Send Payments, a global payment infrastructure provider serving Australian customers worldwide, has elevated their operations by building an interconnected AI agent workforce. Led by CTO Ryk Neethling and Head of Product Eldert Bongers, the company partnered with Relevance AI to create specialized agents that deliver 24/7 customer service, automate compliance tasks, and give back 40 hours per week to their team.
The Challenge: Manual Processes in a Global Business
Send Payments faced operational challenges that many global companies struggle with.
The manual lead qualification process was particularly painful, requiring account managers to understand lead context before any meaningful interaction could happen. With customers spread across different time zones, immediate response became impossible during off-hours.
The Solution: Partnering with Relevance AI for Customization
Send Payments discovered Relevance AI through their BDR agent “Bosh” and immediately saw the potential across other department functions. After reviewing all available documentation, the leadership team realized this wasn’t just another generic automation tool.
The platform’s ability to understand intent and make intelligent decisions based on changing context, set it apart from basic automation tools.
The Results: Multiple AI Agents Delivering 24/7 Operations
- 40 hours saved weekly
- 24/7 global operations coverage
- Thousands of conversations automated
Building an Interconnected AI Ecosystem
The real power emerges when individual agents connect to automate entire business processes, moving beyond single-task automation to comprehensive workflow management.
What’s Next: AI-First Transformation
Send Payments has moved beyond viewing AI as a potential solution to adopting an AI-first mindset across all operations. The shift goes far beyond incremental improvements - it’s fundamentally redefining how the business operates.
The company can now deliver solutions without waiting for traditional development cycles, enabling rapid response to business needs. Their approach has become: if you can write a job description for a task, you can create an agent to handle it.