While some states are modernizing to reduce costs, others are being driven by a different challenge, improving how government services are experienced by the people who use them. In 2026 constituents expect digital interactions with the government to feel no different than digital interactions anywhere else. They expect services to be intuitive and available when they need them.

Research from Georgetown University’s Beeck Center for Social Impact and Innovation’s DigitalGovHub shows that states are increasingly investing in digital transformation to improve service delivery. Better service delivery is not separate from efficiency. In many cases, improving how services are delivered is what drives efficiency.

Expectations Have Changed

The standard for government services has shifted. People no longer compare public services to other agencies. They compare them to every other digital experience they have. Whether while applying for benefits or updating records, users expect those interactions to be simple and consistent. According to StateScoop, the National Association of State Chief Information Officers (NASCIO) reported that digital services are now among the top priorities for state leaders.

This reflects a broader realization that digital experience is no longer just a technology issue but is becoming a trust issue. When services feel difficult to access, the government feels harder to trust.

Why Legacy Systems Cannot Deliver Modern Experiences

Legacy systems were not built for today’s user experience. Many still rely on outdated interfaces, disconnected databases, and siloed workflows that make it difficult for systems to communicate effectively or share information seamlessly across agencies. As a result, users are often forced to repeat information, move between systems, or wait through delays that feel unnecessary. That creates frustration for constituents and inefficiency inside agencies. Every confusing process adds manual work and places more pressure on internal teams. Over time, poor digital experience becomes an operational problem instead of just a customer experience problem.

This is where many states are now seeing modernization differently. Improving digital services is not just about making systems easier to use. It is about reducing the operational burden those systems create.

Better Experience Creates Better Efficiency

States that improve service delivery often see internal benefits at the same time. When systems are easier to navigate, users complete tasks more accurately. When services are connected, staff spend less time correcting errors. When processes are automated, agencies can focus resources on more complex work instead of repetitive tasks.

The result is that better experiences often lead to more efficient operations. This is why digital experience is becoming a strategic priority as it is about building systems that reduce friction across the entire organization. As both Pew and NASCIO suggest, agencies are increasingly recognizing that user experience and operational efficiency are deeply connected.

A More Citizen-Centered Approach

One of the more important shifts happening in 2026 is how states define service delivery. Instead of designing systems around agency workflows, more states are beginning to design services around how people actually interact with the government. That means creating simpler experiences, connecting systems behind the scenes, and continuously improving services based on real user needs. That shift changes modernization from a technical exercise into a service strategy that may be the more meaningful transformation for many agencies. 

Moving Forward with Confidence

Digital experience is becoming one of the strongest drivers behind modernization. Platforms like Karsun’s ReDuX help agencies better understand the systems behind their services, identify where friction exists, and modernize in a way that improves both usability and efficiency. By creating a clearer path from legacy systems to connected experiences, agencies can improve service delivery without increasing complexity.

To learn how agencies are modernizing digital services, explore our latest case studies or connect with our team to discuss how these strategies can be applied in your organization.

State agencies entered 2026 with a familiar challenge: rising expectations constrained by shrinking budgets. Agencies must deliver better services, move faster, and remain secure, all while operating with fewer resources. That pressure changes how states think about modernization. What was once positioned primarily as a long-term technology investment is now being evaluated based on its ability to deliver near-term operational and financial improvements. 

Research from Pew shows that states are doubling down on efficiency and technology innovation as budgets tighten. This shift is already producing measurable results. Maryland, for example, modernized parts of its vehicle fleet operations and expects to save approximately $800,000 annually while Pennsylvania, having taken a similar approach, saved tens of millions of dollars.

States are no longer modernizing simply to replace aging systems. They are modernizing because efficiency has become a financial necessity.

Legacy Systems Are Standing in the Way

Many agencies are trying to improve efficiency using systems that were never designed for this level of demand. Insights from StateScoop show that states are shifting toward technologies like artificial intelligence to automate work and improve operational efficiency. At the same time, the National Association of State Chief Information Officers (NASCIO) continues to highlight digital services and cybersecurity as top priorities.

Legacy systems make that balance difficult. Financial data is often siloed, limiting visibility into spending and making it harder to identify fraud and waste. Manual processes slow reporting and force agencies to react to problems rather than prevent them. This lack of visibility and automation impacts both efficiency and experience. Without real-time insight into operations, agencies struggle to identify inefficiencies early, and staff are forced to rely on time-consuming, manual workflows. 

Fortunately, legacy modernization can solve many of these concerns. For example, one Karsun team addressed this challenge with the development of a dashboard for one of our federal customers. By improving transparency the customer improved accountability across IT investments. By centralizing data and improving visibility into spending and performance, they could more easily track outcomes, identify inefficiencies, and make more informed decisions. 

Cost Optimization Is Becoming a Strategic Priority

Agencies face another challenge with their aging systems. Legacy systems become more costly to maintain and increasingly difficult to support as expertise declines. The longer they remain in place, the harder it becomes to operate efficiently and respond to changing demands.

For many states, modernization is now directly tied to cost control. Rather than making broad changes all at once, agencies are focusing on improving financial visibility, reducing waste, and automating repetitive internal processes that consume time and resources. Modern systems enable this by providing real-time insights and reducing reliance on manual work, allowing agencies to manage resources more proactively. This shift also reflects growing pressure for accountability. As reported by StateScoop, political and budget constraints  are pushing agencies to justify spending more clearly. Modernization, in this context, becomes a way to reduce costs while improving transparency.

A Shift Toward Measurable Results

States are also redefining how they approach modernization. Instead of large, multi-year efforts, agencies are prioritizing initiatives that deliver measurable outcomes such as cost savings and improved operational performance. As highlighted by both The Pew Charitable Trusts and National Association of State Chief Information Officers, modernization is increasingly tied to efficiency, not just innovation.

Moving Forward with Confidence

Cost optimization is only one part of the modernization story. Many states are also focusing on improving how services are delivered, recognizing that better experiences can also drive efficiency. We will explore this shift in more detail in the next blog.

Platforms like Karsun’s ReDuX help agencies better understand their systems, identify inefficiencies, and modernize in a way that reduces risk while improving visibility and control.

To learn how agencies are approaching modernization to improve financial performance, explore our latest case studies or connect with our team to discuss what that strategy could look like in your agency.

As organizations move from AI experimentation to real implementation, one thing is becoming clear: there is no single path to integrating large language models (LLMs). The way enterprises adopt AI depends heavily on where they start, whether that is application development, data infrastructure, or regulatory requirements.

At Karsun, we see this play out across customers. Instead of pushing a single solution, our focus is on helping organizations navigate this growing ecosystem of enterprise AI platforms and then select the approach that best aligns with their goals and mission. We work with many of  the most common enterprise AI platforms including Amazon Bedrock, Databricks Mosaic AI and Snowflake Cortex.

Two Ways Organizations Are Approaching AI

At a high level, enterprise AI strategies tend to fall into two categories. Some organizations prioritize speed and accessibility, adopting API-driven services that make it easy to integrate AI into applications. Others prioritize control and governance, embedding AI directly into their data platforms as part of a broader enterprise AI platform strategy. I like to think of this as a spectrum, ranging from a high-end all-you-can-eat buffet to a custom Michelin-star kitchen. Some enterprise AI platforms are designed for speed and flexibility, allowing teams to quickly access a variety of models and get started with minimal overhead. Others are built for control and customization, enabling organizations to tailor models, workflows, and governance to their specific data and requirements. 

Neither approach is inherently better. They reflect different priorities, and in many cases, organizations are starting to use both, aligning each approach to the specific problem they are trying to solve.

Enterprise AI Platform for Speed and Optionality

Amazon Bedrock 

Amazon Bedrock represents a more application-layer approach to AI. It provides a fully managed, serverless enterprise AI platform that provides access to multiple foundation models through a single API, removing the need to manage infrastructure.

This allows teams to move quickly from experimentation to production. Developers can integrate AI capabilities, test across different models, and scale usage without needing deep expertise in model hosting or data engineering. Built-in guardrails and simplified retrieval-augmented generation workflows also make it easier to incorporate enterprise data while maintaining appropriate controls. For many organizations, especially those already operating in cloud environments, this becomes a practical way to introduce AI without significant overhead.

Enterprise AI Platform for Control and Customization

Databricks Mosaic AI

Databricks Mosaic AI reflects a different approach by embedding AI directly within the data platform. Instead of sitting on top of the stack, it becomes part of the broader data and analytics workflow.

This gives organizations tighter control over how models are built and trained. Teams can work directly with proprietary data and develop more customized solutions. It also supports more complex, multi-step AI systems that go beyond simple prompt-response use cases. For organizations where AI is deeply tied to data strategy, this type of enterprise AI platform is essential to delivering reliable and repeatable outcomes.

Snowflake Cortex

Snowflake Cortex represents another data-centric approach to enterprise AI by embedding AI capabilities directly within the data cloud. Similar to Databricks Mosaic AI, it focuses on bringing models closer to where enterprise data already lives. This allows organizations to run AI-powered functions directly within Snowflake using familiar tools like SQL and Python. Teams can apply LLM capabilities including classification and data enrichment without needing to move data outside the platform or manage additional infrastructure.

For organizations already invested in Snowflake, Cortex becomes a natural extension of their data strategy. It enables teams to integrate AI directly into analytics and reporting workflows while maintaining strong governance and control over sensitive data.

Built for Compliance and Change

For enterprise and government customers, these decisions are rarely just technical. Compliance, security, and regulatory requirements often play a central role in shaping AI architecture. Some organizations prioritize managed enterprise AI platforms with built-in guardrails to simplify compliance, while others require full control over their data and models to meet strict governance standards. Both approaches are reflected in platforms like Bedrock,  Mosaic AI, and Snowflake Cortex.As regulations evolve and model availability changes, organizations need the ability to adapt without being locked into a single provider or model. At Karsun, this is a key part of how solutions are designed. Our goal is to help customers build AI systems that align with their compliance frameworks while remaining adaptable over time.

Connect with our team and learn more about Karsun’s AI solutions to see how these enterprise AI platforms can be applied to your organization’s needs.

When teams move from deciding on their migration strategy to mobilizing to act, agentic AI can be used to enforce secure-by-design practices and policies. Welcome back to the second in our two part series on using agentic AI for DevSecOps to drive secure-by-design architecture. (If you missed part one, check out our previous post The Speed of Relevance: Laying the Foundation for Strong DevSecOps Practices)

Infrastructure as Code Delivers Automated Compliance Enforcement

Secure-by-design principles are enabled through both infrastructure as code and robust security testing practices. First, deployments via infrastructure-as-code (IaC) produce consistent, repeatable, and hardened environments, reducing misconfigurations. This addresses a common security weakness. Additionally, these pipelines generate detailed logs and audit trails.

Furthermore, pipelines can embed policy-as-code and compliance-as-code frameworks, continuously validating that builds align with standards like NIST, CMMC, STIG, RMF, or agency-specific security baselines. They can also build custom workflows and templates that ensure their DevSecOps agents work consistently following their internal guidelines and processes.

There are many examples of how teams can use preconfigured DevSecOps templates and workflows to enhance their security:

• Pipeline templates that enforce secure configurations by default (e.g., encryption turned on, least privilege IAM roles, logging enabled) across every environment.
• Workflows that eliminate hardcoded credentials by integrating with vaults and key management services, ensuring sensitive data is injected securely at runtime.
• Automated pipelines that support rolling updates and security patching, making it easier to quickly remediate vulnerabilities without manual intervention.

Automated Security Testing Provides Guardrails

The other component is security testing. Using ReDuX AI agents in combination with other automation tools, DevSecOps teams use security testing (static code analysis, dependency scanning, secret detection) early in the pipeline, ensuring vulnerabilities are caught before deployment.

Moreover, for every task performed by any ReDuX agent, output can be verified and corrected by a human team member. The self-learning agents improve their process, further improving efficiency gains. And because in enterprise implementations of ReDuX agents share skill improvements across the digital workforce, all agents improve when one agent improves. This process further enhances security beyond what is available with simply co-pilot tools or AI agents used for a single step of the process.

Ultimately, by both using OODA loops as described in our first post and automated compliance, teams can use AI for end enforcement of DevSecOps best practices. In addition to this, one of the most important ways DevSecOps supports security best practices is it fosters a culture of continuous improvement and collaboration, particularly between developers, security, and operations. At its core, DevSecOps best practices shift important security decisions to the left, moving them earlier in the process so that the tools use by modernization teams have the greatest impact at reducing risk.
If you want to see how your team can accelerate decision making and modernize with secure-by-design architecture, schedule a live demonstration with our team. And if you are headed to the AFCEA Belvoir Industry Days May 5-7, 2026, Let’s talk!

Last year our team examined agentic DevSecOps for secure-by-design architecture on our ReDuX blog. In this two part series, experts in our Defense portfolio are returning to their call to shift security decision making left. With renewed attention to the needs of the warfighter and the mission we address these new challenges and opportunities.

For agencies operating in an IL4 or IL5 environment integrating security into their modernization is an essential requirement of the mission. Modern environments must adhere to strict security standards, meet data sovereignty requirements, and enforce secure CI/CD pipelines all while operating within GovCloud.

In short, to effectively optimize DevOps outputs using AI, agencies must partner with organizations that have proven experience working with their highly-regulated environments. Moreover, when the modernized systems are well-architected, they directly operationalize secure-by-design principles. To achieve this state we use our ReDuX platform to observe to understand the legacy system, make informed decisions early and when relevant while building incrementally to adapt to changing mandates.

Shift-Left Security and the Speed of Relevance

In our original post, we noted, the earlier teams identify problems and security risks, the earlier they adapt and develop solutions. We call this Shift-Left Security and it greatly improves the speed of relevance for making critical architecture decisions that improve the overall security of the system.

Shift-Left Security is based on our integration of the Observe-Orient-Decide-Act (OODA) loop into our GoLean development methodology. With GoLean we observe process improvement opportunities earlier. After orienting to a new approach, we then decide on process improvements and act to implement it within our work. As a result of using this data-driven OODA loop process for over a decade our development methodology has been repeatedly appraised at CMMI Level 5. 

When incorporated into our ReDuX process we use OODA loops to not only continuously improve process but also to reduce risk. We do this by building a comprehensive blueprint of the legacy system so we can observe risks. Next we orient to plan our migration roadmap, then decide to mobilize agents and teams to complete our work and finally, act to incrementally modernize while reducing rework.

Reducing Risk with Legacy System Blueprinting

Using agentic AI in our ReDuX platform, blueprinting agents map the structure of legacy systems and identifies its relationship to external components. Our teams can then match legacy code to screen flows and end-points so that teams identify orphan or dead code before they begin migration planning.

Thus, using agentic-driven blueprinting we greatly accelerate the speed of relevance for making critical security decisions. We move the decisions about how to improve, optimize and reimagine the system earlier in the discovery phase which allows us to act and adapt sooner to emerging risks.

If you want to learn how we use system blueprinting to accelerate decisionmaking and build secure systems, schedule a live demo with our team. If you are headed to the AFCEA Belvoir Industry Days May 5-7, 2026, Let’s talk!

And stay tuned! In our next blog we will share how we use AI agents to build secure architecture as part of our DevSecOps practice.

Joe Gebbia, the nation’s first-ever chief design officer, is planning to reshape federal government websites. Calling for delightful websites, the new America by Design initiative calls on agencies to modernize the interfaces that serve everyday citizens. Signaling a new era for government websites, agentic AI may provide the key to unlocking the rapid transformation required to meet this challenge.

The Foundation: A Brief History of USWDS

The cornerstone of this transformation is the U.S. Web Design System (USWDS). Launched in 2015, USWDS was created to provide a shared set of design tools and user interface (UI) components for federal agencies. According to the USWDS history, the project was born from the need to reduce design debt and create a consistent appearance across federal websites. Beginning with a small library of buttons and forms, USWDS eventually evolved into a robust ecosystem used by hundreds of federal projects to ensure accessibility, mobile-friendliness, and trust.

The Product Mindset: Meeting User Expectations

Modernizing federal applications is about more than meeting a set of requirements. Service delivery experts know delivering a delightful user experience requires a fundamental shift in strategy. At Karsun, we advocate for a Product Mindset.

Users expect a seamless experience. Government applications should work across devices. They should meet evolving expectations such as the integration of chatbots. A Product Mindset asks delivery teams to consider not only users’ needs today, but also their future needs. It builds in feedback loops to continuously track and ensure expectations are met. Its modernization and digital transformation that delivers applications designed for every next, ensuring they remain resilient and adaptable using not only design best practices but secure, modular, digital architecture.

ReDuX: The Bridge to the Future

Bridging the gap between legacy systems and delightful, yet compliant, interfaces is a monumental task. This is where the ReDuX platform provides a revolutionary AI-powered solution.

ReDuX uses agentic AI to ensure modernization efforts align perfectly with user intent. By taking user demos as an input, ReDuX analyzes real-life behavior to craft functional requirements that reflect how people actually use a service.

Today, ReDuX incorporates the USWDS among its input during code generation. This ensures that every line of code produced adheres to federal design standards by default. Furthermore, because ReDuX can ingest any reference material provided, it is future-proof. When USWDS evolves to the next era of web standards, ReDuX can meet the challenge, optimizing applications to new standards as they emerge.

The path to better service delivery doesn’t have to be slow. With the power of agentic AI and a Product Mindset approach, federal agencies can transform applications faster than ever before.We invite you to learn how ReDuX can help your agency deliver delightful websites that honor the mission and respect the user. Let’s build the future of federal digital services, together.

For the last decade, microservice architecture has been king. Breaking down massive, monolithic applications into small, independently deployable services revolutionized how we build and scale software.

But what’s the next step?

We’re on the verge of the next major evolution, one that infuses our systems with intelligence. We’re moving from a world of reactive microservices to one of proactive micro-agents.

This isn’t just a change in buzzwords. It’s a fundamental shift from building systems that wait for commands to building systems that understand goals.

The Now: The Reactive Microservice

First, let’s look at the microservice as we know it.

A microservice is like a specialist at a desk with a phone. It’s an expert at one specific task such as checking inventory, processing a payment, sending an email. It’s highly efficient, but it’s also dumb. It does absolutely nothing until someone calls its API and gives it a very specific, rigid command.

• It’s Reactive: It waits for a request.
• It’s Task-Oriented: It executes a single, well-defined job.
• It’s Unaware: It has no concept of the “bigger picture” or the user’s ultimate goal.

All the smart logic for coordinating these services lives elsewhere, often in a central “Orchestrator” service or hard-coded into the application’s front-end. If the payment service fails, the orchestrator must have pre-written, rigid logic to handle that specific error.

The Next: The Proactive Micro-Agent

Now, imagine that same specialist, but instead of just giving them a command, you give them a goal.

This is the micro-agent. It’s an intelligent, autonomous entity that has an objective and the power to decide how to achieve it.

• It’s Proactive: You give it a goal (e.g., “Get this order shipped”), and it decides the steps.
• It’s Goal-Oriented: It understands the “why” behind a request, not just the “what.”
• It’s Context-Aware: It can plan, execute, and even handle errors dynamically.

If the payment agent fails, the checkout agent doesn’t need to follow a rigid script. It can reason about the problem. It might decide to try an alternative payment method, or it might call the notification agent to ask the customer for a new card, all without human intervention.

The Secret Sauce: AI as the Reasoning Engine

What makes this shift possible? Artificial Intelligence (AI) and Large Language Models (LLMs).

An LLM acts as the brain or the reasoning engine for the agent. It’s what gives the agent the power to understand a complex goal, break it down into steps, and orchestrate the tools needed to get it done.

In this new model, your old microservices don’t disappear. They become the “tools” in the agent’s toolbox.

• The PaymentService (a microservice) is just a tool.
• The PaymentAgent (a micro-agent) is the intelligent “brain” that knows when and how to use that tool.

A Tale of Two Checkouts

Let’s look at a simple e-commerce example to see the difference in action.

The Future is Autonomous

Microservices were about decentralizing tasks. This was a huge step forward.

Micro-agents are about decentralizing decisions.

This is the logical and necessary next step for building systems that are not just scalable, but truly intelligent, resilient, and autonomous. The specialists in our architecture are finally getting the promotion they deserve. They transition from order-takers to problem-solvers.

This blog is part of our ongoing series spotlighting our enterprise modernization experts. This edition’s featured author is Hari Narayanan, a Solution Advisor and Architect in the Karsun Innovation Center. Connect with him on LinkedIn.

Join Karsun Solutions on LinkedIn for more from our Enterprise Modernization Experts. To start building with your own autonomous agents check out GoReDuX.ai. For more on our approach to modern software development check out our solutions https://karsun-llc.com/solutions/.

A contractor supporting a critical mainframe modernization project for a state government agency, faced a significant challenge. Tasked with migrating a legacy COBOL system, the contractor’s initial pilot migration attempt using AWS Blu Age could not fully meet customer requirements. The tool could only output Java and not meet the new requirement for Python or C#. Furthermore, the migration lacked traceability, making it impossible to validate. This contractor engaged Karsun Solutions, which leveraged its AI-powered ReDuX modernization platform. Using Agentic AI capabilities powered by Amazon Bedrock, ReDuX automatically analyzed the legacy system and generated the required C# programs, Summer Batch Core workflows, and MS SQL Server tables, delivering the 100% traceability needed to successfully complete the pilot.

About the Customer

The customer was one of three vendors awarded a contract for a cloud services project for a state government agency. This project’s goal is to modernize a core business system by converting legacy mainframe code to Python or C# while moving to a cloud-based platform.

Customer Challenge

A state government agency initiated a high-priority project to convert its legacy system to a modern, cloud-based platform. The state’s primary goals were to enhance security, improve flexibility, ensure data redundancy, and lower long-term maintenance costs.

The project was structured in two phases: a competitive part one pilot awarded to three bidders, followed by a part two award for the full system conversion given to a single winner from the pilot. The customer team was tasked with migrating a subsystem for the pilot, as it was the least entangled with other subsystems.

The customer’s initial modernization attempt hit two critical roadblocks. First, the agency’s requirements evolved. After initially preferring Java, the state later specified a preference for Python or C#. This immediately put the team’s approach in jeopardy, as AWS Blu Age can only create code in Java. Second, they were unable to show file-level COBOL program traceability to modern programs. This made validation and future maintenance impossible in the mind of the customer. Failure to solve these challenges would result in a failed pilot and disqualification from the lucrative part two of the contract.

The Partner Solution

The pilot contract was explicitly for cloud services. The state agency sought the enhanced security, flexibility, and redundancy that a cloud-native solution provides. The successful solution embraced this by using a suite of AWS-native services. Additionally, Karsun used its AI-powered modernization platform, ReDuX, to solve complex traceability problems in legacy systems and could output modern C# code, directly meeting the new requirements.

The ReDuX platform rapidly analyzed the system and generated a fully modernized, traceable equivalent.

1. Agentic Analysis: First, Karsun used its ReDuX Blueprint tool. This tool, which is powered by Amazon Bedrock, applied GenAI models to analyze the system, including COBOL programs, JCL, and DB2 database tables. This process created a comprehensive and fully documented map of the legacy system’s architecture and business logic.
2. Automated Transformation: With this detailed blueprint, Karsun’s ReDuX Code Companion used agents to automatically transform the legacy application. The modernized solution consisted of:
   • Agentic Analysis: First, Karsun used its ReDuX Blueprint tool. This tool, which is powered by Amazon Bedrock, applied GenAI models to analyze the system, including all COBOL programs, JCL, and DB2 database tables. This process created a comprehensive and fully documented map of the legacy system’s architecture and business logic.
   • Automated Transformation: With this detailed blueprint, ReDuX Code Companion used agents to automatically transform the legacy application. The new, modernized solution consisted of:
     • Modern C# Application: C# programs were generated for every COBOL program. Verifiable, one-to-one mapping solved the traceability problem. This new application is hosted on .NET Core containers over the AWS EKS service or on EC2 instances.
     • Cloud-Native Batch Processing: Summer Batch Core workflows were generated for every JCL, converting all mainframe batch processes into cloud-native, serverless workflows.
     • File Storage: Application files are now stored in AWS S3.
     • Angular Front-End: A new user interface in Angular precisely mimicks the flow of the original mainframe green screens, such as the menu and work order creation screens.
     • MS SQL Server Tables: Every legacy DB2 database table was migrated to a new table in MS SQL Server.

Results and Benefits

Karsun’s AI-powered ReDuX platform successfully delivered a modernized pilot application that met all of the state’s requirements, rectifying the failures of the initial attempt. The primary benefit was the achievement of 100% traceability for the entire application stack.

The key success metric was the one-to-one mapping of legacy assets to modern, cloud-native code, all made possible by the GenAI analysis from AWS Bedrock:

• Every COBOL program was converted to a corresponding C# program, now running in .NET Core containers on AWS EKS or EC2.
• Every JCL was converted to a Summer Batch Core workflow.
• Every DB2 table was migrated to an MS SQL Server table.

This provided the customer with a complete, verifiable, and maintainable modern application, allowing them to successfully complete the pilot.

About Karsun Solutions

Karsun Solutions modernizes enterprise systems enabling agencies to make the next technological advancement their next opportunity to elevate mission capability. Solutions are tailored to meet agencies’ unique needs and optimize operations. These solutions adapt and stay relevant to current trends while using secure, digital architecture built to last. It is a proven modernization partner whose expertise elevates agency capabilities and ensures every next opportunity is within reach.

Learn more about Karsun’s Cloud Solutions.

One of Karsun Solutions’ government agency customers manages critical certification systems processing millions of applications globally. This agency partnered with Karsun to modernize its legacy Software AG Natural and ADABAS mainframe system used for its certification workflows. It had demonstrated expertise in mainframe modernization and clear competency in these environments. It had also previously delivered secure, compliant cloud solutions meeting federal requirements for this customer.

It migrated the system to Amazon Web Services (AWS) using modern code deployed on Karsun’s container platform. The modernized system eliminated mainframe dependencies, reduced operational costs, enabled modern integrations, and positioned the agency for continuous innovation while maintaining seamless processing for millions of certifications.

About the Customer

The customer’s regulatory portfolio consisted of approximately 10 interconnected systems handling certifications covering both professional certifications as well as equipment registrations. While serving millions of people globally, the internal processing systems are utilized by fewer than 100 examiners, indexers, and supervisors who review and approve certification applications.

Customer Challenge

The certification system operated on legacy mainframe technology using the Software AG Natural programming language and ADABAS databases. It integrated with multiple systems including an external application submission portal, document management relying on screen scraping, and a portal where individuals manage their certifications.

Moreover, documentation of business rules was limited, making maintenance and enhancement difficult. Integration required manually keyed information from digitized documents. Additionally, recruiting developers with mainframe skills was increasingly difficult, threatening long-term system sustainability.

Partner Solution

The solution architecture utilizes Java for backend services and Angular with Angular Material for the frontend. The customer selected AWS for its proven reliability with mission-critical government systems and compliance with federal security standards. 

Tyrion Container Platform

The new system is hosted on Tyrion, Karsun’s Federal Information Security Management Act (FISMA) High-rated AWS GovCloud container platform based on Amazon EKS. The AWS GovCloud’s isolated region is specifically designed for U.S. government workloads providing the security and compliance framework necessary to meet regulatory requirements.

Karsun previously delivered Tyrion as part of a broader transformation initiative for this customer. It provides agency application owners a platform to build, test, deploy, host, and scale container-based software solutions.

The platform leverages multiple AWS services including Amazon EKS for Kubernetes orchestration, Amazon EC2 for compute resources, Amazon Elastic Block Store (EBS) for persistent storage, and Amazon RDS for managed database services. Tyrion’s DevOps ecosystem includes Tekton for build pipelines, ArgoCD implementing GitOps for continuous deployment, Karpenter for intelligent node scaling, Datadog for comprehensive monitoring, Kubecost for consumption apportionment across teams, and Backstage as a developer portal providing self-service capabilities.

System Consolidation and Modernization

Karsun consolidated two mainframe subsystems eliminating complex inter-system communication mechanisms. The team replaced legacy screen-scraping interfaces with RESTful APIs. This enabled real-time data exchange and eliminated brittle integration points.

Document Workflow Reimagined

Rather than simply replicating mainframe processes, Karsun reimagined workflows for the cloud environment. The new system retrieves only essential metadata from the document management system while maintaining secure access to document images. Karsun implemented automated processing for routine certification types eliminating unnecessary manual review and streamlining operations.

Requirements Validation Approach

Given the minimal documentation, Karsun established a rigorous requirements validation process. It worked closely with the mainframe sustainment team to analyze mainframe code and database schemas. Working with the customer, the team documented findings and confirmed functionalities. Requirements were captured as user stories with acceptance criteria following agile best practices, ensuring business approval at each increment.

Karsun introduced its ReDuX platform. This AI-powered platform for legacy code analysis accelerates discovery and delivery of complex modernization initiatives. Its parser ingests Natural mainframe code and creates comprehensive system blueprints. Using ReDuX the team validated business rules and identified edge cases often overlooked during traditional requirements gathering.

Results and Benefits

Certification processing transitioned from mainframe to AWS, delivering measurable business and technical benefits:

• Operational Efficiency: Automated processing of routine certifications eliminates manual examiner workload for specific application types, allowing staff to focus on complex cases requiring judgment.
• Technical Modernization: API-based integrations replaced screen scraping, improving reliability and reducing integration maintenance. The modern Java and Angular technology stack addresses talent acquisition challenges, with significantly larger developer pools.
• Platform Leverage: Deploying on Tyrion provides the application with enterprise-grade container orchestration, automated scaling through Karpenter, comprehensive monitoring via Datadog, and streamlined DevOps workflows through Tekton and ArgoCD. The FISMA High-rated platform eliminates the need for separate security authorization, accelerating time to production.
• Knowledge Preservation: Comprehensive documentation created during the project created a sustainable foundation for maintenance and enhancements, addressing the legacy system’s critical documentation gap.

About Karsun Solutions

Karsun Solutions modernizes enterprise systems enabling agencies to make the next technological advancement their next opportunity to elevate mission capability. Solutions are tailored to meet agencies’ unique needs and optimize operations. These solutions adapt and stay relevant to current trends while using secure, digital architecture built to last. It is a proven modernization partner whose expertise elevates agency capabilities and ensures every next opportunity is within reach.

Learn more about Karsun’s Cloud Solutions or Discover Modernization Accelerated with ReDuX.

When it comes to IT modernization, standing still is the same as moving backward. Whether adapting to an evolving market or addressing challenges during a government shutdown, Karsun teams never stop learning and growing. They innovate, collaborate and build for every next.

That’s because at Karsun Solutions, our greatest asset is not only what we do with technology, but the collective growth of our experts. Agility and continuous improvement is in our DNA. As a result, we have woven professional development into the very fabric of our identity. Today, we are proud to be recognized nationally and regionally as a Top Workplace. 

Growth Engine: Karsun Academy Career Journey

At the center of our culture is Karsun Academy, our engine for career transformation. Our team members have access to resources covering everything from the latest in AI to essential soft skills like leadership and communication. 

Karsun team members typically take one of two paths for their training. We bring in expert instructors to guide trainees through key certifications. We actively encourage and pay for certifications ensuring our team remains at the cutting edge.

Karsun also offers dojos. These consist of deep-dive, hands-on technical workshops where experts mentor peers through complex coding and architectural challenges. These are typically multiday bootcamp style sessions.

Shutdown Academies Supercharge Expertise

Nowhere is this commitment to training more evident than in Karsun’s shutdown academy program. Initially launched in 2019, during the historic 35-day partial government shutdown, Karsun offered certain team members that could not perform work on their customer programs the opportunity to engage in targeted training. We found this approach to be both beneficial to our team members and our customers who now had access to Karsun experts with enhanced skills and new approaches to their modernization work.

Recognizing these benefits, we launched a special academy cohort during the 2025 government shutdown. This year’s shutdown academy delivered over 8,200 hours of training.

Nurturing Innovation, Growth and Collaboration

We pair our rigorous learning programs with a culture that emphasizes collaboration.The best way to learn is by doing. This is why we created our Innovation Center. 

Here, our experts work together to prototype extraordinary ideas. Every month town halls spotlight the most exciting accomplishments and preview new work from our teams. Through the center team members get hands-on experience with emerging technologies long before they hit the mainstream. Meanwhile, weekly brown bag sessions allow team members to present on emerging trends fostering a culture of peer-to-peer mentorship. Additionally, every quarter, we honor team members who have demonstrated excellence, many of whom have used Karsun Academy resources to level up their performance.

At Karsun Solutions, we look beyond the capabilities and skills needed to fulfill contract requirements today. We nurture and grow the experts our customers will need tomorrow. To learn more about partnering with our experts, Contact Us.