Fargo Handled More Than 200 Million Requests Without Sending Customer Data to an LLM

Not a pilot. Not a proof of concept. A production-grade generative AI assistant that handled 245.4 million interactions in 2024 alone, more than doubling its original forecast and it did so without ever sending sensitive customer data to a language model.

Most banks are still running internal trials and whiteboarding risk scenarios. Wells Fargo built Fargo, rolled it out inside the app, and scaled it to handle everyday banking tasks via voice and text: bill pay, transfers, transaction lookups, account questions. The tool averages multiple interactions per session, and usage has exploded: from 21.3 million interactions in 2023 to 245.4 million in 2024, totaling 336 million since launch.

Fargo works not because of a flashy model, but because of architecture. Customer speech is transcribed locally. That text is then scrubbed and tokenized by Wells Fargo’s own stack, with a small language model (SLM) detecting PII. Only after that does the system call out to Google’s Gemini Flash 2.0, which extracts the user’s intent and relevant entities never raw input, never names or account data.

“The orchestration layer talks to the model,” said Wells Fargo CIO Chintan Mehta in an interview with VentureBeat. “We’re the filters in front and behind.”