- 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 trace how systems behave in production and reveal 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 AI‑driven engines evaluate code, infrastructure, and configurations to pinpoint outdated components, exposed interfaces, and high‑impact vulnerabilities.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 identifies the modernization sequence that delivers 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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MOVE 50% FASTER
Modernization with a business case your board can trust. Our engineers use AI to cut modernization timelines by up to 50% and tie every upgrade to revenue impact and predictable costs. You get one accountable partner and numbers your board can defend.
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ONE SOURCE OF TRUTH
Make your data ready for AI before you invest in models. We connect fragmented systems to a trusted data layer so RAG systems and AI agents work from governed data your security team can trust. And you stay free to switch clouds or models anytime.
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KEEP THE LIGHTS ON
Modernize the core while production stays online. With the Strangler-Fig pattern, AI-assisted refactoring, and auto-generated tests, we swap in modern components one piece at a time. You modernize in phases while the roadmap keeps moving. Business logic stays intact, and users keep working as usual.
Why choose Devox Software?
What We Offer
AI-Ready Data Assets
We turn enterprise data into a semantic architecture that AI systems can use. Software platforms become ready for RAG systems and AI agents, helping your team ship new product capabilities faster.
System Interoperability
We connect fragmented application interfaces into one reliable ecosystem. Data moves cleanly across your operations, which helps your team make faster decisions.
Innovation Capacity
We move resource-heavy legacy systems to modern architectures that cost less to run. Software teams free up budget and ship features faster. Industrial teams get a more predictable cost model that supports growth.
Audit-Ready Compliance
We build data lineage and access controls into the database layer. This architecture helps you meet U.S. requirements such as SOC 2, NIST, and relevant SEC rules while protecting proprietary data.
Platform Modernization
We pair AI agents with senior engineers to move your infrastructure to modern stacks. This approach preserves core business logic and can cut modernization timelines by up to 50%.
Asset Uptime
We build event-driven data pipelines that process high-volume data in real time. Technology platforms detect anomalies in real time and maintain stronger service levels.
Platform Independence
As your dedicated delivery partner, we architect highly adaptable, multi-cloud environments. You keep the freedom to choose the cloud providers and AI models that fit your roadmap and budget.
Revenue Optimization
We tie every architectural upgrade directly to your financial performance and operational stability. Software companies realize concrete return on investment through accelerated product cycles. Manufacturing facilities improve uptime and make output more reliable.
What We Deliver
Services We Provide
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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 where cloud spend can be reduced 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 gives your crew 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 transition them from basic lift‑and‑shift deployments into 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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AI Delivery Accelerator
- SDLC Automation. AI‑driven workflows connect planning, coding, testing, and deployment into a single delivery loop. The shift removes the usual friction between stages and keeps work moving without manual coordination.
- AI Coding Assistant Integration. Modern coding assistants are embedded directly into the development workflow to generate code, surface patterns, and speed up routine tasks.
- Automated Testing. AI expands test coverage, generates new cases, and runs continuous regression checks as the codebase evolves.
- CI/CD Acceleration. Build and deployment pipelines are optimized with AI insights that identify slow steps, redundant jobs, and unnecessary waits. Releases start to move at the pace of the team rather than the pace of the tooling.
- Developer Adoption Playbooks. Clear playbooks guide your team through new workflows, measure velocity gains, and support consistent adoption across engineering groups. It’s a smoother way to bring AI into daily development without leaving anyone behind.
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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 data 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 onto 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 fast, accurate access to enterprise knowledge.
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Enterprise AI Integration
- LLM Workflow Integration. Large language models are embedded directly into core workflows so they can interpret requests, trigger actions, and support decisions inside the systems your department already use. Focused on elevating existing operations rather than adding another layer of tooling.
- RAG System Development. Retrieval‑augmented generation pipelines connect AI models to verified enterprise data, ensuring responses stay grounded in facts. Built to keep AI accurate, auditable, and aligned with your internal knowledge base.
- AI Agent Design. Agents are designed to plan tasks, call tools, and coordinate multi‑step actions across systems without constant human oversight. Intended to remove the manual coordination that slows down complex operational flows.
- Predictive Analytics. Models forecast demand, detect anomalies, and surface recommendations that help your team act earlier and with more clarity.
- Intelligent Automation. AI adapts interactions to user behavior and automates repetitive front-line tasks in real time. Created to make customer experiences feel more responsive and less constrained by static rules.
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API-First Modernization
- Microservices Migration. Large monoliths are broken into well‑defined services using domain‑driven design and real usage patterns. The approach creates boundaries that reflect how the business actually works, not how the legacy system happened to evolve.
- Event-Driven Architecture. Systems are re-architected around event streams, asynchronous communication, and real-time data flows.
- Secure API Gateway. Modern API layers are designed with strong authentication, rate controls, and consistent patterns across REST or GraphQL endpoints. Structured to give you a single, reliable entry point that protects services without slowing them down.
- Legacy Integration Bridges. Anti-corruption layers isolate modern services from legacy systems while still allowing them to exchange data. A cleaner path to modernization emerges when new components no longer inherit the constraints of the old ones.
- Service Observability. A service mesh introduces traffic control, policy enforcement, and distributed tracing across all services.
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Phased Modernization Programs
- Strangler‑Fig Incremental Migration. Legacy systems are replaced step‑by‑step, with new components gradually taking over production traffic. A phased cutover like this reduces operational risk and avoids the disruption of a full rewrite.
- DevOps/MLOps Foundations. Pipelines, infrastructure‑as‑code, and automation frameworks are established to support consistent delivery across applications and models.
- Team Enablement. Engineers are upskilled on modern stacks, cloud patterns, and AI-powered development tools. The goal is to ensure your team can operate the new environment confidently rather than relying on a small group of specialists.
- Post-Modernization Support. Site reliability practices are introduced to monitor systems, optimize performance, and maintain SLAs after the transition.
- Modernization Program Governance. Clear milestones, risk controls, and reporting structures guide the program from planning through execution. A governance model like this keeps stakeholders aligned and prevents modernization from drifting off course.
WORKFLOW MANAGEMENT
SAP-Integrated Automation for a Multi-State U.S. Enterprise
End-to-end tax compliance module embedded into SAP S/4HANA for a U.S. retail and distribution enterprise, automating multi-state filings, real-time calculations, and audit-ready reporting.
Additional Info
Core Tech:
- SAP S/4HANA
- ABAP
- Avalara AvaTax
- Node.js
- PostgreSQL
- SAP BTP
- AWS
- Fiori
- CI/CD
- SOC 2
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
FINTECH
Web3 PaaS Ecosystem for Next-Gen NeoBanking, RegTech, and Secure Data Vaulting
A blockchain-powered PaaS ecosystem enabling financial providers to launch custom neobanking solutions with secure infrastructure.
Additional Info
Core Tech:
- Blockchain
- .NET
- Node.js
- AWS
- Docker
- PostgreSQL
- React Native
Country:
USA
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 Solutions
Industry Trends
Software Development
AI Beyond ADAS: How Automotive Software Creates Additional Value with ML
AI Solutions
Fintech
Web3
Scaling Trust: How AI-Augmented Architectures Accelerate Web3 Business Adoption
AI & ML
AI Solutions
Legacy Modernization
AI-Native Architecture Roadmap: From Legacy Systems to AI-Centric Platforms
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 reporting 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. In many cases, 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 does modernization improve industrial ROI?
We tie each upgrade to measurable business outcomes. With event-driven architecture, your team can capture production telemetry in real time and use predictive maintenance more effectively in day-to-day operations. Equipment availability improves, energy use becomes easier to manage, and capital investments are more likely to translate into operational gains.
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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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How do you scale AI infrastructure quickly?
We build cloud pipelines and containerized environments using proven AWS and Azure patterns. That allows the platform to scale under heavier workloads without adding unnecessary operational overhead. For federal compliance or industrial site requirements, we can also design modular infrastructure with dedicated AI compute, which can reduce deployment time compared with traditional capital projects.
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How do you manage enterprise AI adoption?
We begin with an AI Readiness Assessment to understand team workflows, operating constraints, and the current technology landscape. Those findings inform the governance model, access controls, and rollout approach. We then integrate secure AI services that support internal workflows while giving leadership clear visibility into usage, data handling, and compliance expectations.
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