Kastle

Kastle launched their MVP with a single, narrowly-scoped capability: AI voice agents that collected mortgage payments over the phone. ​​‌‌‌‌‌‌‌​‌‌​​‌​​​​​​‌‌​‌‌‌​​​‌‌Rather than building a full platform addressing multiple servicer pain points, Rishi and Nitish deliberately excluded loan qualification and borrower verification features—despite knowing these were valuable—to ship within eight weeks. This constraint forced them to perfect call handling, payment capture, and integration with existing servicer systems before expanding. They deliberately left out complex decision trees and multi-language support, accepting that early customers would need workarounds. The focused scope proved validating: their first three pilot customers reported 40% reduction in payment collection call times within two weeks, and one servicer immediately requested expanded deployment across their entire portfolio. This early traction on a single use case gave them credibility to raise their seed round and gradually layer in verification and qualification features. The execution discipline—shipping fast with intentional omissions—created momentum that attracted both customers and capital, though it meant early adopters experienced feature gaps that required manual workarounds.

Kastle charged mortgage servicers per completed call, starting at $8-12 per interaction, directly aligning costs with value delivered. Before committing customers, the founders validated willingness-to-pay through conversations with servicers who faced $15-25 labor costs per phone interaction. This gap signaled strong demand. Their revenue model relied on volume—as servicers processed thousands of borrower calls monthly, Kastle's per-call pricing created predictable, scalable revenue. Early validation came when servicers immediately agreed to pilot programs without negotiation, and several committed to multi-month contracts after seeing 40% cost reduction on payment collection calls. The fact that customers didn't request discounts or alternative pricing structures confirmed the model resonated. Rishi's experience scaling Redfin's mortgage business to $6M in nine months meant he understood servicer economics intimately, enabling confident pricing from day one. Customer retention and expansion deals validated that servicers actually paid and found sufficient ROI to increase usage.