Microsoft Solutions Partner
Agents that act, with guardrails
AI-Powered Application Modernization Services
Modernize legacy applications into cloud-ready, AI-enabled systems with AI-assisted discovery, conversion, testing, and practical intelligence across your existing technology environment.
AI application modernization across Microsoft Azure, AWS, Google Cloud, hybrid platforms, and open-source AI frameworks.
years legacy modernization
developers
Microsoft Azure, AWS, Google Cloud & open-source AI
.NET, Java, DevOps & cloud-native engineering
Built across the major agent ecosystems
Copilot Studio
Azure AI Foundry
AWS Bedrock
Google Vertex AI
OpenAI
LangChain / LangGraph
Legacy Applications Holding You Back?
01
Legacy applications that nobody understands and nobody can maintain?
02
Modernization project estimated at 18 months — can’t wait that long?
03
Users need better search, reporting, or workflow access than the legacy app can provide?
Competitors are adding AI-enabled workflows while your legacy applications are difficult to extend?
What We Do
01
Reduce modernization effort with AI-assisted delivery
Use AI-assisted analysis, planning, documentation, conversion, and test generation where your application and architecture support it.
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Understand undocumented code
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Test before you touch
AI can accelerate test creation, but engineers validate coverage before the tests become part of the modernization safety net.
Add intelligence to the result
Introduce natural-language search, document processing, predictive analytics, and workflow automation where valuable.
05
Replace manual with intelligent
06
Cloud-native on the right platform
Run modernized applications across Azure, AWS, Google Cloud, Kubernetes, containers, or hybrid infrastructure with operational capabilities built in.
Our Services & Capabilities
01
AI Modernization Assessment
Assess legacy complexity, AI acceleration opportunities, and modernization priorities to create a practical roadmap with realistic timelines.
- Legacy code analysis
- AI acceleration estimate
- AI feature opportunity map
02
AI-Assisted Code Analysis & Documentation
Analyze legacy code, map dependencies, document application logic, and support modernization decisions through AI-assisted engineering review.
- AI code analysis
- Dependency mapping
- Auto-documentation
03
AI-Accelerated Application Conversion
Modernize legacy applications to current frameworks using AI-assisted conversion where technically suitable, followed by validation and testing.
- NET Framework → .NET 8+
- Web Forms → Blazor
- AI-accelerated conversion
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AI-Generated Test Suites
Generate unit and integration tests with AI, then validate coverage through engineering review before modernization begins.
- AI test generation
- Unit + integration tests
- Regression safety net
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Azure OpenAI Integration (RAG, Q&A, Chat)
Add RAG, natural-language Q&A, and conversational experiences using the AI stack suited to your platform, data, and security requirements.
- RAG
- Natural-language Q&A
- Conversational UI
- Cloud or open-source AI
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Intelligent Document Processing
Automate document extraction, classification, and routing for invoices, contracts, forms, and correspondence.
- Document extraction
- Classification
- Routing
- Data capture
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Copilot Extensibility
Expose modernized application data and actions through approved Copilot integration patterns within supported Microsoft environments.
- Copilot plugin
- Graph connector
- Copilot agent
- M365 integration
What Does Your Legacy App Need?
| Your situation | AI modernization approach | AI features to add |
|---|---|---|
Legacy .NET Framework, unmaintainable | AI-accelerated .NET 8+ conversion | Natural-language search, Copilot plugin |
ASP.NET Web Forms, outdated UX | AI-assisted Blazor conversion | Conversational UI, smart forms |
Undocumented codebase, nobody knows it | AI code analysis + documentation first | Documentation as foundation |
Manual document processing workflows | Modernize + add Document Intelligence | AI extraction, classification, routing |
App needs intelligence, not just a rewrite | Modernize + embed Azure OpenAI | RAG, Q&A, predictive analytics |
App data trapped — users can’t search it | Modernize + add AI search | Azure OpenAI search + embeddings |
Competitors have AI — we don’t | Modernize with AI features built in | Full AI feature set |
Modernization too slow traditionally | AI-accelerated approach | Speed + AI features |
Modernize legacy applications with AI-assisted engineering, then add practical AI features using the cloud or open-source stack that fits your architecture, security, and budget.
Our AI-Powered Modernization Methodology
Free assessment
AI code analysis & documentation
We use AI to analyze the legacy codebase — mapping dependencies, documenting undocumented logic, and producing the technical understanding that enables modernization.
AI-generated test safety net
We generate unit and integration tests using AI — creating the regression safety net before any code is changed.
AI-accelerated conversion
We convert the codebase to modern frameworks and architecture using AI-assisted tools where appropriate, then validate through testing and engineer review.
AI feature integration
We embed AI capabilities into the modernized application using the right stack for the client’s environment: Azure OpenAI, Amazon Bedrock, Google Vertex AI, open-source models, intelligent document processing, enterprise search, Copilot extensibility, and predictive analytics.
Production deployment, CI/CD & operations
We deploy to the target environment with CI/CD pipelines, environment-specific configuration, identity and access controls, monitoring, logging, alerts, backup and recovery planning, and release validation — whether the target is Azure, AWS, Google Cloud, hybrid infrastructure, or a containerized platform. The goal is a production-ready application, not just a migrated codebase.
Launch, adoption & iteration
We launch, measure AI feature usage, and iterate — refining AI capabilities based on real user feedback.
It starts with a free AI modernization assessment. No obligation.
Technology Stack / AI Modernization Framework
Here’s what that looks like in practice:
Dimension 1: AI Accelerates the Modernization Process
AI tools help analyze legacy code, support conversion planning, generate tests, and produce documentation. Actual time savings depend on code quality, dependencies, test coverage, and target architecture.
- AI-assisted code analysis maps dependencies, identifies patterns, and documents legacy logic for engineer review.
- AI-assisted conversion supports framework upgrades, refactoring, and pattern-by-pattern modernization where suitable.
- AI-assisted test creation helps generate unit and integration tests before modernization; engineers validate coverage and quality before relying on them.
- AI-assisted documentation helps capture legacy business logic, dependencies, and technical behavior for engineer review.
Dimension 2: AI Gets Embedded in the Result
The modernized application does more than move to a newer stack. It can add AI capabilities where the workflow, data quality, security model, and business case support them.
- Natural-language search and Q&A can use RAG with Azure OpenAI, Amazon Bedrock, Google Vertex AI, or open-source models depending on platform fit.
- Intelligent document processing extracts, classifies, and routes documents using cloud AI services or open-source tooling where appropriate.
- Copilot extensibility can expose approved application data and actions through Microsoft 365 Copilot when the client’s Microsoft environment supports it.
- Natural-language interfaces can simplify forms, search, and workflows where conversational UX improves user productivity.
AI-Powered vs Traditional Modernization
| Phase | Traditional Approach | AI-Powered Approach |
|---|---|---|
Code understanding | Manual reading, months | AI-assisted analysis with engineer validation |
Documentation | Written manually (often skipped) | AI-generated from code |
Test creation | Written manually (often skipped) | AI-generated safety net |
Code conversion | Line-by-line rewrite | AI-assisted, pattern-by-pattern |
Result | Modern code, same features | Modern code + AI features |
Timeline | 6–18 months typically | Potential effort reduction after assessment |
Risk | High (untested, undocumented) | Lower risk with documentation and test coverage |
Case Studies
Leading wedding planning business in Australia
Disconnected data sources, manual handling, and no scalable way to qualify leads
A fully integrated AI concierge powered by LangChain, OpenAI’s o3 model, and Azure Cloud
Insurance company, claims processing application.
Claims documents manually processed — data entry, classification, and routing done by a 6-person team.
Modernized the claims app to .NET 8 on Azure, and embedded Azure Document Intelligence for automated extraction, classification, and routing.
Technology company, internal project management tool on ASP.NET Web Forms.
Outdated Web Forms app that nobody wanted to use — and the CTO wanted it accessible from Copilot.
AI-accelerated conversion to Blazor. Built a Copilot plugin so project data is queryable from M365 Copilot
Industries We Serve
Financial Services
Insurance
Manufacturing
Operational applications with AI analytics
Healthcare
Professional Services
Technology
Retail
Government
Citizen-facing applications with AI accessibility
From a single legacy app modernization to an enterprise portfolio programme with AI features across every application.
Insights
Freshness / 2026 Positioning
In 2026, application modernization is no longer just a rewrite or migration exercise. For many legacy systems, AI can help teams understand undocumented code faster, strengthen test coverage, improve documentation, and identify where intelligent features such as search, document processing, analytics, workflow automation, and Copilot access can create business value. The right AI stack may be Microsoft, AWS, Google Cloud, open source, or a combination. The goal is not simply a newer application; it is a more maintainable, secure, and useful product.
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AI can reduce discovery — documentation, and test-generation effort when the codebase and architecture are suitable.
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Should we modernize first and add AI later, or do both together?
Often together, if architecture, budget, and risk allow it. For large portfolios, start with the highest-value applications and add AI features where the use case is clear.
Frequently Asked Questions
What is AI-powered application modernization?
Two things: (1) Using AI tools and frameworks to accelerate the modernization process — code analysis, conversion support, testing, and documentation. (2) Embedding AI capabilities into the modernized application using the right stack for the client’s environment, which may include Azure OpenAI, Amazon Bedrock, Google Vertex AI, open-source models, intelligent document processing, Copilot extensibility, enterprise search, or predictive analytics.
How much faster is AI-accelerated modernization?
AI can reduce discovery, documentation, testing, and conversion effort, but results vary by code quality, dependencies, test coverage, integrations, and target architecture. The assessment confirms realistic effort and timeline.
Can AI really understand our undocumented legacy code?
AI can accelerate legacy code understanding by identifying code patterns, dependencies, business rules, and documentation gaps. Star Knowledge uses tools such as GitHub Copilot, cloud AI services, and open-source AI frameworks as accelerators, then validates the output through engineer review, stakeholder walkthroughs, test coverage, and risk analysis before conversion begins.
What AI features can you add to our modernized application?
What is RAG (Retrieval-Augmented Generation)?
RAG combines a language model with approved application data, documents, or knowledge sources so users can ask questions and receive answers grounded in the organization’s own information. It can be implemented with Azure OpenAI, Amazon Bedrock, Google Vertex AI, open-source models, vector databases, and governed retrieval pipelines depending on the client’s platform and security requirements.
How much does AI-powered modernization cost?
Cost depends on codebase size, target platform, integration complexity, testing needs, security requirements, and AI feature scope. The pricing calculator should use the approved $400–$600 per delivery-day range, and final pricing should be confirmed after assessment. Keep the calculator, FAQ, offer schema, and any estimate bands consistent before publishing.
What legacy technologies can you modernize?
.NET Framework (any version), ASP.NET Web Forms, classic ASP, VB6, Java (to .NET or modern Java), and COBOL/mainframe (with honest scoping). The most common is .NET Framework → .NET 8+ with Blazor frontend.
What is Copilot extensibility?
Making your application’s data and actions available through M365 Copilot — as a plugin, Graph connector, or Copilot agent. Users can query your application’s data and trigger actions from Copilot, without opening the application directly.
Book My Free AI Modernization Assessment
Tell us the task you have in mind. We’ll assess whether an agent suits it, at what autonomy level, and what the first step should be.
- No cost, no obligation
- Typically responds within 4 business hours
- Confidential — your data isn't shared
Typically responds within 8 business hours · Confidential