LLMs
Model inference and streaming responses
AI ENGINEERING ORCHESTRATOR
Governed AI.
Connected engineering.
An internal AI platform for daily software-development workflows, connecting developers and GitLab with model access, tools and automated review.
Internal production deployment
The challenge
Developers need AI assistance within their engineering workflow, with authentication, model routing and usage governance managed consistently. Automated review also needs to connect to the development tools the team already uses.
HOW THE WORKFLOW CONNECTS
From request to useful response.
START WITH THE WORKDeveloper / GitLab
Request, code and project context
COORDINATION
AI Engineering Orchestrator
Authentication · Model routing · Usage governance
MCP tools
Connected tools and agentic tool execution
Automation
Engineering workflows and automated review
Project knowledge
Relevant code context and project information
DEVELOP AND REVIEWDevelopment assistance · Engineering actions · Code review
Output feeds into the team’s validation and decision-making.
Conceptual architecture: capabilities are coordinated as the task requires, rather than always running in a fixed sequence.
DEVELOP AND REVIEW
AI assistance across the engineering workflow.
Start with the task and context
A developer request or GitLab workflow provides the starting point. Code context and relevant data help connect the request to the engineering work at hand.
Connect models and tools
The Orchestrator handles authentication and model routing. Model inference can interact with MCP tools to bring code context and tool execution into the workflow, with streaming responses for interactive assistance.
Support development and review
The platform supports daily development assistance as well as automated source-code review. GitLab integration connects those activities to the tools developers already use.
Evaluate the result
The Plan, Execute and Verify diagram illustrates the engineering cycle. AI assistance and review output should feed into the team’s own validation and decision-making before changes are accepted.
IMPLEMENTATION
What we built.
Access and routing
Authenticate access and route requests through a shared orchestration layer.
Tools in the workflow
Connect model inference with MCP and agentic tool execution, including streaming responses.
Engineering integration
Support development assistance and automated source-code review through GitLab workflows.
What this demonstrates
A working internal implementation spanning AI access, orchestration, tool integration and engineering automation. It provides a concrete foundation for discussing similar developer workflows in a customer environment.
FROM INTERNAL USE TO YOUR TEAM
A starting point for
your engineering workflow.
The implementation brings model access, tool connections and engineering automation into one coordinated system.
For a customer project, we would first map the development workflow: who needs access, which repositories and tools are involved, and where AI assistance or automated review would be useful.
That defines the integration scope, model-routing needs, usage governance and validation approach. The goal is a workflow that fits the team’s development process and can be evaluated in practice.
Discuss your developer workflowYOUR NEXT PROJECT