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Make AI truly understand your business code while balancing engineering productivity, security, and compliance.
Cover the entire workflow from coding, debugging, and testing to review, with code completion, unit test generation, and refactoring suggestions that greatly reduce repetitive work so developers can focus on core business innovation.
Connect internal code repositories, technical documentation, and engineering standards so AI output aligns with your technology stack and coding conventions, solving the common problem of generic models that do not understand the business or match your style.
By default, enterprise code and interaction data are not used to train public models. Sensitive path exclusions and end-to-end audit trails are supported to meet MLPS 2.0, GDPR, and industry regulatory requirements.
Support organization-level permission control, layered policy rollout, and visual usage dashboards so large-scale adoption remains manageable and measurable, with administrators able to track team usage and productivity gains in real time.
Five core capabilities to build an enterprise AI hub for software engineering
Enable one-click access to internal GitHub repositories, technical documents, API manuals, development standards, and other knowledge assets to build a dedicated business knowledge space. AI can generate code, answer technical questions, and explain legacy business logic using internal context, producing results that closely fit your technology stack and business scenarios while avoiding irrelevant generic-model responses.
Fine-tune the base model using your full historical codebase so it can deeply adapt to your technical architecture, coding style, and business model. For complex business scenarios, in-house frameworks, and legacy systems, this significantly improves code generation accuracy and usability, enabling deeper integration between AI and your engineering system.
Automatically identify Pull Request changes and generate structured summaries and impact descriptions to help reviewers quickly understand the logic of each update. It also highlights potential risks, performance issues, and standards violations to shorten review cycles and keep code quality consistent across the team.
Cover the full engineering workflow, including real-time code completion in the IDE, chat-based code explanation, debugging, and refactoring, CLI assistance, automatic documentation generation, and mobile code review. It supports mainstream tools such as VS Code, Visual Studio, and the full JetBrains family, and works with most major languages and frameworks including Java, Python, C#, JavaScript, and Go.
Provide an organization-level admin console with SSO, granular permission assignment, layered policy inheritance, and sensitive file or path exclusion rules. Full operation logs are traceable and exportable, can connect to enterprise SIEM platforms, and satisfy regulatory audit and internal compliance requirements, while usage dashboards quantify the engineering value delivered by AI.
Enterprise-grade security standards that make AI usage safe and controllable
Enterprise code, prompts, and business data are not used for GitHub public model training by default. Data can also be configured to remain entirely within the enterprise tenant, reducing data leakage risk at the source.
Backed by security certifications such as SOC 2 Type II, ISO 27001, and ISO 27701, and aligned with leading global data security and privacy requirements including GDPR and MLPS 2.0.
Support exclusions for sensitive files and core code paths, with built-in secret and sensitive data scanning to prevent accidental exposure of credentials, passwords, and other confidential information during development.
Maintain complete records of user actions and interaction logs with support for query, traceability, and export, and integrate with internal security operations platforms to meet regulatory audit and internal risk-control requirements.
More than product licensing, it is end-to-end implementation assurance
Assist with enterprise tenant activation, organizational synchronization, SSO integration, permission framework setup, and baseline policy configuration, completing standard deployment in 1-3 business days so teams can start using it immediately.
Design Copilot usage policies, data retention rules, sensitive information exclusion plans, and audit log frameworks based on your industry profile and compliance requirements, supporting regulated sectors such as government, finance, and healthcare.
Integrate with internal DevOps platforms, knowledge systems, CI/CD pipelines, and project management tools so AI capabilities can be embedded into the existing engineering workflow for seamless collaboration.
Provide developer onboarding and administrator operations training, along with internal best-practice guides and usage standards. Usage data is reviewed regularly to deliver optimization recommendations and support strong adoption with measurable real-world results.
Provide dedicated technical consultants for day-to-day issue response, version update coordination, and troubleshooting, while also connecting to Microsoft support channels to keep enterprise operations stable.
Built for engineering scenarios across industries to unlock technical productivity
Accelerate product iteration, shorten release cycles, and support fast business innovation
Meet strict compliance requirements, protect code and data security, and improve efficiency for core systems development
Preserve industrial software and embedded-code knowledge, improve engineering standardization, and shorten product development cycles
Align with MLPS and data security requirements, standardize engineering practices, and improve the efficiency of digital transformation initiatives
Support parallel development across multiple business systems, reduce cross-team collaboration costs, and enable rapid business expansion
Standardize teaching code practices, lower the barrier to programming, and improve the development efficiency of research projects
