Interline

Overview

Interline Route Discovery

Help operations teams quickly identify optimal airline route combinations across partner airlines. At Teleport, the operations team regularly needs to identify interline cargo routes across AirAsia and partner airlines to transport shipments globally.

However, the existing process relied heavily on manual Google Sheets tracking, making route discovery slow, error-prone, and difficult to scale.

I led the design of a centralized Interline Route Discovery platform that allows the operations team to search, compare, and identify optimal routes within minutes.

Interline Route Discovery

Help operations teams quickly identify optimal airline route combinations across partner airlines. At Teleport, the operations team regularly needs to identify interline cargo routes across AirAsia and partner airlines to transport shipments globally.

However, the existing process relied heavily on manual Google Sheets tracking, making route discovery slow, error-prone, and difficult to scale.

I led the design of a centralized Interline Route Discovery platform that allows the operations team to search, compare, and identify optimal routes within minutes.

Problem Statement

Before this solution, the operations team managed interline routes manually.

The workflow looked like this:

Manually track flight availability and routes using Google Sheets
Cross-check AirAsia and partner airline schedules
Identify potential multi-leg routes manually
Continuously update sheets when schedules changed
Compare multiple airline combinations manually

Challenges

> Manual data management
Flight availability and routes had to be constantly updated manually.

> Slow decision making
Finding the optimal route could take 30–40 minutes.

> Cognitive overload
Users had to mentally calculate route combinations across multiple legs.

> High error risk
Manual data entry increased mistakes.

> Poor scalability
As partner airlines increased, spreadsheets became impossible to manage efficiently.

Research & Discovery

To deeply understand the workflow, I conducted several discovery activities.

User Interviews (Operations Team)
The primary users were Teleport Operations team members responsible for identifying and coordinating interline cargo routes.

? Questions

• Walk me through how you currently identify an interline route
• Which part of the process takes the most time?
• What information do you need before confirming a route?
• How do you compare different airline combinations?
• What would make this process faster for you?

To deeply understand the workflow, I conducted several discovery activities.

User Interviews (Operations Team)
The primary users were Teleport Operations team members responsible for identifying and coordinating interline cargo routes.

To deeply understand the workflow, I conducted several discovery activities.

User Interviews (Operations Team)
The primary users were Teleport Operations team members responsible for identifying and coordinating interline cargo routes.

Vision for Scale

This self-initiated prototype serves as the blueprint for an Agoda AI Hub. By proving that we can route intents—from searching for a "Boutique Hotel in Bali" to "Canceling Booking AGD-78234"—within one interface, we move from being a booking site to becoming a traveler’s intelligent companion.

Process

To ensure the Agoda AI Hub was both functional and visually consistent, I utilized a modern AI-driven development stack. Instead of a traditional linear handoff, I built a circular workflow using Claude + Figma MCP.

1. Design-to-Code (Figma → HTML/CSS)

The Action: I used the Figma MCP to allow Claude to "read" my design components directly from the Figma canvas.
The Result: Claude generated clean, semantic HTML and modular CSS. This ensured that the chatbot UI—the message bubbles, property cards, and agent status indicators—matched the Agoda brand identity perfectly without manual CSS tweaking.

2. Code-to-Design (Logic → Figma)

The Action: To visualize the multi-agent architecture (the Manager, Guardrails, and Property agents), I prompted Claude to generate Figma layouts based on the JavaScript logic I wrote.
The Result: A live design document that stayed in sync with the actual code structure. This allowed for rapid prototyping of the "Plug-and-Play" UI for future agents (like the Flight Agent).

3. The "Agile Sandbox" (Simple HTML/CSS/JS)

Strategy: I intentionally chose a Vanilla HTML/CSS/JS stack rather than a heavy framework (like React or Angular).
Reasoning: * Portability: The project can be opened in any browser instantly without npm install or complex build steps.
- Speed: It allows for immediate testing of the Orchestrator’s routing logic.
- Demonstration: It proves that complex AI agent logic doesn't require complex infrastructure—it’s about the intelligence of the routing.