CHI Software provides application re-engineering services for legacy applications that require bigger changes than routine maintenance or upgrades. We understand the Run vs. Change challenge and the need to balance operational continuity with transformation: keeping critical business operations stable while progressively modernizing the underlying technology.
We analyze existing business logic, restructure code and architecture, modernize data and integrations, and rebuild high-risk components without replacing the entire system at once.
Our phased approach reduces technical debt, improves maintainability, performance, security, and release velocity while preserving critical functionality and business continuity.
Most re-engineering projects don’t start with a technology wish list. They start with a business problem the current application can no longer solve: a release that takes months instead of days, a market the platform can’t expand into, a maintenance bill that keeps climbing. Three patterns come up again and again.
When the codebase is brittle and tightly coupled, engineers spend most of the week on defensive fixes instead of new features. Every change risks breaking something else, so releases get slower and more cautious. AI assistants make this worse, not better: GitClear’s analysis of 211 million lines of code found an eightfold jump in duplicated code blocks once teams adopted AI coding tools without structured review. Google’s DORA report is blunt about it. AI amplifies whatever foundation you already have, so a team drowning in technical debt just gets more debt, faster.
An aging monolith quietly narrows what the business can do next. You can’t enter a new market because the platform won’t localize. You can’t ship an AI feature because roughly a third of legacy systems can’t connect to modern AI tooling at all. For the CEO, this means a loss of strategic flexibility: the core system is too unwieldy to change course when a competitor makes a preemptive move. And this is not an isolated issue: approximately 96% of organizations still rely on legacy technology solutions in some part of their operations.
Legacy applications incur costs in ways that never appear as a specific line item in the budget. SnapLogic estimated the average annual maintenance cost for legacy systems at $2.9 million, roughly double what it was five years ago. Added to this is operational instability: critical processes residing in a single engineer’s head, manual workarounds, and security gaps that widen as unpatched components age. McKinsey found that companies in the bottom 20% regarding technical debt are 40% more likely to abandon modernization efforts halfway through. Waiting rarely makes any of this cheaper.
Application re-engineering means examining an existing system and reconstructing it in a better form without throwing away what already works. It runs through three stages.
Reverse engineering reads the code and data to recover what the system actually does — which matters most where documentation and original developers are long gone. Restructuring refactors that code, cuts technical debt, and normalizes the database, improving the internals without changing external behavior. Forward engineering then rebuilds the components that need it on modern technology.
Because your business logic is preserved rather than rewritten from scratch, re-engineering usually costs a fraction of a full rewrite and carries far less risk to the running business.
We adapt the scope to the state of your system. Sometimes that means a deep audit; sometimes a targeted rebuild of the two modules causing 80% of the pain. Six services cover most engagements.
Before touching anything, we read the system as it really is. Our engineers analyze architecture, security exposure, and data flow, digging into old code to map how it behaves in production — not how a decade-old spec says it should. Where documentation is missing, we reconstruct the business logic straight from the code. You get a component-by-component plan showing the risk and payoff of fixing, rebuilding, or retiring each part. As a result, the sequencing decision stays yours, made with real information.
This is the core of the work: improving the internal structure of the code without changing what users see. We break rigid, tangled modules into cleaner, independent parts, remove duplication, and add the test coverage that lets a team move fast without fear. Since replacing everything at once is rarely affordable, we help prioritize which components deserve refactoring and which are better off rebuilt. About 48% of companies can’t simply retire their legacy apps because those apps still run the business (Pega), so surgical beats wholesale almost every time.
Monolithic systems hit a ceiling. We redesign the architecture toward modular services that scale and deploy independently, usually applying the Strangler Pattern. As a result, new components come online while the legacy core keeps running. Nothing goes dark during the transition. What you end up with is a system that absorbs traffic spikes, ships features in parallel, and connects to the AI and analytics tools a monolith simply blocks.
Performance issues often start in the data layer, not the application code. We clean up data, rethink schemas, optimize slow queries, and move to databases that better match the workload. For one healthcare client, that meant moving from MariaDB to PostgreSQL to enable real-time synchronization across dozens of institutions. Better data integrity and faster access follow, while the modernized layer becomes the foundation for reporting and analytics the old setup could not support.
Aging runtimes and unsupported libraries are as much a security and hiring problem as a technical one. We upgrade the stack to current, supported versions (.NET 8 and EF Core, modern Java and Spring, current React or Angular on the front end) while preserving the business rules that make your product yours. Paired with CI/CD pipelines and a proper DevOps setup, updates stop being a quarterly event and become a routine, low-drama part of the week.
Some components are beyond refactoring and require a complete overhaul. We carry this out incrementally, avoiding a massive, all-at-once transition. Each redesigned module is deployed under the protection of automated tests and feature toggles, and a rollback plan is agreed upon prior to deployment rather than hastily devised afterward. The highest-risk parts are replaced with modern technologies while the rest of the system continues to serve users.
Teams commit to a full rewrite when a week of refactoring would have done the job, or nurse a doomed system that should have been rebuilt years ago. This is how we draw the lines.
| Approach | Scope | Best For |
|---|---|---|
| Re-engineering | Reworks code, architecture, and data together while preserving proven business logic. | An aging application that still delivers value but blocks scaling, integration, or AI-readiness. |
| Refactoring | Improves the internal structure of existing code; external behavior stays the same. | A system that is fundamentally sound but carries technical debt that slows the team down. |
| Rebuilding | Starts from an empty repository, often with a new data model. | A system beyond economical repair, or one whose original logic can no longer be recovered. |
Most real projects mix all three. A typical engagement refactors the modules worth keeping, re-engineers the architecture around them, and rebuilds only the handful of components that can’t be salvaged. That is exactly why a blanket “rewrite it all” quote is usually the wrong answer.
Choosing a partner for application reengineering is a high-stakes decision for procurement and legal teams. Working with a certified partner like CHI Software eliminates a layer of risk before the project even begins.
Transformation shouldn’t be a gamble. Based on HFS recommendations, we treat application reengineering as a strategic, outcome-based shift rather than a technical “lift-and-shift” chore.
Defining Business and Technical Goals We begin by aligning with your business goals. Whether it is reducing maintenance costs or preparing for big data analytics, we define what success looks like in measurable terms.
System Analysis and Dependency Mapping We map every data flow and system integration point to build a comprehensive blueprint of your digital ecosystem. Understanding how your existing system interacts with external services and internal databases is vital for ensuring minimal disruption during the transition. By identifying these critical links early, we can isolate legacy components for modernization without destabilizing the surrounding infrastructure. API modernization services help improve data exchange between legacy systems, external services, and modern platforms.
Value-Based Prioritization We identify the components of your legacy apps that offer the highest ROI. By refactoring legacy code in stages, we deliver value early and often, funding the broader legacy application re-engineering services through realized savings.
Automated Refactoring and Migration Using modern tools and application reengineering services, we systematically update your technology stack. Our old application re-engineering services focus on preserving your unique business logic while upgrading the underlying outdated code to modern standards.
Real-Time Monitoring and Stabilization Post-migration, we use advanced analytics and performance optimization tools to ensure the application re-engineering solutions we’ve implemented remain stable and perform at peak efficiency in your new cloud environment.
Post-Migration Control and Independence As a dedicated application re-engineering company, we don’t create vendor lock-in. We provide the documentation and training your team needs to manage the modern platforms independently, ensuring you are future-proof and in control.
Investing in application re-engineering services is a defensive and offensive move. According to Pega/Savanta 2025, the average global enterprise wastes more than $370 million every year through technical debt. We help you reclaim that capital.
Cloud-native re-engineering converts fixed infrastructure OPEX into elastic, usage-based expenditure. For CEOs tracking the Rule of 40, this directly strengthens the efficiency component without compromising growth velocity. For CIOs, it eliminates the overhead of maintaining on-premise capacity that no longer aligns with business demand.
Outdated systems are prone to downtime. Our re-engineering approach builds resilience into the architecture, utilizing cloud-native architectures that offer auto-healing capabilities and high availability.
When your developers aren’t fighting legacy code, they are building value. Re-engineering removes the bottlenecks that keep engineering teams in maintenance mode, including tightly coupled components, manual deployment processes, and absent test coverage.
Security risks are the hallmark of outdated applications. We provide security enhancements that protect your legacy data from modern threats, ensuring security compliance across your entire digital transformation journey.
A scalable system is essential for any evolving business. We ensure your architecture can handle surges in traffic and data flow without breaking, supporting your future growth and global expansion.
Slow, clunky user interfaces lead to low user satisfaction. By performing database optimization, refreshing the frontend, and applying UI/UX modernization services, we provide a modern experience that delights users and reduces churn.
Problem: A global US investment firm (trading and portfolio management, named under NDA) ran its business on an internal platform that had aged into a monolith. As data volumes and compliance demands grew, reports loaded slowly and every new integration took weeks.
Solution: This was architecture re-engineering, not a rewrite. A team of 20 specialists migrated the monolithic system to microservices on AWS, upgraded the core to .NET 8 and EF Core, and implemented CI/CD using Jenkins and Terraform, enabling the entire environment to be reproduced directly within the client’s repository. Group Roles and SSO tightened access along the way; and all without interrupting the platform’s trading operations.
Result: Response time improved about 60%, the new architecture now handles roughly five times the concurrent operations — without downtime, deployments run twice as fast, and infrastructure costs fell close to 20%.
Problem: A US healthcare technology company (oncology decision support, under NDA) had a platform that, by its eighth year, could no longer onboard new labs and clinics or absorb larger data volumes on its original monolithic design. We’ve worked with them since 2020.
Solution: Re-engineering targeted architecture and the data layer. A 28-person team split the monolith into microservices on AWS Kubernetes, migrated the database from MariaDB to PostgreSQL for real-time sync, connected external systems through HL7 and REST APIs — using Mirth for the messy healthcare edge cases, and built custom SSO on OAuth 2.0.
Result: Clinical decisions came about 40% faster, redundant lab tests dropped by roughly 30%, the platform held 99.9% uptime — through the migration, and it stayed fully HIPAA- and HL7-compliant, which was enough for the client to expand into new regions.
Problem: A Canadian agriculture and consumer-goods company (named under NDA) had run the same Direct Store Delivery platform for years. The aging .NET stack throttled performance under load, and many distributors were still on older versions. Thus, no new product could be released without breaking them.
Solution: A focused three-person team re-engineered the stack in place. They updated legacy code to .NET, optimized SQL queries and indexes to handle high volumes of routing and inventory tracking transactions, redesigned the interface using React and Angular, and added ERP integration layers via RESTful APIs. And all this while maintaining backward compatibility, so no distributor was left behind.
Result: 30% faster performance under load, a 20% shorter feature-development cycle, 40% higher user satisfaction, and a 97% customer retention rate over a partnership spanning more than three years.
Re-engineering here centers on making legacy EMR/EHR systems interoperable (FHIR R4/R5 support, clean integration with modern medical devices) while keeping PHI secure and the platform online. A Strangler-Pattern rollout with a clinically validated rollback plan lets us modernize without interrupting patient care, allowing for performance enhancement and 99.9%+ uptime. Compliance-by-design keeps CTOs clear of HIPAA penalties that can reach $1.9M a year.
Financial platforms must simultaneously ensure speed and strict regulatory compliance, particularly with EU DORA and PCI DSS 4.0 standards. We help companies move away from legacy monolithic code in favor of architectures that support real-time fraud detection with latency of less than 100 ms. Our solutions cut $2.9M in yearly legacy maintenance costs and directly increase EBITDA. CHI’s ISO 27001 certification confirms a robust level of information security, simplifying audits by corporate clients and accelerating due diligence processes during mergers and acquisitions.
Logistics giants are often held back by 10-20-year-old ERP and CRM systems that block digital transformation. We re-engineer these core platforms to handle the massive volumes of data required for Big Data analytics, ensuring seamless communication across the global supply chain. By focusing on Vendor Consolidation, we help CIOs move away from fragmented point solutions toward a unified, AI-ready architecture. Our modernization strategy frees up to 50% internal engineering time, allowing teams to focus on transforming the business instead of maintaining legacy systems.
The EdTech sector is projected to reach $348.41B by 2030, but growth is often stalled by monolithic architectures. The typical re-engineering work in EdTech involves breaking these legacy systems into scalable services that can handle exam-season peaks and millions of concurrent users. We prioritize enterprise features like SSO and GDPR compliance — that close large corporate-training deals. The backend ends up ready for adaptive learning and aligned with EU AI Act rules for educational tools.
For SaaS companies, staying on an aging platform is a competitive risk — high-technical-debt companies are 40% more likely to miss their modernization goals. We migrate applications to multi-cloud setups and put Internal Developer Platforms and structured code review in place, which is what stops AI assistants from multiplying duplicated code eightfold. Done right, AI becomes a delivery accelerator instead of a new source of debt.
We understand the intrinsic value of your established business logic. Our approach to legacy software modernization is surgical — utilizing application re-engineering and the Strangler Pattern to enable incremental migration without the risk of a full-system rewrite or business downtime. We preserve the core components that work while replacing legacy bottlenecks that block AI-readiness or scalability. This strategy specifically addresses the 96% of organizations that remain dependent on legacy stacks but need to break the “Tech Vicious Cycle”.
We mitigate your financial risk through a milestone-based delivery model. Our re-engineering process begins with fixed-price pilot phases that provide a “board-ready” ROI case before you commit to a full-scale digital transformation. This approach is tailored for CEOs and CFOs managing “hiring freezes” or CapEx constraints, allowing for manageable operational costs and clear financial predictability.
We don’t just deliver code; we deliver measurable results tied to your specific KPIs. Whether it is achieving Elite DORA metrics (on-demand deployment), reducing the $2.9M average annual legacy maintenance cost, or freeing up 50% of engineering time for innovation, we hold ourselves accountable to the business goals set at the project’s inception. Our SLA-backed Dedicated Teams ensure that modernization translates directly into Revenue Growth and NRR.
Partnering with CHI Software means working with an organization that treats security and quality as a foundational architecture, not an afterthought. Our ISO 9001 (Quality Management) and ISO 27001 (Information Security) certifications serve as a documented guarantee for board-level stakeholders and regulators. This is critical for SaaS and enterprise organizations in FinTech and Healthcare who must meet stringent HIPAA, GDPR, and EU DORA requirements while navigating the 8x increase in AI-induced technical debt.
The CHI Software team successfully developed upgraded versions of the company's products with new functionalities and features. Thanks to these contributions, the company was able to increase its number of platform users and deepen the level of their engagement.
Founder at Albert AB
At Imagine Learning, we highly value our collaboration with CHI Software. Their skilled AI and generative AI engineers have significantly enhanced our K–12 education solutions.
With CHI Software’s help, we have developed tools for providing feedback on student writing, mechanisms to create learning content based on reading education strategies, and a series of chatbots that provide efficient and accurate information access for our teams. Their ability to deliver specialized applications swiftly demonstrates their agility.
VP, Product Management, AI Initiatives at Imagine Learning
CHI Software's team has impressed us with their exceptional expertise and deep knowledge in generative AI. Their specialists are not only highly educated but also demonstrate an advanced understanding of the latest AI trends, particularly in Large Language Models (LLMs) and cutting-edge AI systems.
What sets CHI Software apart is their ability to stay at the forefront of AI innovation, consistently providing expert insights and creative solutions. Their professionalism and intellectual rigor have been invaluable, making them a trusted partner for our AI-driven initiatives. We highly value the depth of their knowledge and forward-thinking approach.
Founder and CEO at New Digital Intelligence GmbH
The CHI Software team were able to implement several applications in the app that were in accordance with the requirements of the client. This allowed them to speed up and simplify the interface and make it more comfortable to use. The team's workflow was efficient.
COO at Soft Industry Alliance
CHI Software has played a pivotal role in the success of Trapelo Health by bringing flexibility, scalability, and deep technical expertise to the project. Their team has been instrumental in integrating complex lab systems and developing a highly configurable, adaptive platform.
Thanks to their well-architected approach, we continuously incorporate new laboratories and clinics into the system, significantly accelerating onboarding and data exchange, regardless of their technology stack. CHI Software’s contributions have helped us achieve a scalable, future-proof system that continues to deliver value to all its stakeholders.
Head of Product at Trapelo Health
We are experts in a wide variety of modern technologies. Our typical technology stack includes Java (Spring), Node.js, Python, and .NET for backends, with React or Angular for the user interface. We are highly proficient in all major cloud platforms (AWS, Azure, GCP).
The main application re-engineering strategies include refactoring, re-engineering, and rebuilding. Refactoring improves the internal structure of existing code, re-engineering makes broader changes to code, architecture, and data while preserving valuable business logic, and rebuilding replaces components or systems that are no longer practical to modernize.
You need re-engineering when your maintenance costs exceed your innovation budget, when your outdated applications can no longer scale, or when you face significant security risks due to outdated code.
Application modernization is a broad term for updating legacy technology and systems. Application re-engineering is a specific, deep-level process within modernization that involves changing the code and architecture to enhance performance and efficiency.
A typical re-engineering process can take anywhere from 3 to 12 months, depending on the complexity of the existing system and the volume of legacy data involved. We use a phased approach to ensure you see results early in the project.
The cost is dependent on the level of technical debt and the scale of the outdated systems. We offer an initial audit to provide a detailed cost-benefit analysis, ensuring the project aligns with your business goals and budget.
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