Trusted, traceable enterprise AI, optimised for cost.
An open, six-month study building a verifiable way to reduce the token cost of AI systems that read financial documents, while keeping every answer accurate, cited to its source, and willing to say when it does not know.
What this is, in one minute
Enterprise AI agents are quietly burning enormous, unbudgeted token costs, and most teams cannot cut that cost without quietly losing quality. This project builds and rigorously proves a way to cut it while keeping answers trustworthy.
The technical depth
The work targets retrieval-augmented question answering over financial filings, where a wrong number is not an embarrassment but a liability. It treats token cost as a first-class objective, gates every change behind a statistically honest evaluation, keeps a human in the loop before any change is adopted, and makes every answer traceable to its source with a calibrated willingness to abstain. It is conducted as open, reproducible Design Science Research over six months.