- Architecture Analysis. AI agents examine the structure of your legacy systems and surface the logic paths, bottlenecks, and hidden debt that slow down delivery. You get clear architectural visibility, which makes modernization decisions easier.
- Dependency Mapping. Autonomous scanners show how systems behave in production and highlight fragile integrations, duplicated endpoints, and risky third-party touchpoints. You see where the system is likely to fail before real-time or AI-driven workloads add pressure.
- Security Scan. AI‑driven engines evaluate code, infrastructure, and configurations to pinpoint outdated components, exposed interfaces, and high‑impact vulnerabilities. You get a realistic view of your security posture instead of relying on old audits or assumptions.
- Modernization Roadmap. A data-backed model shows the modernization sequence that gives the fastest operational lift with the lowest execution risk. The roadmap ties each step to a measurable business outcome, so modernization stays focused.
- Modernization Cost Modeling. A financial model quantifies the cost of maintaining the current state and contrasts it with the projected gains from modernization and AI‑assisted refactoring. The analysis gives leaders a clearer business case for modernization.
Modernize-to-AI Programs
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ACCELERATE VALUE CREATION
Cut modernization timelines by up to 70% with AI while tying every upgrade to measurable business outcomes and predictable costs. -
UNIFY YOUR OPERATIONS
Connect fragmented systems through a trusted data layer that keeps information flowing cleanly across your business. -
MODERNIZE WITHOUT DISRUPTION
Upgrade legacy systems incrementally with AI-assisted refactoring and automated testing while keeping production online.
The Devox Software Difference
AI‑ready data foundation
Your data becomes a governed semantic layer that AI can actually rely on. A unified, trusted foundation replaces scattered sources, giving your team a clean path to RAG systems, AI agents, and faster product delivery.
Audit‑ready compliance
Your architecture gains built‑in lineage, access controls, and evidence trails aligned with SOC 2, NIST, and SEC expectations. This lets you adopt AI at enterprise speed without exposing the business to compliance or data‑handling risk.
Platform modernization
Your legacy stack evolves into a modern, AI‑capable platform without breaking core logic or slowing delivery. AI‑assisted refactoring paired with senior engineering oversight cuts timelines nearly in half while keeping the system stable under real workloads.
System interoperability
Your fragmented systems start operating as one coherent platform instead of isolated silos. A unified namespace and event‑driven patterns create clean data flows across teams, helping decisions move faster and with more confidence.
Services
What We Offer
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Legacy Modernization Assessment
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AI Data Readiness
- Data Readiness Assessment. Your current data landscape is evaluated to determine whether it can support AI workloads and where the gaps are. You get a clear view before investing in models or tooling.
- Data Quality Scoring. Data sets are scored for accuracy, consistency, and completeness to understand how well they can serve AI systems. This shows which sources are usable today and which need remediation.
- Data Access Governance. Permissions, PII handling, and access controls are structured to support safe AI usage across your engineers. Sensitive data stays protected while approved systems can access what they need.
- Training Data Preparation. Raw data is labeled, structured, and transformed into formats suitable for machine learning and GenAI pipelines. This shortens the path from messy inputs to production-ready training data.
- AI‑Readiness Roadmap. A clear plan outlines how to move from fragmented, inconsistent data to AI‑ready inputs that can power real applications. Leaders can sequence the work and invest where it matters most.
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AI-Driven Code Refactoring
- Framework Upgrades. Our highly specialized engineers use AI agents to assess legacy frameworks and move applications to modern runtimes such as .NET 8 or current JDK versions.
- Monolith Decomposition. AI systems examine call patterns, data flows, and code boundaries to identify natural seams inside large monoliths. Your team can break complex systems into services based on evidence instead of guesswork.
- Integration Tests. AI helps generate test suites for legacy codebases that lack coverage. Every change gets automated validation, which makes modernization safer.
- Documentation Generation. AI analyzes undocumented codebases and produces documentation that reflects how the system works today.
- Platform Migration. AI-assisted translators move applications from aging languages like COBOL, VB6, or legacy PHP into modern, maintainable stacks. This reduces long-term risk by removing technologies that are harder to support each year.
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Cloud-Native Modernization
- Kubernetes Modernization. AI systems analyze application behavior and reshape legacy workloads into container‑ready components that run cleanly on Kubernetes. This creates a smoother path from rehosting to full re-architecture without forcing a risky, all-at-once rewrite.
- Event-Driven Re-Architecture. Workloads are redesigned around serverless functions and real‑time event flows that eliminate idle compute and reduce operational drag. It’s a shift that gives your systems the elasticity needed for AI‑driven automation and unpredictable traffic patterns.
- Cloud Cost Optimization. Models evaluate usage patterns, resource waste, and deployment inefficiencies to identify opportunities for reducing cloud spend without hurting performance. The outcome is a more disciplined cost structure that aligns cloud consumption with actual business demand.
- Resilient Auto-Scaling. Architectures are rebuilt to scale automatically and withstand regional failures, traffic spikes, and dependency outages. A design like this architecture gives your team confidence that AI workloads won’t collapse under pressure or create new single points of failure.
- Cloud Migration Execution. AI‑assisted workflows move applications into AWS, Azure, or GCP and change them from basic lift‑and‑shift deployments to fully cloud‑native patterns. This approach shortens migration timelines while avoiding the operational chaos that usually comes with large‑scale cloud moves.
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Intelligent Data Modernization
- Legacy Database Migration. AI-assisted workflows move legacy databases into modern cloud environments and reshape outdated schemas into cleaner, more scalable structures. The shift removes long‑standing constraints that make data slow, brittle, and expensive to maintain.
- Data Cleanup. AI models detect inconsistencies, duplicates, and low‑quality records across fragmented data sources and remediate them automatically. It’s a practical way to restore trust in data that has been accumulating errors for years.
- Data Cataloging. Metadata is captured, organized, and connected to show where it comes from, how it moves, and who relies on it. Clarity around lineage makes governance far easier and reduces the risk of AI models pulling from the wrong sources.
- Lakehouse Modernization. Legacy warehouses are re-platformed to modern lakehouse architectures such as Snowflake or Databricks.
- GenAI Data Pipelines. AI‑ready pipelines generate embeddings, build retrieval layers, and connect structured and unstructured data into a single semantic fabric. This becomes the backbone for GenAI applications that need rapid, accurate access to enterprise knowledge.
WORKFLOW MANAGEMENT
Configurable Workflow Platform Built on a Low-Code ERP Stack for a U.S. Industrial Manufacturer
A low-code, rule-driven workflow platform layered beside an ERP to automate approvals, enforce SLAs, and deliver instant audit trails.
Additional Info
Core Tech:
- .NET 7
- YAML rule engine
- React 18
- PostgreSQL
- Docker Swarm
- GitLab CI/CD
- Prometheus
- Grafana
- SAML SSO
Country:
USA
REAL ESTATE
Immersive Property Portal with 360° View for Real Estate Buyers and Brokers
A real estate portal designed to streamline property search, simplify renting and buying decisions with personalized housing recommendations.
Additional Info
Core Tech:
- NET Core
- MS SQL
- ELK
- Angular
- React Native
- NgRx
- RxJS
- Docker
- GitLab CI/CD
Country:
UAE
Germany
Testimonials
Testimonials
Sweden
The solutions they’re providing is helping our business run more smoothly. We’ve been able to make quick developments with them, meeting our product vision within the timeline we set up. Listen to them because they can give strong advice about how to build good products.
Carl-Fredrik Linné
Tech Lead at
CURE Media
United States
We are a software startup and using Devox allowed us to get an MVP to market faster and less cost than trying to build and fund an R&D team initially. Communication was excellent with Devox. This is a top notch firm.
Darrin Lipscomb
CEO, Founder at
Ferretly
Australia
Their level of understanding, detail, and work ethic was great. We had 2 designers, 2 developers, PM and QA specialist. I am extremely satisfied with the end deliverables. Devox Software was always on time during the process.
Daniel Bertuccio
Marketing Manager at
Eurolinx
Australia
We get great satisfaction working with them. They help us produce a product we’re happy with as co-founders. The feedback we got from customers was really great, too. Customers get what we do and we feel like we’re really reaching our target market.
Trent Allan
CTO, Co-founder at
Active Place
United Kingdom
I’m blown up with the level of professionalism that’s been shown, as well as the welcoming nature and the social aspects. Devox Software is really on the ball technically.
Andy Morrey
Managing Director at Magma Trading
Switzerland
Great job! We met the deadlines and brought happiness to our customers. Communication was perfect. Quick response. No problems with anything during the project. Their experienced team and perfect communication offer the best mix of quality and rates.
Vadim Ivanenko
COO at
Optherium
United States
The project continues to be a success. As an early-stage company, we're continuously iterating to find product success. Devox has been quick and effective at iterating alongside us. I'm happy with the team, their responsiveness, and their output.
Sweden
We hired the Devox team for a complicated (unusual interaction) UX/UI assignment. The team managed the project well both for initial time estimates and also weekly follow-ups throughout delivery. Overall, efficient work with a nice professional team.
John Boman
Product Manager at
Lexplore
Canada
Their intuition about the product and their willingness to try new approaches and show them to our team as alternatives to our set course were impressive. The Devox team makes it incredibly easy to work with, and their ability to manage our team and set expectations was outstanding.
Tamas Pataky
Head of Product at
Stromcore
Estonia
Devox is a team of exepctional talent and responsible executives. All of the talent we outstaffed from the company were experts in their fields and delivered quality work. They also take full ownership to what they deliver to you. If you work with Devox you will get actual results and you can rest assured that the result will procude value.
Stan Sadokov
Product Lead at
Multilogin
United Kingdom
The work that the team has done on our project has been nothing short of incredible – it has surpassed all expectations I had and really is something I could only have dreamt of finding. Team is hard working, dedicated, personable and passionate. I have worked with people literally all over the world both in business and as freelancer, and people from Devox Software are 1 in a million.
Mark Lamb
Technical Director at M3 Network Limited
Insights
Our Experts' Insights
AI & ML
AI Solutions
Legacy Modernization
AI-Native Architecture Roadmap: From Legacy Systems to AI-Centric Platforms
AI & ML
AI Solutions
Industry Trends
Logistics
The AI Revolution in Retail: Transforming the Shopping Experience
AI Solutions
Research Paper
Designing Responsible AI Systems: Architecture Patterns for Compliance and Transparency
FAQ
Frequently Asked Questions
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How do you control modernization costs?
We combine domain-expert engineering leadership with AI-assisted delivery to accelerate execution while keeping scope, quality, and cost under control. AI-generated code is reviewed, tested, and approved before it reaches production. From planning through rollout, we stay accountable for delivery and provide reports your leadership team can use to evaluate progress and investment.
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How do you uncover legacy business rules and algorithms?
We use AI-assisted analysis to map the codebase and surface the business rules embedded in it. Often, that shortens work that would otherwise take weeks of manual review. Our architects validate the findings and turn them into documentation your team can use, giving you clearer control of critical IP and a stronger foundation for modernization.
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How do you move legacy tech stacks into modern systems?
Our engineers work across both legacy platforms and modern cloud environments, which helps them translate older systems into maintainable software that can scale. We use specialized tooling to convert legacy logic into modern components, with seasoned engineers reviewing each step. The result is a platform that is easier to support, extend, and operate over time.
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How do you avoid cloud vendor lock-in?
We design portable architectures that can run across platforms such as Snowflake, Databricks, and Confluent. Our deployments integrate cleanly with AWS and Azure services while keeping the core system flexible. That gives you room to change providers, adopt best-fit services, and avoid unnecessary dependence on a single vendor.
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How do you keep AI outputs accurate?
We design AI architectures with governance, lineage, and data controls built in from the start. Applications connect to a consistent source of truth, which helps AI systems work from reliable data. That improves the accuracy and auditability of RAG-based workflows while preserving security controls. The architecture can also support controls aligned with SOC 2, NIST, and relevant SEC requirements.
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How do you keep operations running during modernization?
We modernize in phases using the Strangler Fig pattern, replacing one part of the system at a time while the core platform remains in service. Each function is isolated and transitioned gradually so customer activity, transactions, and day-to-day operations can continue with minimal disruption. In most cases, end users experience little to no interruption.
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