasia Infotech combines more than two decades of software engineering experience with AI, cloud, data engineering, DevSecOps, and enterprise application development capabilities.

Its relevance to legacy modernization comes from approaching the problem as an engineering transformation rather than simply a migration exercise. Seasia supports application reengineering, microservices adoption, API development, cloud migration, UI modernization, data engineering, workflow automation, and AI integration across existing enterprise environments. Its US practice also positions AI-enabled engineering teams around modernization, platform development, and enterprise transformation.

For systems that cannot safely undergo a complete rewrite, Seasia can use incremental approaches that preserve the existing core while exposing functionality through APIs, middleware, or modern service layers. New cloud-native modules, intelligent workflows, AI agents, and user experiences can then be introduced progressively.

AI also has a role earlier in the modernization lifecycle. Legacy code and system artifacts can be analyzed to recover undocumented business logic, identify dependencies, assist documentation, create test cases, and determine which components should be retained, wrapped, refactored, or replaced. Seasia describes human architectural validation as part of this process rather than treating generated output as production-ready by default.

This makes Seasia particularly relevant for organizations looking for one partner across custom software development, custom enterprise application development, cloud engineering, QA, and AI development services, rather than coordinating multiple vendors across the modernization lifecycle.

Best fit: US businesses that want to modernize incrementally, prepare legacy applications for AI adoption, and retain flexibility across AWS, Azure, GCP, microservices, APIs, and custom enterprise platforms.

2. Thoughtworks

Thoughtworks has a strong history in software engineering, continuous delivery, microservices, and enterprise modernization, and its recent AI strategy extends those practices into agentic software development.

Its AI/works platform is specifically designed for both brownfield modernization and new software engineering. It can analyze existing codebases, reconstruct business logic and specifications, visualize future-state architecture, generate software, evaluate generated outputs against requirements, and maintain traceability throughout the lifecycle.

That combination is particularly useful where an enterprise has large systems with incomplete documentation or substantial institutional knowledge embedded in the code.

Thoughtworks is therefore a strong option for organizations treating modernization as a continuous engineering capability rather than a one-time migration project.

Best fit: Large organizations with sophisticated engineering environments, hybrid estates, mainframes, and long-running modernization requirements.

3. IBM Consulting

IBM remains particularly relevant wherever modernization involves mainframes, hybrid infrastructure, mission-critical enterprise applications, or complex application portfolios.

Its modernization approach covers strategies including rehosting, replatforming, refactoring, rearchitecting, replacing, and incrementally enhancing existing systems. IBM also applies generative AI to code understanding, migration, refactoring, application extension, and modernization planning.

For mainframe-heavy organizations, IBM's ability to work across IBM Z, hybrid cloud environments, Red Hat OpenShift, modern application architectures, and AI provides an obvious advantage. Its consulting portfolio also now uses agentic AI and deterministic application analysis to support application discovery and transformation.

Best fit: Large organizations with mainframe estates, complex hybrid environments, and long-term enterprise transformation programs.

4. Cognizant

Cognizant's modernization portfolio combines application transformation, cloud engineering, AI, automation, and enterprise-scale managed services.

Its application modernization practice supports application-led cloud migration, architecture assessment, technical-debt reduction, and cloud-native transformation. Cognizant also uses platforms such as Skygrade, Flowsource, Neuro AI, and AppLens across different areas of the application lifecycle.

The company increasingly positions AI as part of the modernization engine itself. Its legacy modernization offering uses a Human + AI approach to analyze systems, preserve business functionality, improve traceability, and transition applications toward AI-ready architectures.

This breadth is useful for companies dealing with hundreds or thousands of interconnected applications rather than a single aging product.

Best fit: Large US enterprises that need application portfolio modernization, cloud transformation, AI adoption, and ongoing application management under one delivery model.

5. Infosys

Infosys brings substantial enterprise-scale modernization experience across mainframes, databases, monolithic systems, enterprise applications, APIs, microservices, and cloud infrastructure.

Its application modernization capabilities are supported by Infosys Cobalt and Infosys Topaz. The Infosys Application Modernization Platform uses AI to support migration toward scalable, cloud-ready architectures built around APIs, microservices, and modern development practices.

AI is also being applied to activities such as legacy assessment, business-rule extraction, code explanation, documentation, testing, and integration migration. In April 2026, Infosys also announced a collaboration with OpenAI that includes an early focus on software engineering, legacy modernization, and DevOps automation.

Best fit: Global enterprises undertaking large application-portfolio, mainframe, integration, or cloud modernization programs.

6. Accenture

Accenture approaches application modernization as part of broader enterprise transformation.

Its services cover application portfolio assessment, target architecture definition, cloud modernization, engineering transformation, application operations, and GenAI-enabled software development.

Accenture's GenWizard platform is particularly relevant to AI-enabled modernization. It includes capabilities for reverse engineering existing applications, maintaining an AI-assisted knowledge base, modern engineering, refactoring, re-platforming, and re-architecting systems.

Because of its consulting depth, Accenture tends to make the most sense where technology modernization is connected to larger operating-model, process, ERP, cloud, or business transformation initiatives.

Best fit: Large enterprises executing multi-year transformation programs across multiple business units and technology platforms.

7. ScienceSoft

ScienceSoft is a useful option for businesses that need deep application engineering without engaging a very large global consultancy.

Its modernization capabilities cover application evolution, redesign, reengineering, database migration, cloud migration, platform upgrades, and architectural transformation. The company works with legacy technologies including COBOL, older Java and .NET environments, PowerBuilder, Delphi, C++, and proprietary database platforms.

Its experience across healthcare, financial services, insurance, manufacturing, logistics, retail, and other regulated or operationally complex industries also makes it relevant where modernization must account for compliance requirements.

Best fit: Mid-market and enterprise organizations with older custom applications, mixed technology stacks, and significant compliance or migration requirements.

8. Unified Infotech

Unified Infotech has positioned its modernization practice specifically around AI-assisted, phased application transformation.

Its approach includes application assessment, dependency mapping, legacy system integration, API-first middleware modernization, cloud migration across AWS, Azure and GCP, AI-assisted code refactoring, test automation, microservices, containerization, DevSecOps, and SRE.

The company also emphasizes incremental migration techniques such as the Strangler Fig pattern and API facades, which can reduce the operational risk associated with big-bang rewrites.

Best fit: Mid-market organizations that want a phased modernization roadmap and need application engineering, AI, cloud, and integration capabilities within the same engagement.

Where AI Actually Adds Value to Legacy System Modernization

A common mistake is treating AI integration as the final step: modernize the application first, then add an LLM or chatbot.

In practice, AI can contribute throughout the modernization lifecycle.

During discovery, models can help engineers analyze large codebases, explain unfamiliar modules, extract business rules, reconstruct documentation, and map dependencies. During transformation, AI-assisted development tools can accelerate code conversion, API generation, refactoring, and modernization of repetitive patterns. During validation, AI can support test generation, regression analysis, defect investigation, and documentation.

Once the underlying architecture and data are ready, AI can become part of the application itself through intelligent search, copilots, predictive analytics, AI agents, document intelligence, automated decision support, or workflow orchestration.

The important distinction is governance. Production enterprise software modernization still requires architects and domain specialists to verify business logic, security boundaries, data contracts, generated code, and system behavior.

AI should reduce engineering effort. It should not remove engineering accountability.

Choosing the Right Legacy Modernization Company

There is no universal modernization architecture.

An application that is stable but difficult to integrate may only require an API or orchestration layer. Another might be suitable for re-platforming to managed cloud services. A tightly coupled monolith could require gradual domain decomposition. An unsupported application with severe architectural debt may justify a complete rebuild through custom enterprise application development.

That is why a credible modernization partner should not begin by prescribing microservices, Kubernetes, generative AI, or even cloud.

It should begin by understanding the existing system.

For most organizations, the first engagement should establish the application portfolio, dependencies, business-critical workflows, data architecture, security constraints, integration landscape, technical debt, modernization priorities, and acceptable migration risk. Architecture decisions should follow that assessment.

Modernizing Legacy Software for an AI-First Enterprise

Legacy software does not necessarily need to disappear. It needs to stop limiting what the business can do next.

For US enterprises, that increasingly means building a technology core where old and new systems can coexist during transformation; data can move through governed interfaces; applications can be deployed, tested, and changed more safely; and AI can interact with enterprise processes without creating another layer of technical debt.

That requires more than an AI development Company and more than a conventional application migration provider. It requires software architecture, integration engineering, cloud expertise, data engineering, quality assurance, security, and AI capability working as one modernization discipline.

At Seasia Infotech, we combine these disciplines to help enterprises assess existing technology, modernize high-value systems incrementally, establish AI-ready application and data architectures, and introduce intelligent capabilities without unnecessarily disrupting the platforms already running the business.

Planning a legacy modernization initiative? Start with the architecture, dependencies, and business logic, not the rewrite! Talk to Seasia’s enterprise modernization team about building an AI-ready roadmap around the systems you already have.