How Klarna’s AI Assistant Resolves 80% of Queries in Under 2 Minutes
Klarna is a global fintech company serving over 85 million active users with payment and shopping solutions, processing 2.5 million transactions daily across more than 45 markets. Facing mounting pressure to scale customer support across global markets without proportional headcount increases, Klarna deployed an AI assistant built on LangGraph and refined with LangSmith that now handles the work equivalent of 700 full-time staff. The result is 80% faster customer query resolution and 70% automation of repetitive support tasks.
Impact
80%
Reduction in average customer query resolution time
~70%
Repetitive support tasks automated
700 employees
Full-time staff equivalent output
2.5 million
Total AI conversations
Challenge
Klarna faced growing challenges managing multi-departmental escalations at scale across 85 million active users, with rising consumer expectations requiring speed, accuracy, and accessibility across global markets without proportional headcount growth.
Solution
Klarna built an AI assistant on LangGraph with a controllable multi-agent architecture for request routing and task handling, using LangSmith for step-by-step observability, prompt optimization, and continuous evaluation against custom metrics.
Tools & Technologies
What Leaders Say
“LangChain has been a great partner in helping us realize our vision for an AI-powered assistant, scaling support and delivering superior customer experiences across the globe.”
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Full Story
Klarna sits at the intersection of commerce and consumer finance, operating one of the world’s most widely used buy-now-pay-later platforms. With over 85 million active users and 2.5 million daily transactions, the company faces a customer support challenge that scales with its business: payments, refunds, and escalations require fast, accurate responses across multiple languages and time zones. Traditional support scaling meant adding headcount, which introduced costs and quality inconsistencies.
The pressure point was escalation management. As Klarna’s user base grew, multi-departmental escalations became harder to route and resolve. Customers arriving with complex payment disputes or refund issues often waited while queries moved through internal chains before reaching the right team. The friction was measurable, and the costs of unresolved escalations compounded across millions of interactions.
Klarna built its AI assistant on LangGraph, using a controllable agent architecture to route requests and handle different task types across a structured multi-agent system. LangSmith provided the observability layer: the team could see exactly how the AI behaved step by step, run experiments against multiple prompts and model configurations, and measure outcomes with custom evaluation metrics. A key collaboration came through prompt optimization — Klarna’s insights directly shaped LangSmith’s meta-prompting feature, which allows users to suggest specific improvements and measure their impact on response quality.
Over nine months, the results shifted the support operation at scale. Average customer query resolution time dropped by 80%. Approximately 70% of repetitive support tasks were automated, freeing human agents to focus on complex, high-value interactions requiring judgment and empathy. With 2.5 million conversations logged, the AI assistant delivers the equivalent output of 700 full-time employees, fundamentally changing the economics of Klarna’s support function.
The broader implication for fintech and consumer platforms is that AI can meet the dual demand of scale and quality in customer support. Klarna’s model — pairing LangGraph’s structured agent architecture with LangSmith’s rigorous evaluation and iteration cycle — demonstrates that automating support at this scale requires not just a capable model, but a disciplined, testable approach to deployment and refinement.
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