AI Transformation Solutions For Technology Leaders
Make Your Legacy Systems AI-Ready
Planning
Intertech’s software planning & requirement analysis process sets the foundation for the entire software development process.
Architecture & Design
Our software architecture and system design stage lays the groundwork for successful software implementation by providing a clear roadmap for building the system.
Custom Development
Intertech experts help you select languages and implement coding standards and development practices that are well-informed & collaborative when updating or creating new web -based and desktop applications.
Quality Assurance
Intertech brings a comprehensive and integrated approach to software quality assurance (QA) and testing that fosters a commitment to delivering software of the highest quality.
Testing
Each type of test serves a specific purpose in the software development process, contributing to the overall quality and reliability of the software. The choice of tests depends on the project’s requirements, goals, and the nature of the software being developed.
Cloud Migration & Integration
Work with a team that understands cloud migration and cloud integration, as well as application architecture and development, so you get the “cloud full stack” experience from your dev-team.
Is Your Existing Software Ready for AI?
Whether you’re working with legacy .NET or Java applications, tightly coupled enterprise systems, outdated APIs, or disconnected data sources, our senior consultants help identify the architectural changes needed to introduce AI responsibly.
How Intertech Helps Prepare Existing Systems for AI
Evaluate existing applications, reduce tightly coupled dependencies, improve APIs, and introduce flexible integration layers that support new AI capabilities.
Improve access to business data, establish secure integration paths, and prepare information for AI services while protecting existing systems and workflows.
Connect appropriate AI capabilities to existing applications with attention to security, testing, operational reliability, and long-term maintainability.
“Intertech has been an invaluable partner for our business. They have enabled us to implement automation in our finance business that is seldom present in organizations 10 times our size. They are responsive, innovative and absolutely committed to their customer’s success. You can frequently find vendors that meet your needs, but with Intertech, we have found a strategic partner who is just as committed to our success as we are.“
Chief Technology Officer | Microf
Frequently Asked Questions About Preparing Legacy Systems for AI
Can we integrate AI into our existing software without replacing it?
Intertech helps organizations evaluate these factors and identify a practical modernization strategy before making major replacement decisions.
Can older .NET and Java applications support modern AI capabilities?
A technical assessment can identify the most appropriate integration options and areas that may require modernization.
Does our organization need to move to the cloud before using AI?
The right approach depends on your existing systems, security and compliance requirements, data location, performance needs, and business objectives.
How do we know whether our architecture is ready for AI?
Intertech can help identify architectural limitations, prioritize necessary improvements, and develop a modernization roadmap aligned with specific AI opportunities.
Can Intertech work with our existing software development team?
The goal is to complement your team’s knowledge of the business while providing additional technical expertise where needed.
Modernize Your Architecture to Support AI — Without Replacing the Systems Your Business Depends On.
Why Legacy Systems Can Prevent AI Adoption
Existing Applications Were Built for Different Requirements
Although these applications may still support critical operations reliably, introducing AI can expose limitations in how they manage data, expose functionality, and communicate with other services.
Common Challenges Organizations Face
- Limited APIs and integration options: Existing applications may not expose the information or functionality needed by AI services.
- Fragmented or inaccessible data:Business information may be spread across disconnected databases, applications, and file systems.
- inconsistent in structure
- Tightly coupled architecture: Changes to one component can introduce unintended consequences elsewhere.
- Security and governance requirements: Sensitive business data must be protected when integrating new AI capabilities.
- Outdated dependencies and documentation: Legacy libraries, unsupported components, and poorly documented systems can increase modernization risk.
- Performance and reliability concerns: New AI workloads and integrations can place additional demands on existing systems.
The Result: AI Projects Become More Complex Than Expected
Organizations may discover that the AI technology itself is not the primary challenge. Instead, existing architecture, data accessibility, integration capabilities, and security requirements determine how effectively AI can be introduced.
Addressing these constraints early can reduce implementation risk, protect business continuity, and create a more practical path to AI adoption.
Intertech’s Approach to AI-Ready Legacy Modernization
Preparing existing software for AI does not necessarily require replacing the underlying application.
Intertech works with technology leaders and engineering teams to evaluate their systems, determine the changes that matter most, and implement a practical modernization roadmap.
We focus on:
- Reducing System Complexity
- Improving Integration Capabilities
- Preparing Data for AI use
- Enabling Scalable Architecture Patterns
Roadmap
- 1. Assess Existing Architecture
Review application structure, system dependencies, integration points, data access, and technical constraints and identify where existing systems can support AI capabilities and where architectural changes may be required.
- 2. Improve APIs and Integration Capabilities
Introduce or improve APIs, service boundaries, and integration layers that allow existing applications to communicate with modern AI services. This can make legacy functionality accessible without unnecessarily rewriting the underlying business logic.
- 3. Prepare Business Data for AI
Evaluate data quality, structure, accessibility, and security requirements and improve the way data is exposed and managed so approved AI services can access relevant information while maintaining appropriate controls.
- 4. Modernize Critical Components Incrementally
Refactor selected components, reduce problematic dependencies, and improve system modularity where the business case justifies the change. Prioritize improvements that support specific AI use cases and minimize disruption to existing operations.
- 5. Integrate, Test, and Support AI Capabilities
Connect appropriate AI services to existing applications and workflows and evaluate security, reliability, performance, data handling, and operational requirements before introducing new capabilities into production environments.
A Practical Path Forward
The objective is not modernization for its own sake. It is to make targeted improvements that support valuable business capabilities while extending the useful life of your existing software.