Services

We provide end-to-end product engineering services for companies building, modernizing, and scaling digital products. From early product strategy and MVP development to full-scale software engineering, QA, DevOps, and ongoing technical leadership, Devox Software can support the complete product lifecycle with one coordinated team.

Our product engineering services include:

• Custom Software Development for building tailored web, mobile, enterprise, and cloud applications around specific business workflows and technical requirements.
• Product Strategy Consulting to define product scope, priorities, architecture direction, technical risks, roadmap, and go-to-market assumptions before major development investment.
• SaaS & Digital Platform Engineering for multi-tenant SaaS products, digital platforms, portals, marketplaces, billing systems, integrations, and scalable cloud infrastructure.
• Web Development covering front-end and back-end engineering, responsive interfaces, APIs, integrations, CMS solutions, portals, and complex web applications.
• CTO as a Service for companies that need technical leadership, architecture decisions, engineering governance, modernization planning, team setup, or support with vendor and technology selection.
• MVP Development to turn a validated product concept into a focused first release that can be tested with users, demonstrated to investors, and expanded based on real feedback.
• QA Engineering including manual and automated testing, API testing, performance testing, regression coverage, test strategy, and quality gates integrated into the development lifecycle.
• DevOps for CI/CD, cloud infrastructure, containerization, observability, deployment automation, infrastructure as code, reliability engineering, and release-process optimization.

We can take ownership of a complete product build or strengthen a specific part of your existing engineering organization, helping move products from concept to production and through ongoing growth.

We modernize aging applications, infrastructure, and architectures to improve maintainability, performance, security, scalability, and readiness for cloud or AI adoption. Our approach ranges from targeted refactoring and integration work to full platform reengineering, helping businesses reduce technical debt without forcing unnecessary rewrites.

Our legacy modernization services include:

• Application Modernization for upgrading outdated applications, frameworks, runtimes, databases, and architectures while preserving critical business logic and minimizing disruption to ongoing operations.
• Legacy-to-Cloud Migration for moving applications and workloads to AWS, Azure, or hybrid environments through rehosting, replatforming, refactoring, or cloud-native transformation based on business and technical requirements.
• Platform Reengineering for redesigning applications whose existing architecture limits scalability, release speed, maintainability, or integration with newer systems.
• Code Refactoring and Optimization to restructure hard-to-maintain code, improve application performance, strengthen testability, remove duplication, and reduce complexity without changing the intended business behavior.
• Monolith Decomposition for identifying service boundaries and gradually breaking large monolithic applications into modular services or microservices, with AI-assisted analysis where appropriate to accelerate dependency mapping and modernization planning.
• API & Integration Modernization to expose legacy functionality through modern APIs, replace brittle point-to-point integrations, introduce API gateways or event-driven patterns, and connect older systems with SaaS, cloud, mobile, and partner platforms.
• Technical Debt Reduction for identifying, prioritizing, and systematically reducing technical debt across code, architecture, dependencies, infrastructure, testing, and delivery processes based on business impact and engineering risk.
• Modernize-to-AI Programs for preparing legacy systems for AI adoption by improving data accessibility, APIs, architecture, cloud infrastructure, security, and integration patterns before adding copilots, RAG, agents, predictive models, or other AI capabilities.

We modernize a complete legacy platform or focus first on the parts creating the greatest operational risk, development bottlenecks, or barriers to cloud and AI adoption.

We design, train, integrate, and operationalize machine learning systems that turn business data into predictions, recommendations, classifications, and automated decisions. Our work covers the full ML lifecycle, from data preparation and model development to deployment, monitoring, retraining, and ongoing performance management.

Our ML and data science services include:

• Custom ML Model Development for building supervised and unsupervised models tailored to your data, workflows, and business objectives, including classification, regression, forecasting, clustering, scoring, and optimization.
• Computer Vision for image and video analysis, object detection, classification, OCR, visual inspection, quality control, document understanding, and other vision-based workflows.
• Predictive AI & Recommendations for demand forecasting, churn prediction, propensity scoring, risk models, next-best-action systems, personalization, and recommendation engines that support faster, data-driven decisions.
• Anomaly Detection for identifying unusual transactions, system behavior, sensor readings, operational events, or user activity in real time or batch workflows, helping detect fraud, failures, defects, and emerging risks earlier.
• MLOps & Model Lifecycle Management for model versioning, deployment pipelines, experiment tracking, monitoring, drift detection, retraining, evaluation, governance, and controlled promotion of models from development into production.

We can build an ML capability from the ground up or improve an existing one by strengthening data pipelines, model quality, deployment reliability, and monitoring so machine learning remains useful after the initial release.

We design and modernize data platforms that make business information easier to collect, move, govern, analyze, and use across applications, analytics, and AI systems. Our work covers data pipelines, migrations, warehouses, reporting, governance, and security controls, helping organizations turn fragmented data into reliable infrastructure for operational and strategic decision-making.

Our data engineering services include:

• Data Analytics for building analytics platforms, data pipelines, reporting layers, and decision-support solutions that turn operational, customer, financial, and product data into actionable insights.
• Database & Data Migration for moving data between legacy and modern databases, cloud platforms, warehouses, or applications with schema mapping, validation, reconciliation, cutover planning, and rollback controls.
• Data Warehousing for designing centralized analytical repositories that consolidate data from multiple systems, support historical analysis, and provide a reliable foundation for BI, forecasting, and AI workloads.
• Business Intelligence for dashboards, KPI frameworks, self-service reporting, semantic models, executive analytics, and operational reporting that give teams consistent access to trusted business metrics.
• Cybersecurity for protecting data platforms and pipelines with access controls, encryption, secrets management, monitoring, vulnerability management, secure cloud configuration, and controls aligned with applicable security and compliance requirements.

We can build a new data platform from the ground up or improve an existing environment by reducing silos, modernizing pipelines, strengthening governance, and preparing data for advanced analytics and AI use cases.

We help companies identify where AI can create measurable value, design the right technical approach, and move solutions from experimentation into production. Our AI services cover readiness assessment, strategy, architecture, generative AI, RAG, conversational systems, agentic workflows, and modernization of existing software for AI adoption. Our AI development services include:

• AI-Powered Development Services for accelerating modernization and software delivery with AI-assisted analysis, code transformation, architecture support, testing, and engineering workflows.
• AI Readiness Assessment to evaluate your data, infrastructure, applications, security, governance, and business processes before investing in AI implementation.
• AI Strategy & Roadmap for prioritizing use cases, estimating business value, identifying dependencies, defining governance, and creating a phased implementation plan.
• AI Architect as a Service for companies that need senior AI architecture leadership without hiring a full-time specialist, including model selection, system design, integration strategy, evaluation frameworks, and technical oversight.
• Generative AI for building copilots, content-generation tools, knowledge assistants, document-processing systems, and other applications powered by foundation models.
• LLM Integration and Fine-Tuning for connecting commercial or open-source models to your products, APIs, workflows, and proprietary data, with fine-tuning or adaptation where it provides measurable value.
• RAG Systems for grounding LLM responses in your documents, databases, and knowledge sources using retrieval, vector search, reranking, citations, and evaluation pipelines.
• Conversational AI Assistants for customer support, employee assistance, sales, operations, and knowledge access through chat or voice interfaces connected to real business systems.
• Agentic AI for building AI systems that can plan, use tools, interact with APIs, and complete multi-step tasks under defined permissions and guardrails.
• Workflow Automation for automating repetitive business processes with AI-driven decision points, tool execution, approvals, and human-in-the-loop controls.
• Multi-Agent System Development for orchestrating multiple specialized agents that collaborate across research, analysis, operations, customer service, or complex enterprise workflows.
• Autonomous Operations for designing controlled AI systems that monitor environments, detect events, recommend or execute actions, and escalate exceptions within defined operational boundaries.

We can start with an assessment and roadmap or take ownership of the full delivery path, from architecture and model selection to integration, evaluation, deployment, monitoring, and ongoing optimization.