System One: Building an Autonomous FinTech Triage Engine

Want to build a low latency and low cost decision engine for your customer support that requires different action and routes confidently

Your customer support ticketing system gets a request from a customer who wants to pay back a double credit on a transaction—how confidently can it decide to send this to the finance team?

You’re also seeing a ticket from a few users on a certain browser who can’t download this year’s financial statements. Given tax season, how sure is your system that this is urgent and needs quick action?

Because LLMs are autoregressive and uncalibrated, they aren’t the best fit here—and they can cost more and 7- 8 seconds latency. Models like Jev or Kev are probabilistic, which helps make confident decisions with low latency of 70 - 500 milliseconds.

System One (Jev/Kev) as your judgement system, and System Two (LLM) steps in if you need to draft a reply for customers based on their tickets.

Would you like to try System One with a hands-on FinTech Triage Engine project like the one in the below diagram?

Explore the full architecture blueprint, production ready reusable foundation code, complete code implementations, and interactive diagrams in Mastering Jev: Type-Safe System 1 AI in Production book.

Architecting deterministic, sub-second decision layers for mission-critical systems.

• Deterministic Primitives: Replace brittle JSON prompt schemas with Choice, Score, and Noul.

• Sub-Second Latency: Low-overhead System 1 probabilistic inference designed for high-throughput streaming.

• Real-World Capstone: Full architectural implementation covering autonomous FinTech fraud triage and Industrial IoT edge control.