Engineering the Future of Fleet & Distribution with AI-DLC
How Brain Station 23 used AI-DLC to plan, build, test, and validate complex logistics workflows while increasing demonstrable delivery capacity by approximately 4x.
Platforms
Web, Enterprise Management Platform, Admin Portal
Technology Used
Cursor AI IDE and Agents, Playwright MCP
Industry
Distribution, Logistics, Supply Chain
Tags
AI-DLC, Fleet Management, Logistics, Enterprise Software, Test Automation
4x
Increase in demonstrable delivery capacity
27
Database migrations
23
Story-level end-to-end testing sub-tasks completed within a sprint
10
Production bugs fixed in-sprint during the measured period
72
Commits in a measured two-week delivery window
195
Files changed
31,033+
Lines of code added
9
Applications involved across the measured delivery
Overview
An enterprise organization required a unified platform to digitize and standardize complex distribution, transport, fleet, and logistics operations.
The Distribution and Fleet Management System was designed to centralize transport planning, vehicle operations, vendor coordination, quality control, and financial processes within an enterprise-grade platform.
Brain Station 23 applied AI-DLC throughout the development lifecycle, from requirements planning and implementation through to end-to-end testing and validation.
The approach combined AI-assisted development with a structured project context repository and Playwright MCP for testing against the running application.
This allowed the team to handle complex operational workflows while increasing the amount of functionality that could be delivered and demonstrated within a sprint.
Business Challenges
Complex operational workflows
The platform had to represent real-world logistics operations where a single transport order could involve multiple loading or unloading points, different operational states, vendors, yards, gates, scales, and role-specific permissions.
A simple one-to-one model between a yard visit and a loading or unloading point did not reflect actual depot operations.
Real depots could split a load across multiple bays, requiring the software to track each assignment and prevent a transport order from progressing before all required work had been completed.
Permission and access-control edge cases
The system contained complex role and permission rules.
Some permission issues only appeared when a specific combination of user role, working site, URL, or operational context was tested.
For example, a tenant administrator without an assigned point of interest was expected to receive restricted access to certain yard operations. A wildcard permission path could nevertheless allow an Add URL to remain accessible.
The issue was difficult to detect through isolated unit or list checks.
Keeping product rules aligned with implementation
Business rules were distributed across stories, scenarios, admin querysets, and API services.
This created opportunities for the documented requirements and actual application behaviour to diverge.
The team needed a process that could continuously verify whether implemented functionality matched the intended operational rules.
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Solution Provided
AI-DLC across the development lifecycle
AI-DLC was integrated into the entire development workflow:
Plan → Build → Test
During planning, AI helped break requirements into implementation tasks, identify dependencies, and establish the technical approach.
During implementation, AI generated and refined code according to the agreed plan.
During testing, AI was used with Playwright MCP to execute end-to-end scenarios directly against the running application.
This made testing part of the AI-DLC development loop rather than a separate activity at the end of implementation.
Structured project context
A dedicated context repository was maintained as a Git submodule within the backend repository.
The repository used a structured directory system to give AI agents project-specific knowledge instead of relying solely on general programming patterns.
Multi-point yard operations
One of the most valuable use cases involved changing the transport-order yard visit model from a single assignment to multiple loading and unloading point assignments.
AI traced the relevant command center, loading, gate, and scale specifications and the existing implementation.
It then supported the redesign of the visit model around multiple BayAssignment records and an “all points completed” rule.
The administrative forms were also updated to support repeatable loading/unloading point assignments without breaking existing single-point workflows.
This brought the digital workflow closer to actual depot operations and prevented downstream gate or scale processes from progressing while work remained open at another point.
AI-assisted end-to-end testing
Playwright MCP was used to execute complete operational scenarios.
When a test failed, the failure was treated as evidence rather than dismissed as a test flake.
AI could then inspect the failure, trace the relevant code and data, identify the root cause, propose a fix, and generate or update tests.
For example, an unloading workflow exposed a unit-of-measure mismatch between product master data and purchase/requisition transaction data.
The team confirmed that the incorrect unit had already been persisted in the database. AI then traced the mismatch and supported the implementation of a centralized unit mapping function, along with automated tests.
The affected workflow was then executed again to validate the durable fix.
Human validation and control
The team did not treat AI-generated output as automatically correct.
Controls included:
- Actual command output and logs as verification
- Manual validation checkpoints
- Scoped task boundaries
- Diff reviews
- Source-code grounding
- Official documentation checks for unfamiliar APIs
- Architecture and coding rules
- Smaller development tasks
- Screenshots and error logs
- Automated testing
- Security scanning
- Tech Lead and developer review
The guiding principle was to treat AI output as a draft requiring verification, rather than as a finished deliverable.
Impact
- Increase demonstrable delivery capacity by approximately 4x, reaching 90-110 story points per delivery cycle compared with 20-35 previously.
- Complete 23 story-level E2E testing tasks within a sprint, giving the team broader validation of real operational workflows.
- Resolve 10 production issues in-sprint, allowing defects to be investigated and addressed within the same delivery cycle.
- Deliver 72 commits across a measured two-week period, covering 195 changed files and more than 31,000 lines of code.
- Use AI-assisted testing to identify issues that could remain hidden when individual components were tested in isolation.
From Faster Coding to Faster Delivery
The value of AI-DLC on the project extended beyond code generation.
AI supported planning, architectural analysis, implementation, debugging, end-to-end testing, and validation.
More importantly, the process connected these activities into one continuous workflow.
A requirement could be planned, implemented, tested against the running application, investigated when it failed, and refined using the resulting evidence.
That allowed the engineering team to increase delivery capacity while maintaining the human review required for complex enterprise software.
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