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The AI Employee for Banks

Kastle is building the AI employee for banks. Their agents automate operations across mortgage servicing, consumer lending, and other parts of the bank back office. Today they work with 10 of the top 25 mortgage servicers and have processed more than $2 billion in transactions using AI agents. They just raised a $24M Series A, two years after going through YC. In this episode of Founder Firesides, Kastle founders Rishi and Nitish sat down with YC's Diana Hu to share how they went back to zero one month before Demo Day and found their way to one of the hardest places to deploy AI: banking. They talk about finding their first customers and what it takes to make AI agents reliable enough to handle real financial transactions. https://www.kastle.ai Chapters: 00:00 — Intro 00:05 — What Kastle Does 01:00 — Why Banks Need AI Employees 02:50 — Going Back to Zero Before Demo Day 05:00 — Finding the Idea for Kastle 06:43 — Landing Their First Customer 08:18 — Moving Into a Customer’s Office 08:59 — The Future of AI Employees in Banking Apply to Y Combinator: https://www.ycombinator.com/apply Work at a startup: https://www.ycombinator.com/jobs

Diana HuhostRishiguestNitishguest
Sep 18, 202611mWatch on YouTube ↗

At a glance

WHAT IT’S REALLY ABOUT

Kastle’s AI “employee” automates bank operations without core replacements

  1. Kastle AI builds an “AI employee” for banks that automates operational workflows, starting with mortgage servicing and expanding into broader consumer lending operations.
  2. The founders chose an AI-employee approach to deliver value without forcing banks to rip and replace legacy core systems, which can take 5–10 years in regulated environments.
  3. After abandoning an earlier YC-batch idea that reached about $4K MRR, they found a mission-critical wedge by discovering the scale of phone-based mortgage payments handled by U.S. contact centers.
  4. Early go-to-market traction came from conference-driven customer discovery and a pitch competition that generated inbound, leading to first customers just before Demo Day.
  5. Kastle claims major scale growth—crossing $2B in transactions processed via AI agents—and aims to become a cross-institution coworker that automates high-volume tasks while escalating complex cases to humans.

IDEAS WORTH REMEMBERING

5 ideas

They’re selling adoption speed and reliability in a change-averse industry.

Kastle positions itself as an “AI employee” that plugs into existing bank workflows, avoiding multi-year core-system replacements while still delivering automation benefits quickly.

Mortgage servicing is the credibility wedge for broader bank operations.

They started in mortgage servicing—an especially regulated, operationally heavy environment—and used that as the proof point that autonomous agents can perform real work (payments, servicing, collections) safely.

They optimized for existential customer value over early revenue traction.

The founders intentionally killed a growing but non-essential product (~$4K MRR) because it felt “nice-to-have,” then re-ran discovery to find a mission-critical pain point customers couldn’t live without.

A surprising “legacy” channel (phone payments) created the breakout opportunity.

A key discovery was that ~10M Americans still make mortgage payments by phone monthly, handled by onshore human agents due to regulatory and outsourcing risk—creating a large, repetitive, automatable workflow.

Distribution and timing mattered as much as product readiness early on.

Their first customer momentum came from a pitch competition that generated inbound even after a live demo malfunction; shortly after, they secured early customers right before Demo Day.

WORDS WORTH SAVING

5 quotes

So Kastle is an AI employee that automates all the operations inside a bank.

— Rishi

We recently crossed, uh, $2 billion in transactions processed- ... using AI agents.

— Rishi

It's very hard for such large regulated industries to completely replace their core systems.

— Rishi

We couldn't see ourselves, like, building a really large company, uh, on that idea.

— Rishi

So to actually make them live, we moved the entire company to Phoenix, and we moved into their ba- offices and, you know, k- uh, didn't come back until they were live.

— Rishi

AI employee concept for banksMortgage servicing automationPhone-based mortgage payment workflowEnterprise change management constraintsPivoting before YC Demo DayConference-led customer discoveryLive deployment inside customer offices (Newrez)

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