Global Engineering.
Deliver software faster with an AI-native software development company

Our business analysts use Claude and specialized AI agents to turn complex project artifacts into functional and non-functional requirements, user stories, and testable acceptance criteria. AI-assisted analysis helps identify dependencies, gaps, conflicting logic, and missing scenarios, while reusable Skills provide consistent instructions and validation rules. Every requirement is reviewed, refined, and approved by a business analyst before becoming part of the project specification.

Globaldev designers integrate AI into their workflow by providing access to product requirements, UX/UI specifications, and design-system context. MCP integrations with tools such as Figma help accelerate interface exploration, prototyping, component design, and consistency checks across screens and states. Designers apply their expertise to refine concepts, validate usability, accessibility, and responsive behavior, and approve every design before it moves into development.

Globaldev architects incorporate AI agents into solution design, using them to explore architecture patterns, technology stacks, integration approaches, system dependencies, and infrastructure configurations. AI also accelerates API design and Infrastructure as Code (IaC), while architects make the critical decisions around security, scalability, performance, availability, and long-term maintainability.

Backend and frontend engineers integrate artificial intelligence into project-aware development workflows, using source code, requirements, specifications, and technical documentation as context. Backend teams use it for codebase analysis, implementation planning, and consistent reuse of established patterns, while frontend teams apply Specification-Driven Development (SDD) to move from requirements to code and unit tests. Engineers review plans before implementation and verify, refactor, and approve the resulting code against architecture and engineering standards.

Globaldev QA engineers integrate AI tools into testing workflows for requirements analysis, test design, test documentation, and test-case creation. Project specifications and established QA standards provide context for building structured test scenarios and supporting automated unit, integration, API, and end-to-end testing. Predefined rules require AI to flag ambiguous requirements rather than make assumptions, while QA specialists review test logic, coverage, and results and retain ownership of exploratory testing, edge cases, and final product quality.

Globaldev DevOps specialists use AI to accelerate work across CI/CD, infrastructure, and observability workflows. AI helps analyze build failures, logs, configuration changes, deployment history, and infrastructure state to identify root causes and prepare remediation steps faster. Infrastructure as Code (IaC), automated quality gates, and version-controlled pipelines keep changes reproducible and traceable, while engineers validate critical changes and retain control over production releases.