Builder's Desk Labs

A portfolio lab for production-minded software demonstrations built around real business problems—not generic CRUD tutorials.

The projects in this lab are designed to show how I move from an ambiguous operational problem to a clear domain model, safe workflow, testable architecture, and executive-ready product demonstration.

What this lab demonstrates

• full-stack product architecture
• AI agents and human-in-the-loop workflows
• deterministic decision and rule engines
• workflow automation and exception handling
• API and integration boundaries
• operational visibility and system health
• testable domain logic
• secure use of synthetic demonstration data
• incremental delivery through reviewed branches

Featured builds

Project	Business problem	Engineering proof
Supplier Pricing Engine	Suppliers apply different sizing, compatibility, pricing, freight, and margin rules.	Versioned rules, deterministic pricing, explainable calculations, approvals, tests, CI, and a live CPQ demo.
Logistics Control Tower	Last-mile operations need capacity-aware planning, exception recovery, and live visibility.	React control tower, Node API, FastAPI optimizer, SSE events, graceful degradation, resilience scenarios, tests, CI, and Docker services.
STT Operations Command Center	Disconnected systems of record create payroll, billing, reporting, and ownership risk.	Governed integration layer, source-of-truth boundaries, exception routing, executive metrics, typed Next.js prototype, and a 90-day implementation model.

Architecture spine

Each production-style demonstration follows a consistent product spine:

Command Center
      ↓
Workspace
      ↓
Domain Modules
      ↓
Data Registry
      ↓
Integrations
      ↓
System Health

This keeps the user experience tied to the system beneath it. A dashboard is not treated as the product unless its data ownership, decisions, failures, and recovery paths are also modeled.

Engineering standards

A project is not considered portfolio-ready until it can answer these questions:

1. What business problem does it solve?
2. What is real, and what is simulated?
3. Which decisions must be deterministic and auditable?
4. Where can the workflow fail?
5. How does the system recover without duplicating or losing work?
6. What protects the core behavior from regression?
7. How would the prototype evolve into production?

Featured projects should include:

• a 60-second demonstration path
• local setup instructions
• typed contracts and explicit domain boundaries
• automated tests for critical business behavior
• CI or another visible quality gate
• architecture and tradeoff documentation
• clear separation between implemented and planned capabilities
• synthetic data only
• no committed secrets or customer records

Delivery workflow

Client-specific or experimental work is developed on dedicated agent/* branches and enters main through reviewed pull requests.

Business problem
      ↓
Acceptance criteria
      ↓
Small working increment
      ↓
Tests and build verification
      ↓
Reviewable pull request
      ↓
Demo and documented next step

Data and security rules

• Demonstrations use synthetic or publicly safe data.
• Client credentials, private records, and proprietary source material are never committed.
• Environment variables are documented through examples, never real secrets.
• Production integrations begin with read-only or sandbox access where possible.
• AI-generated output is validated before it becomes an authoritative business decision.

About

Built by Kohron Burton, a senior full-stack and AI software engineer focused on enterprise workflows, production AI systems, SaaS architecture, integrations, and operational software.