Old code drains money. It chokes feature development. It turns security fixes into nightmares. And it locks your best people into the past.
Most companies run their business on millions of lines of this stuff. Critical systems. Customer data. Revenue streams. All sitting on code written years ago, sometimes decades.
You could rewrite everything. But that’s a fantasy. Too costly. Too time-consuming. Too dangerous.
AI offers a smarter route. It reads through messy codebases at machine speed. It spots patterns humans miss. It handles the tedious refactoring work. And it does this in small steps, not giant leaps.
The AI software engineering companies listed here do exactly that. They use AI-assisted software development to analyze old code, transform it systematically, and modernize without blowing everything up.
How We Selected These Companies
Legacy modernization is different from greenfield development. It requires specific capabilities. Here’s what we looked for:
- AI-powered code analysis. The company must be able to scan large codebases and identify dependencies, dead code, security vulnerabilities, and architectural patterns. Manual discovery takes months. AI does it in days.
- Automated refactoring capabilities. The provider must have tools that transform old code into modern frameworks. Not just translation. Real refactoring that preserves business logic while improving structure.
- Proven results. We looked for documented case studies with specific metrics. Time reduction. Cost savings. Code quality improvements. No vague claims about “transformation.”
- Human oversight. AI generates suggestions. Humans make decisions. The best companies keep engineers in the loop, reviewing AI outputs and making final calls.
- Enterprise experience. Legacy systems are mission-critical. The provider must have experience with large, complex, regulated environments.
These criteria separate genuine modernization partners from vendors selling basic code translation tools.
1. N-iX
N-iX doesn’t overcomplicate legacy work. They get in, figure out what’s there, and start improving things.
First comes the analysis. AI scans sprawling codebases that have lost their documentation years ago. It maps dependencies. It flags problem areas. It shows where to start.
Then comes the refactoring. Automated tools clean up technical debt piece by piece. Production stays online the whole time. Everything keeps running while the improvements happen.
The transformation happens gradually. Applications shift toward modern architectures one module at a time. Small steps. Safe steps. Measurable progress.
Experienced engineers stay involved throughout the process. They review AI suggestions. They catch potential issues early. They make the final decisions on architectural changes. This combination of AI speed and human judgment reduces risk significantly.
N-iX brings serious weight to modernization projects. Twenty-three years in the game. 2,400 engineers on staff. Fortune 500 clients trusting them with mission-critical work.
Finance. Manufacturing. Healthcare. These aren’t simple domains. They’re regulated. They’re complex. They don’t tolerate downtime.
When you’re comparing AI software engineering companies, you want a partner that’s been around. That has the depth. That’s already proven itself in your industry.
N-iX checks all those boxes.
Modernization approach:
- Scans legacy codebases to map dependencies and identify technical debt
- Runs automated refactoring on isolated modules with human oversight
- Transforms monoliths into modular architectures incrementally
- Tracks modernization progress with specific metrics
- Transfers knowledge to internal teams to avoid long-term dependency
Bottom line: You get AI speed with human judgment. No full rewrites. Just steady, measured progress.
2. EPAM
EPAM runs controlled modernization pilots that show exactly what’s possible before full implementation. In one engagement with a financial services firm, EPAM improved engineering efficiency by 20% while achieving 98% code completeness and 99% fidelity in legacy system modernization.
The company uses a three-phase pipeline for legacy analysis. It scans configuration files to map external dependencies. It identifies container boundaries. It analyzes component structure inside each container.
EPAM’s approach to legacy modernization is incremental. Teams isolate critical components. They transform them into independent microservices. They use AI to automate decoding of old code streams. This avoids the “big bang” rewrite that creates massive risk.
The company has 42,805 custom software development FTEs and over 1,800 certifications across major cloud providers. EPAM has deep experience in finance, insurance, and healthcare modernization. Their AI software development services cover the entire modernization lifecycle from analysis to deployment.
Modernization approach:
- Runs side-by-side AI pilots to quantify modernization gains
- Incrementally transforms monoliths into microservices
- Automates code decoding for faster migration
- Maintains backward compatibility throughout the process
- Provides continuous validation to ensure functional accuracy
Bottom line: You know exactly what you’re getting before you commit. Controlled pilots prove the value first.
3. Thoughtworks
Thoughtworks launched AI/works in early 2026. It’s an agentic development platform built specifically for legacy modernization. The platform uses AI-enabled reverse engineering to interpret legacy applications and convert them into structured specifications. Agentic workflows then generate production-grade code, automated tests, and deployment pipelines.
The platform supports Thoughtworks’ 3-3-3 delivery model. Three weeks to discover. Three weeks to build. Three weeks to deploy. That’s 90 days from idea to production.
What makes AI/works different is its ongoing approach. Once deployed, the platform regenerates affected components as requirements evolve. This reduces reliance on manual patching and avoids large-scale rebuilds.
Clients report something striking. Modernization cycles that dragged on for years now finish in months.
The platform fits into any cloud setup. AWS. Google Cloud. Azure. Databricks. Snowflake. It even handles mainframes through a partnership with Mechanical Orchard.
For enterprises with hybrid environments, Thoughtworks stands out among AI-powered software development companies that deliver large-scale transformation.
Modernization approach:
- Reverse-engineers legacy code into structured specifications
- Automates code generation, testing, and deployment pipelines
- Continuously regenerates components as requirements change
- Works with hybrid technology estates including mainframes
- Delivers modernization in 90-day cycles
Bottom line: A co-innovation program lets you test AI/works on your actual systems before committing to broader adoption.
4. SoftServe
SoftServe’s Agentic Engineering Suite tackles legacy modernization through specialized AI agents. The suite contains agents for code analysis, automated refactoring, migration strategy, and quality assurance. These agents learn from existing application landscapes and autonomously modernize legacy systems.
SoftServe is a strategic launch partner for AWS Transform for .NET. The company modernized an application with 20,000 lines of code in just 18 minutes. That process previously took an entire week. For larger applications with hundreds of thousands of lines of code, transformation was achieved up to four times faster.
Clients have reduced modernization costs by up to 50% using SoftServe’s approach. The company uses its SoftServe Adaptive Modernization Platform (SAMP) as an accelerator for container adoption and cloud migration.
SoftServe’s GenAI Lab has created over 200 AI-based solutions for more than a hundred clients, including Cisco and Dell. The company reports 85% year-over-year growth in AI-powered software development services. This makes SoftServe a strong contender for enterprises with large-scale modernization needs.
Modernization approach:
- Uses specialized AI agents for each modernization phase
- Reduces manual code conversion effort by up to 50%
- Modernizes applications through agentic automation
- Accelerates container adoption with proprietary platform
- Cuts modernization costs by up to 50%
Bottom line: You get purpose-built AI agents for modernization, not generic code-generation tools.
5. GlobalLogic
GlobalLogic modernized legacy .NET services for a major pharmaceutical company. The organization relied on outdated .NET web services for critical patient and healthcare provider systems. Manual code conversion and fragmented documentation stretched timelines and introduced risk.
GlobalLogic embedded GenAI-driven automation directly into the modernization lifecycle. AI auto-generated approximately 50% of base code during the .NET to Java Spring Boot conversion. The team automated JUnit test generation. They recreated missing documentation through reverse engineering.
The numbers speak for themselves. Delivery time? 11 months down to 6. That’s a 44% acceleration.
Cost savings hit 68%. Automation did most of the heavy lifting. Cloud consolidation helped. Infrastructure optimization finished the job.
JUnit test creation? Up to 70% faster.
GlobalLogic also built an AI-native SDLC for a leading ERP software company. The client transitioned to an operating model of 80% AI execution and 20% human strategy and oversight. This demonstrates GlobalLogic’s strength in providing end-to-end AI development lifecycle services for large enterprises.
Modernization approach:
- Auto-generates base code during language conversion
- Automates test generation through AI-driven automation
- Recreates missing documentation via reverse engineering
- Tracks specific metrics: 44% faster delivery, 68% cost savings
- Transitions teams to AI-native development models
Bottom line: Specific, documented improvements across code conversion, testing, and documentation.
6. Intellias
Intellias brings AI tools to legacy work. They analyze old codebases. They translate outdated languages into modern ones. They tune performance for the cloud.
Their style is hybrid. Old systems stay running. New ones get built alongside. API gateways connect them. Integration layers handle the communication. Stability stays intact while scalability improves.
The AI toolkit handles the heavy lifting. Mapping dependencies. Orchestrating workloads. Syncing data across systems. Spotting performance issues before they cause problems.
Some clients cut costs by 70%. Others hit the market 1.5X faster.
Intellias has 3,000+ people and 20 years of experience. They know automotive. They know finance. They know telecom. Their AI engineering consulting practice helps clients figure out where to start and how to move forward without breaking things.
Modernization approach:
- Analyzes legacy codebases with AI-assisted tools
- Translates code to modern languages and frameworks
- Optimizes performance for cloud architectures
- Uses a hybrid approach to balance stability and innovation
- Maps dependencies and orchestrates workloads
Bottom line: You modernize gradually. Keep old systems running while building new ones alongside.
The Cost of Legacy Code
Legacy systems are expensive. Here’s what they cost you.
- Money. Maintaining outdated systems consumes up to 70% of enterprise IT budgets. That’s money that could go to innovation.
- Time. Teams spend more time fixing old code than building new features. Development slows to a crawl.
- Talent. Developers don’t want to work with obsolete technologies. They leave for companies using modern stacks.
- Risk. Outdated systems have security vulnerabilities. Compliance becomes harder. Incidents become more frequent.
- Speed. You can’t respond to market changes when your codebase is a tangled mess. Competitors move faster. They win.
Legacy modernization is survival. The question isn’t whether to modernize, but how to do it without breaking everything.
AI helps with this.
It analyzes code faster. It suggests refactoring options. It generates tests. It handles the tedious parts. But AI isn’t a silver bullet. You still need experienced engineers making decisions. You still need careful planning. You still need to measure progress.
The companies in this list understand this. They don’t promise magic. They provide tools, frameworks, and experienced teams that make modernization faster, cheaper, and safer. Many of these firms also offer AI development consulting to help organizations navigate the complex modernization landscape.
Bottom Line
Legacy modernization is a balancing act. You need to move faster. You need to reduce costs. You need to improve security. But you can’t afford to break everything.
AI helps you navigate this balance. It analyzes code faster than humans. It generates refactoring suggestions. It creates tests. It documents what was previously undocumented.
But AI doesn’t replace engineering judgment or eliminate the need for careful planning. It doesn’t remove risk entirely.
The companies in this list understand this. N-iX uses the APEX framework with baseline metrics. EPAM runs controlled pilots before scaling. Thoughtworks uses AI/works for continuous modernization. SoftServe employs specialized agents. GlobalLogic delivers documented cost and time savings. Intellias focuses on hybrid, gradual modernization.
All of them use AI to make modernization faster, cheaper, and safer. None of them pretend AI can do everything.
Choose a partner with proven methodology, real results, and a commitment to knowledge transfer. That’s how you modernize successfully. The best AI-augmented development company for your project will depend on your specific legacy environment, but all of these firms have demonstrated they can deliver measurable improvements.
