Legacy software modernization. Make your monolith AI-ready
We modernize legacy applications into structured, API-first systems that support AI, scale with demand, and remain stable in production. Our engineers refactor core logic, decouple dependencies, and prepare your data and architecture for seamless integration with modern AI copilots and intelligent workflows.
Legacy software modernization services
Legacy modernization is not a single activity. It is a sequence of structured decisions, architectural changes, and controlled execution stages. We design modernization as a set of clearly defined services, so you can engage exactly where your system requires progress – with full visibility into scope, effort, and outcomes.
Legacy system assessment & consulting
We establish a precise understanding of how your system operates today.
We analyze architecture, data flows, integrations, and operational behavior using a combination of expert review and AI-assisted system mapping. Undocumented logic, hidden dependencies, and structural constraints are identified and documented in a shared system view.
Result:
A clear system map, defined risk zones, and modernization options with explicit trade-offs – allowing decisions to be made with confidence and alignment across teams.
Application & architecture modernization
We restructure application architecture so systems remain stable in production and predictable under change.
Components are reorganized, boundaries are clarified, and coupling is reduced. Core business logic is preserved while the system becomes easier to extend, integrate, and maintain.
Result:
Stable releases, fewer unintended side effects, and an architecture that supports ongoing development without operational friction.
Cloud & infrastructure modernization
We redesign infrastructure to behave consistently under load and adapt to changing operational requirements.
Environments, deployment pipelines, and hosting models are structured to support scalability, recovery, and controlled delivery. Infrastructure becomes aligned with both technical and regulatory expectations.
Result:
Predictable deployments, defined recovery paths, and infrastructure that supports growth without operational strain.
AI-accelerated legacy transformation
We apply AI directly to the modernization process to increase speed, accuracy, and visibility.
Legacy codebases are analyzed using AI-assisted mapping, enabling faster understanding of undocumented logic. Outdated technologies are translated into modern frameworks, and regression tests are generated automatically to validate behavior.
Result:
Faster modernization cycles, improved accuracy of system understanding, and a structured transition from legacy environments to modern, maintainable architectures.
Get your modernization roadmap
Receive a phased, low-risk plan to upgrade your software without disrupting operations.
Business outcomes after modernization
From a financial standpoint, modernization matters when it improves predictability, reduces exposure, and makes spending easier to justify and control. The outcomes below describe what changes once systems stop driving unplanned costs and operational risk.
Predictable delivery and planning
We restructure applications so dependencies are visible, boundaries are clear, and changes behave consistently across environments.
At the same time, we introduce API layers that allow AI-driven features such as copilots or decision support to integrate without disrupting core workflows.
Release cycles stabilize, estimates become reliable, and new capabilities can be introduced with confidence.
Lower and more controllable operating costs
We reduce structural complexity inside the system and streamline how infrastructure and services are used.
AI workloads are introduced through controlled pipelines, with clear cost boundaries and measurable usage patterns.
This keeps operations and AI-driven functionality predictable from a cost perspective.
Reduced operational and financial exposure
We design systems to behave consistently under varying load and usage conditions, including AI-driven interactions.
AI components operate through isolated layers, ensuring that core systems remain stable while new capabilities are introduced.
This creates a controlled environment where system behavior remains consistent as complexity grows.
Stronger security and compliance control
We embed security and compliance logic directly into system architecture, data handling, and access control.
AI interactions are governed through defined data access layers, ensuring that sensitive information remains protected and traceable.
Audit processes remain structured as new AI capabilities are introduced.
Reliable data for financial decisions
We align data structures, ownership, and flows across systems, preparing them for analytics and AI usage.
Data becomes consistently accessible through structured pipelines, enabling AI models to operate on clean, governed inputs.
Teams work with the same trusted data across reporting, automation, and AI-driven insights.
Capacity for growth with AI-ready infrastructure
We design systems that support new integrations, channels, and AI capabilities without introducing instability or disproportionate cost increases.
Modern architecture allows you to extend functionality with copilots, automation, and advanced analytics as needed, without reworking the foundation.
Growth becomes a controlled expansion of capabilities rather than a structural challenge.
Predictable delivery and planning
We restructure applications so dependencies are visible, boundaries are clear, and changes behave consistently across environments.
At the same time, we introduce API layers that allow AI-driven features such as copilots or decision support to integrate without disrupting core workflows.
Release cycles stabilize, estimates become reliable, and new capabilities can be introduced with confidence.
Lower operating costs
We reduce structural complexity inside the system and streamline how infrastructure and services are used.
AI workloads are introduced through controlled pipelines, with clear cost boundaries and measurable usage patterns.
This keeps operations and AI-driven functionality predictable from a cost perspective.
Reduced operational and financial exposure
We design systems to behave consistently under varying load and usage conditions, including AI-driven interactions.
AI components operate through isolated layers, ensuring that core systems remain stable while new capabilities are introduced.
This creates a controlled environment where system behavior remains consistent as complexity grows.
Stronger security and compliance control
We embed security and compliance logic directly into system architecture, data handling, and access control.
AI interactions are governed through defined data access layers, ensuring that sensitive information remains protected and traceable.
Audit processes remain structured as new AI capabilities are introduced.
Reliable data for financial decisions
We align data structures, ownership, and flows across systems, preparing them for analytics and AI usage.
Data becomes consistently accessible through structured pipelines, enabling AI models to operate on clean, governed inputs.
Teams work with the same trusted data across reporting, automation, and AI-driven insights.
Capacity for growth with AI-ready infrastructure
We design systems that support new integrations, channels, and AI capabilities without introducing instability or disproportionate cost increases.
Modern architecture allows you to extend functionality with copilots, automation, and advanced analytics as needed, without reworking the foundation.
Growth becomes a controlled expansion of capabilities rather than a structural challenge.
Your architecture blocks AI innovation
The system continues to operate, but its structure defines how far new initiatives can go. AI, analytics, and integrations require flexibility at the architectural level. In legacy environments, this flexibility is limited.
Costly changes
Small updates expand into multi-system efforts. Dependencies are not always visible, and changes affect areas beyond their original scope. Estimates lose consistency, and release cycles extend.
Systems run the business
Critical business rules are embedded in legacy code, undocumented flows, and long-standing assumptions. Understanding how the system behaves requires time, context, and specific expertise.
Technical debt
Maintenance, fixes, and support consume a growing share of engineering capacity. Effort shifts toward keeping the system stable instead of moving it forward.
Fragmented data
Information is distributed across systems with inconsistent structure and ownership. Access depends on manual processes, and data cannot be used directly across workflows.
Security and compliance
Access models, libraries, and data handling approaches reflect earlier requirements. As standards evolve, maintaining alignment becomes increasingly complex.
AI initiatives constrained
Modern AI workflows require stable APIs, consistent data, and controlled integration points. Legacy systems were not designed for this interaction model, which limits how AI capabilities can be introduced.
Request a legacy audit
Identify hidden risks, security gaps, and performance bottlenecks in your current code.
Legacy software we modernize
We modernize business-critical applications and platforms that support real operations, carry transactional load, and connect multiple parts of the organization – while preparing them for AI integration and data-driven workflows.
- Core transaction and processing systems
- Custom ERP, CRM, and internal operational platforms
- Backend services and business logic layers
- Complex integrations between internal and third-party systems
- Data platforms, reporting pipelines, and analytics foundations prepared for AI use
- AI integration layer, including APIs, data pipelines, and vector-ready data structures
- Legacy web, desktop, and hybrid applications that have evolved over time
If a system supports core workflows, holds critical data, or connects multiple parts of the business, we modernize it with a structure that keeps operations stable and enables controlled, scalable evolution with AI capabilities embedded where they create measurable value.
Our recent works
The system has produced a significant competitive advantage in the industry thanks to SumatoSoft’s well-thought opinions.
They shouldered the burden of constantly updating a project management tool with a high level of detail and were committed to producing the best possible solution.
I was impressed by SumatoSoft’s prices, especially for the project I wanted to do and in comparison to the quotes I received from a lot of other companies.
Also, their communication skills were great; it never felt like a long-distance project. It felt like SumatoSoft was working next door because their project manager was always keeping me updated. Initially.
We tried another company that one of our partners had used but they didn’t work out. I feel that SumatoSoft does a better investigation of what we’re asking for. They tell us how they plan to do a task and ask if that works for us. We chose them because their method worked with us.
SumatoSoft is great in every regard including costs, professionalism, transparency, and willingness to guide. I think they were great advisors early on when we weren’t ready with a fully fleshed idea that could go to market.
They know the business and startup scene as well globally.
SumatoSoft is the firm to work with if you want to keep up to high standards. The professional workflows they stick to result in exceptional quality.
Important, they help you think with the business logic of your application and they don’t blindly follow what you are saying. Which is super important. Overall, great skills, good communication, and happy with the results so far.
They are very sharp and have a high-quality team. I expect quality from people, and they have the kind of team I can work with. They were upfront about everything that needed to be done.
I appreciated that the cost of the project turned out to be smaller than what we expected because they made some very good suggestions. They are very pleasant to work with.
The Rivalfox had the pleasure to work with SumatoSoft in building out core portions of our product, and the results really couldn’t have been better.
SumatoSoft provided us with engineering expertise, enthusiasm and great people that were focused on creating quality features quickly.
SumatoSoft succeeded in building a more manageable solution that is much easier to maintain.
When looking for a strategic IT-partner for the development of a corporate ERP solution, we chose SumatoSoft. The company proved itself a reliable provider of IT services.
Thanks to SumatoSoft can-do attitude, amazing work ethic and willingness to tackle client’s problems as their own, they’ve become an integral part of our team. We’ve been truly impressed with their professionalism and performance and continue to work with a team on developing new applications.
We are completely satisfied with the results of our cooperation and will be happy to recommend SumatoSoft as a reliable and competent partner for development of web-based solutions
Together with the team, we have turned the MVP version of the service into a modern full-featured platform for online marketers. We are very satisfied with the work the SumatoSoft team has performed, and we would like to highlight the high level of technical expertise, coherence and efficiency of communication and flexibility in work.
We can say with confidence that SumatoSoft has realized all our ideas into practice.
We speak both languages: legacy code and LLMs
As a dual-engine engineering firm, we modernize deterministic legacy systems while introducing governed AI capabilities as a native part of the platform. This allows your software to remain stable in production while becoming ready for modern AI-driven workflows.
Data ETL and vectorization
We transform legacy data into a structured, usable asset for AI.
We design ETL pipelines that extract, clean, and standardize data across fragmented systems, then structure it for modern retrieval and analysis. Historical data is no longer locked in isolated databases – it becomes accessible, searchable, and usable across applications and AI layers.
This creates a consistent data foundation that supports reporting, automation, and AI-driven interaction without additional operational complexity.
AI-accelerated legacy translation
We modernize legacy systems with AI-assisted engineering workflows.
We use controlled AI tooling to map undocumented business logic, translate legacy code into modern frameworks, and generate regression-safe updates. This allows modernization to progress faster while maintaining alignment with existing system behavior.
The result is a codebase that is easier to maintain, extend, and integrate – while preserving the logic your business depends on.
Incremental modernization with architectural continuity
We modernize systems in controlled steps, without disrupting how the business operates.
We avoid replacing entire systems at once. Instead, we extract and upgrade individual components, introduce modern interfaces, and evolve the architecture progressively. Each step integrates with the existing system while preparing it for future capabilities.
This approach keeps operations stable while steadily improving system structure, flexibility, and performance.
Security and access control as part of the architecture
We embed modern security standards directly into the system design.
We implement structured access control, secure API mediation, and modern authentication mechanisms such as OAuth 2.0. Data handling and system interaction follow clearly defined rules, ensuring that access, usage, and integrations remain consistent and controlled.
This creates a system that aligns with current security expectations while remaining adaptable to future requirements.
Request a legacy modernization quote
Identify hidden opportunities, risks, planning gaps bottlenecks in your current solution.
Risk control & engineering discipline
Modernization is executed as a controlled engineering process with clearly defined system behavior at every stage. Each change is introduced through structured release mechanics, validated against real workloads, and aligned with production constraints. The result is predictable system evolution with stable, observable system performance.
No-downtime execution as a standard approach
Changes are introduced through phased releases, parallel environments, and controlled traffic routing. Components are updated incrementally, allowing new and existing logic to operate side by side while behavior is continuously observed and validated.
Data integrity built into every step
Data handling follows a defined validation flow. Schemas, dependencies, and records are verified before and after each transformation. Migration steps are repeatable, traceable, and aligned with production data structures, ensuring consistency across systems.
Validation under real operating conditions
System changes are tested against realistic data volumes, integrations, and performance scenarios. Business logic, edge cases, and system behavior are evaluated before release, ensuring alignment with production conditions.
Controlled coexistence of system components
Legacy and modernized components operate within clearly defined boundaries. Interfaces are explicit, responsibilities are separated, and interactions are observable. This structure allows individual components to evolve independently while maintaining overall system coherence.
Governed AI integration layer
AI capabilities are introduced as controlled system components within the architecture. Model access is mediated through APIs and middleware, with defined input and output boundaries, rate control, and usage policies. Data used for AI processing follows structured pipelines, including transformation, validation, and access control. This ensures that AI operates within the same engineering discipline as the rest of the system, with predictable behavior and full alignment to your infrastructure standards.
Rollback and recovery as engineered capabilities
Each release includes predefined rollback paths and recovery procedures. These are validated before deployment and aligned with system dependencies, ensuring that system state remains consistent and controlled throughout the transition.
Modernizing a live enterprise system is like performing surgery on a beating heart—there is no room for error. We approach legacy transformation with a strategy that prioritizes business continuity, ensuring that you evolve your technology without pausing your growth.
Cost of legacy systems in an AI-driven environment
AI depends on structured data, clear interfaces, and adaptable systems. In legacy environments, introducing AI requires additional preparation – aligning data, defining APIs, and coordinating dependencies across systems.
At the same time, ongoing maintenance continues to absorb engineering capacity. Teams split effort between supporting existing systems and enabling new AI capabilities, which extends timelines and increases overall cost.
Modernized systems operate differently. With clean architecture and accessible data, AI capabilities are introduced with predictable effort, and costs stabilize as systems become easier to evolve.
Strangler fig AI integration approach
We modernize legacy systems through controlled, incremental transformation.
Instead of replacing entire platforms, we evolve them step by step – introducing modern architecture and AI capabilities where they deliver immediate value.
Incremental modernization with operational continuity
We isolate a single workflow or system boundary and redesign it as an independent service.
This new component operates alongside the existing system, handling a defined scope without disrupting surrounding processes.
Each step is self-contained, observable, and validated before moving further.
The system evolves in place, while business operations continue as usual.
Structured extraction of legacy functionality
Legacy systems often contain tightly coupled logic that spans multiple components.
We reorganize this logic into clearly defined services with explicit interfaces and responsibilities.
This creates:
- Stable boundaries between components
- Predictable behavior under change
- Clear ownership across teams
Over time, the system shifts from implicit dependencies to explicit architecture.
AI integration at the right architectural layer
AI capabilities are introduced only after the system can support them properly.
We connect AI to modernized services, structured data pipelines, and API layers – never to fragile legacy components directly.
This enables:
- Consistent interaction with operational data
- Reliable performance under load
- Controlled integration with existing workflows
AI becomes a managed part of the system, not an external add-on.
Controlled evolution toward a modern architecture
As individual components are modernized, the role of the legacy system gradually narrows.
New services take on more responsibility, while older parts are phased out naturally.
This approach results in:
- Steady architectural improvement
- Reduced dependency on legacy constraints
- A system that becomes easier to extend, integrate, and scale over time.
Zero-downtime migration pipeline
Modernization progresses through a structured pipeline designed for continuous system operation, controlled change, and measurable advancement. Each phase builds on verified outputs, allowing modernization to move forward without disrupting business activity.
We analyze your legacy system using a combination of expert review and AI-assisted code interpretation.
Undocumented logic, hidden dependencies, and implicit workflows are translated into a structured system map.
This creates a shared, accurate understanding of how the system operates today and defines clear boundaries for modernization work.
We introduce an API layer that separates your user interfaces and integrations from the legacy core.
This layer stabilizes system interaction, standardizes data exchange, and creates a controlled environment where new components can be developed and deployed independently of the existing system.
High-impact workflows are incrementally extracted from the legacy system and implemented as independent services.
Each extracted component operates alongside the existing system, with clearly defined interfaces and controlled traffic routing. Functionality is transferred step by step, maintaining continuity while the architecture evolves.
Legacy data is structured, cleaned, and prepared for modern use through continuous data pipelines.
We introduce vector-based data layers and connect AI capabilities directly to modernized components, enabling intelligent interaction with historical and operational data without affecting the stability of core systems.
Each phase is executed with defined validation criteria, measurable outputs, and clear transition checkpoints.
This ensures that modernization remains predictable, observable, and aligned with both technical and business objectives at every step.
Let’s start
If you have any questions, email us info@sumatosoft.com
Frequently asked questions
Do we need to migrate our legacy on-premise application to the cloud before we can integrate AI?
Not necessarily. While cloud migration (AWS/Azure) offers the best scalability, highly regulated industries (defense, healthcare) cannot move data to the public cloud. We can modernize your on-premise architecture by containerizing your legacy app (Docker/Kubernetes) and deploying locally hosted, quantized small language models (SLMs) directly onto your private servers.
How do you safely connect an AI copilot to a fragile, legacy SQL database?
We never connect an LLM directly to a legacy transactional database, as heavy AI querying will cause the system to crash. We build a secure read-replica architecture. We sync your legacy database to a modern, decoupled vector database. The AI queries the vector database, keeping your core legacy system stable and secure.
How do you modernize undocumented legacy code when the original developers have left the company?
We use a combination of static code analysis and AI-assisted context mapping. We deploy secure LLMs to analyze your legacy codebase, reverse-engineer the undocumented business logic, and generate comprehensive architectural diagrams. This allows us to safely refactor the code without guessing how the original logic worked.
What is the difference between “lift and shift” and “AI-ready modernization”?
“Lift and shift” moves your old, inefficient code from a local server to the cloud and leaves technical debt unchanged. “AI-ready modernization” refactors the code into API-first microservices, allowing your system to securely transmit data payloads to AI orchestration layers such as LangChain or LlamaIndex without latency or security issues.
How do you modernize legacy systems written in outdated languages when the original developers have retired?
We do not rely on manual, line-by-line translation. We use AI-assisted code refactoring. We deploy fine-tuned large language models to read your legacy codebase (like COBOL, Delphi, or early Java), map the undocumented business rules, and translate the logic into modern, secure frameworks (like Node.js or modern Java/Python). Our architects validate and optimize the new codebase, reducing modernization timelines by up to 50%.
