Adyen builds LLM-powered smart ticket routing and support agent copilot with LangChain
The problem
Growing merchant volume and transaction load put rising pressure on Adyen's support teams, and ticket hand-offs between teams were a primary driver of slower response times. The team wanted to scale support capacity through technology without growing headcount.
Workflow diagram · grounded in source
1
Merchant submits support ticket
Trigger
A support ticket enters the routing system to be directed to the right support person based on its content.
▾ source quote
“A smart ticket routing system designed to get a ticket to the right support person as quickly as possible based on content”
2
Theme and sentiment analysis
Ai action
An internal tool analyzes the theme and sentiment of each ticket and dynamically updates its priority based on the user.
▾ source quote
“an internal tool that analyzes the theme and sentiment of each ticket, and dynamically updates its priority based on the user”
3
Route to suited technical expert
Routing
The LLM-driven approach enables merchants to receive support from the technical experts most suited to respond quickly.
▾ source quote
“this LLM-driven approach enables merchants to receive support from the technical experts most suited to respond quickly”
4
Document retrieval from vector database
Ai action
The system finds the most relevant and up-to-date document from a collection of public and private documents stored in a vector database.
▾ source quote
“store them in a vector database with an embedding model that optimized for effective retrieval. The team's first milestone on its way to generating proposed ticket responses was finding the most relevant and up-to-date document from a collection of public …”
5
Copilot generates suggested response
Ai action
An LLM produces a suggested response for support agents through their proprietary copilot.
▾ source quote
“connect to an LLM to produce a suggested response for support agents through their proprietary copilot”
6
Agent reviews and applies suggestion
Human review
Support agents work from their queues with easily-modifiable potential answers to customer inquiries at their fingertips.
▾ source quote
“With the right set of tickets in their queues and easily-modifiable potential answers to customer inquiries at their fingertips, support agents are more efficient and more satisfied”
Reported outcome
Adyen's LLM-driven ticket routing and copilot made support agents more efficient and satisfied, with document retrieval far outperforming traditional keyword-based search and immediately establishing team trust in the new system.
Reported metrics
Support agent efficiency and satisfactionmore efficient and more satisfied
Retrieval vs keyword-based searchfar outperformed traditional keyword-based search
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Adyen's LLM-driven ticket routing and copilot made support agents more efficient and satisfied, with document retrieval far outperforming traditional keyword-based search and immediately establishing team trust in the…
Support agent efficiency and satisfaction: more efficient and more satisfied; Retrieval vs keyword-based search: far outperformed traditional keyword-based search; Time to build document collection: 4 months (source-reported, not independently verified).
How is this ticket triage AI workflow structured?
Merchant submits support ticket → Theme and sentiment analysis → Route to suited technical expert → Document retrieval from vector database → Copilot generates suggested response → Agent reviews and applies suggestion.
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