Prototyping an AI support engine - Case study - Part 1
How I designed an LLM-powered triage system to transform customer support for a national sandwich chain
Project Overview
Company: SubWay Express (hypothetical national sandwich chain)
Challenge: Handle 15,000+ monthly customer complaints across a fragmented support system
Solution: AI-powered triage assistant that routes, categorizes, and resolves issues intelligently
The Problem Space
A hypothetical fast food chain “SubWay Express” operates 2,500+ locations nationwide with a mobile app used by 3.2 million active customers. Their customer support was drowning in volume:
Current State Challenges
12-hour average response time for non-urgent issues
35% of tickets misrouted to wrong departments
Agent burnout from repetitive questions (order status, refund requests)
Inconsistent experience across email, in-app chat, and phone
Poor first-time user retention due to unresolved complaints
The Data That Mattered
Using Claude I went about researching public support ticket repositories that outlines general support issues in the food app categories. I found:
68% were routine issues (missing items, wrong orders, app bugs) that could be resolved automatically
22% required human empathy but were simple (refunds, credits, apologies)
10% were complex (allergen concerns, legal, recurring problems)
The insight: Most customers didn't need a "conversation" - they needed fast resolution. But the current system treated every complaint the same way.
Research and Personas
To build out key persona archetypes for this type of a simulation I used Manus and Claude to help deep research through customer service personas specifically within the food tech space to understand pain points. Using the research provided I build out four high level personas to help guide decision making and scoping:
Persona 1: The Loyal Regular (Sarah, 34)
Orders 3-4x per week via app
Issue: Missing item in curbside pickup
Need: Quick acknowledgment and immediate resolution, no friction
Frustration: "I just want my $3 back, why do I need to write an essay?"
Persona 2: The First-Timer (Marcus, 22)
Downloaded app for a promotional deal
Issue: 45-minute wait time, cold food
Need: Feeling heard, compensated generously, convinced to try again
Frustration: "This was my first order and it was terrible. Why would I come back?"
Persona 3: The Serious Concern (Jennifer, 41)
Has a food allergy, received wrong order
Issue: Safety risk, wants accountability
Need: Immediate escalation to management, documentation, reassurance
Frustration: "A chatbot can't handle this. I need to speak to a person NOW."
Agent Perspective (David, Support Team Lead)
Pain Point: "We waste time asking for order numbers, locations, basic details customers already gave us"
Need: Context before they even open a ticket
Frustration: "Half my day is copy-pasting the same apology for app crashes"
Based on the research, I also established core principles to guide the design:
Transparency over deception - Never pretend the AI is human
Speed over conversation - Resolve, don't chat
Graceful degradation - When uncertain, route to human immediately
Empower agents - Give them superpowers, not replace them
Safety first - Serious issues (allergens, injuries) bypass AI entirely
The Solution
High Level Architecture
Three Resolution Paths
Based on the tree above, I provided 3 broad resolution paths that would typically most cases. The goal was to be broad enough at the start to then refine these paths further as we learned more:
Path 1: Instant Resolution
Issues the LLM can resolve without human intervention
Examples: Refunds under $15, order status, app troubleshooting
Response time: Under 60 seconds
Path 2: Assisted Human Support
LLM extracts context, suggests resolution, routes to appropriate agent
Agent receives pre-filled case summary
Response time: Under 2 hours
Path 3: Priority Escalation
Safety concerns, legal issues, high-value customers
Bypass AI, immediate human connection
Response time: Under 15 minutes
The Interface
As this is a high level protoype I wanted to keep the initial interface as simple as possible. I also tasked myself with solely trusting Gemini to help with the overall design implementation on Gradio as I wanted to use that as my source for hosting this project.
I wanted to ensure a quick and easy design process which meant that I would create high level images to give Gemini guidance on layout as what copy and components I expected to see on the interface.
Initial Contact Screen
I wanted to stay away from designing a traditional chat interface since most of the times these types of interactions imply a certain level of interactivity that many times fails users because they expect a human styled interaction but at the end of the day are simply just talking to an AI agent. To avoid humanizing the AI and make sure there is a clear delineation between when a Human is responding to you (in the negative paths) versus when a agent is responding to you, I designed a simple guided input experience. This would also make it more efficient for the user to put in all their details at once.
I then mocked up a quick post it wireframe for Gemini to work with:
Understanding Conformation
In order to provide a sense of transparency and safety for the user, it was important to determine what the agent has understood from their input. Providing a quick high level summary and asking them to confirm its accuracy would ensure that they have control over submissions while also providing them a visual confirmation that their case is covered entirely.
Resolution Screen
The resolution path chosen by the agent would determine the need for a resolution screen. If it was an instant or easily solved inquiry, the basic resolution would highlight the actions the agent has taken and question the user about their overall satisfaction with what has been done. They would also still have the ability to get additional support if needed.