AI-native underwriting · USA
A US underwriting platform needed scorecard summaries lenders could trust at a glance. We helped them shape the system-prompting strategy and build the prompt-data library that drives consistent, defensible output.
The brief
An AI-native underwriting platform was operating at scale, with lender-facing summaries at the centre of the product. The brief was the next-stage question: how do you keep AI output consistent and defensible as the model and the deal flow keep moving?
System discipline — turn what worked under careful supervision into something the platform could lean on every day, across every deal.
Our work
We worked with their team on the system-prompting strategy: the structure, constraints, and house style that anchor every summary the platform ships.
On top of that we helped them build a library of prompt data — a curated bank of references that every prompt change is checked against before it lands in production.
The outcome
Reference items in prompt library
Underwriting dimensions in scope
From kickoff to production-ready system
[CLIENT QUOTE TBD] — A short, specific sentence from the client about lender confidence in the new summaries.
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