Solutions

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Cohort Labs develops automation and intelligence products purpose-built for Application Modernization. Our solutions are designed to accelerate and de-risk legacy transformation initiatives across large IT enterprises, enabling systems integrators, managed service providers (MSPs), and internal IT teams to deliver faster, more predictable outcomes.

Know what you have. Understand what it takes.
Our AI-driven discovery tools automatically map application portfolios, dependencies, technology stacks, and modernization readiness, providing an actionable, risk-prioritized view of your entire legacy landscape. Whether you're migrating to containers, refactoring monoliths, or addressing tech debt, our platform offers the clarity you need to move forward with confidence.

Smarter refactoring. Less manual effort.
We combine static code analysis with AI models trained on real-world modernization patterns to surface anti-patterns, recommend architectural improvements, and generate refactored scaffolding code. Support includes .NET, Java, Python, and common enterprise architectures, enabling partners and clients to transition to cloud-native and microservices models with precision and speed.

Intelligent recommendations from assessment to execution.
At the core of our platform is an orchestration layer that uses AI to align modernization recommendations with business goals, cost drivers, and technology strategy. Whether optimizing for scalability, maintainability, or AI-readiness, the engine adapts to each enterprise’s context—automating where it can and guiding where it must.

Amplify your modernization services.
Cohort Labs enables service providers to productize modernization offerings with repeatable IP and AI-assisted delivery. Whether you're delivering modernization assessments, cloud migration, or application replatforming, our tools scale your teams and differentiate your value.

• Legacy system modernization (.NET, Java, LAMP, monoliths) 
• Cloud migrations to Azure, AWS, GCP 
• Containerization and Kubernetes adoption 
• Application replatforming and refactoring 
• Tech debt identification and reduction 
• Roadmaps to AI-readiness and data modernization
   

• Purpose-built for modernization, not general DevOps
• Partner-first design—enablement, not replacement
• Designed to reduce cost, risk, and lead time
• AI-native tools aligned to real enterprise constraints