Opsfleet designed and deployed a custom multi-agent orchestration framework for Splitit. It runs specialized AI agents in production today and gives Splitit the foundation to extend toward its Agentic Checkout vision.
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Splitit is a global payment technology leader offering white-label Installments-as-a-Service. By tapping consumers' existing credit card limits, it lets merchants offer interest-free monthly payment plans with no new credit application, lifting average order value and conversion for merchants worldwide.
The transition from traditional APIs to agentic workflows is the next frontier in fintech. Opsfleet's expertise in building production-grade AI infrastructure allowed us to transform our 'Agentic Checkout' vision into a secure, scalable reality. We now have the foundational architecture to support autonomous transactions with the high availability and security our global merchants expect.
As commerce shifts toward autonomous buying, Splitit set out to move past the traditional checkout and into Agentic Checkout: embedding its installment logic into the next generation of AI shopping assistants and autonomous procurement systems. That meant production-grade infrastructure for a multi-agent system that handles complex payment logic, eligibility checks, and real-time scheduling on its own, with the enterprise-grade security, full auditability, and scalability that sensitive financial data demands.
Opsfleet's forward deployed engineers (FDE) partnered with Splitit to design and deploy a well-architected AWS environment for autonomous workloads: a custom-built multi-agent orchestration framework with Amazon Bedrock as the primary LLM provider. Specialized agents run in production today, powering operational analytics, automated remediation (such as reauthorizing and retrying failed installment plans), and data workflows, built for the security, reliability, and cost control that enterprise finance demands.
For Splitit, the clearest measure of this project is the business impact: recovered revenue, hours of manual work removed, and predictable AI costs at scale. Recovering 25% of failed installment plans protects revenue that used to depend on manual follow-up. Automatically resolving 80% of recurring incidents frees the team from routine triage. A 30% cut in LLM spend keeps the cost of running AI agents in production predictable as usage grows. Together, these results turn a multi-agent system into a business asset, one that also gives Splitit the foundation to move toward its Agentic Checkout vision.
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