AI Code Modernization: 6 Companies and Their Services

Old code carries decisions that nobody remembers making. Before a team can replace it, someone must recover those decisions and establish which behavior the business still needs.

AI code modernization can assist with that work, from analyzing dependencies to generating replacement code. The scope varies by provider, as does responsibility for testing and delivery.

What does AI code modernization involve?

AI code modernization uses AI to analyze and update existing software, including its code, frameworks, dependencies and architecture. Engineers define the intended changes and check the results against business and technical requirements.

Common tasks include:

• Recovering business rules from code with incomplete documentation
• Updating technologies, including libraries, frameworks and language versions
• Refactoring or translating code into a different structure or language
• Supporting verification through test generation and comparison of application behavior

These tasks can form part of a wider delivery process. For example, Software Mind Shift combines AI agents with engineering work across application analysis, code changes and testing.

Improve code structure and verify behavior

Moving an application to the cloud does not automatically make its code easier to maintain. Translating COBOL into Java can leave the same tangled dependencies in place. If the goal is easier maintenance, the project must address how the code is organized and how its components depend on one another.

AI can help recover knowledge, but generated documentation still needs checking against the application and domain experts. An overlooked exception can become an error in the new implementation. Teams need evidence that the changed system follows agreed business rules and stays within its operating limits.

6 companies offering AI code modernization services and tools

The companies below provide engineering services, coding tools, or a combination of both. Their offerings cover different parts of modernization, so the engagement must specify who handles assessment, code changes, validation and deployment.

Software Mind

Software Mind combines assessment and planning with application modernization. Its approach uses specialized AI agents alongside engineers who review outputs and validate changes.

• Software Mind Blueprint supports discovery and modernization planning. Its dedicated offering analyzes database structures, SQL, ETL artifacts and dependencies to produce assessments and migration plans.
• Software Mind Shift supports application code transformation and testing, including work with legacy backend and frontend technologies. Engineers oversee generated code and review critical changes.

In an extended proof of concept for Cotality, Software Mind reported 55% less human delivery effort while modernizing 73 COBOL services to Java/Spring Boot. The workflow compared new and original service responses and execution times. The effort figure describes that project; it does not establish a standard saving for every engagement.

Svitla Systems

Svitla Systems provides software engineering, application modernization, cloud and QA services. Its Australian offering, delivered with subsidiary Kiandra, explicitly uses AI-assisted analysis to map existing codebases before making changes.

That offering combines discovery with staged delivery and continuing application support. Svitla also describes using AI-assisted analysis to understand applications originally built by another vendor before taking over their support.

Engagement options include team extension, managed services and project-based delivery. The documented AI role here centers on system understanding and discovery within a broader engineering engagement.

Sombra

Sombra’s application modernization services cover system assessment, phased development and ongoing maintenance. Its teams review existing architecture, identify risks and dependencies and prepare a roadmap with proposed changes, timelines and budget estimates.

AI supports dependency mapping, business-rule extraction, code generation, refactoring and migration scripting. Depending on the assessment, implementation can involve restructuring existing code, moving it to another platform, or rebuilding selected components. Ongoing maintenance includes updates and monitoring, with AI used to detect anomalies, triage issues and scan for vulnerabilities.

TIAC

TIAC offers AI-assisted modernization through an engineering delivery model that combines development, testing and documentation with human review. Its workflows include approval requirements, quality checks and reusable project knowledge.

Through CAIT, its AI engineering framework, TIAC incorporates specialized agents and shared knowledge into managed development teams. Engineers oversee technical decisions and review generated outputs. The delivery model includes traceability and governance controls, while clients retain ownership of source code and intellectual property and control over engineering approvals and security requirements.

Anthropic

Anthropic develops Claude and Claude Code, tools that engineering teams can use for codebase analysis and modification. Claude Code reads repositories, edits files, runs commands and connects with development tools.

Anthropic’s modernization guidance covers recovering business logic, generating tests and migrating legacy languages. Its materials address COBOL-to-Java migration and Java version upgrades, covering both language conversion and updates within an existing technology stack.

The relevant offering is agentic coding technology used by internal teams and service providers. A Claude Code deployment still requires project ownership, migration requirements, validation and release decisions from the delivery team.

Mistral AI

Mistral AI provides Mistral Vibe and coding models, including Devstral and Codestral. Vibe supports changes across multiple files, refactoring, test generation, documentation, dependency updates and legacy migration workflows.

The company also offers enterprise code modernization services, alongside model customization for proprietary languages and internal frameworks. Its enterprise offering includes custom deployments, model training and dedicated support.

What to verify before starting a modernization project

Comparing AI code modernization services requires a defined application scope. Map each provider’s capabilities to the actual codebase, dependencies and acceptance criteria before committing to a wider migration.

Establish what must survive the change

Write down the behavior the new implementation must preserve, including:

• Business rules, calculations and rounding behavior
• Interfaces, data formats and error responses
• Transaction boundaries, retries and failed integrations
• Operating limits, including response times and batch deadlines

Generated tests need review against these requirements. Tests derived only from translated code can repeat the same misunderstanding that produced the code.

Measure the whole delivery effort

Ask whether an efficiency claim includes:

• Discovery and preparation
• Code review and corrections
• Integration and regression testing
• Deployment, monitoring and handover

Reducing code generation time affects only part of the work. Compare results against an agreed baseline and include the effort needed to produce an accepted change.

Define control over code and releases

Agree on the controls before connecting AI tools to the project:

• Where source code and intermediate artifacts are processed
• Who can access repositories, models and generated output
• Which changes require human approval
• How releases are checked, deployed and reversed if necessary

Start with a defined modernization scope

Begin with a representative component and its real dependencies. Include a difficult integration or poorly documented rule, then measure engineering effort, functional correctness and operational performance.

Use the pilot to establish what the next stage will require. Record unresolved dependencies, review effort and deployment constraints so the wider plan reflects work the team has actually tested.

FAQ

What is AI code modernization?

AI code modernization uses AI to analyze and update existing software, including code, dependencies and architecture. Engineers define requirements, review changes and verify application behavior.

What tasks can AI handle during a code modernization project?

AI can map dependencies, extract business rules, generate documentation, refactor or translate code and create tests. Engineers review outputs against the application and agreed requirements.

How is AI code modernization different from traditional modernization?

AI code modernization adds AI-assisted analysis, code changes and test generation to established engineering practices. Teams still define requirements, validate behavior and manage releases.

Does AI code modernization guarantee the same business behavior after migration?

No. Preserving behavior requires evidence: documented business rules, comparison with the original system, regression tests and validation of integrations, failure handling and performance.

What should I check before starting an AI code modernization project?

Check application scope, dependencies, business rules and acceptance criteria. Agree on data handling, human approvals, rollback procedures and how total delivery effort will be measured.