How Kepler Built Verifiable AI for Financial Services — Vinoo Ganesh
Kepler’s pattern keeps LLMs away from numeric mutation: agents choose sources and steps, while deterministic systems persist, calculate, and reject unverifiable values.
Kepler separates citation from verification: a source shows where information came from, while verification deterministically checks that a value is correct. Its extraction model reached **94% accuracy**, but the remaining errors were still unacceptable for financial work.
For high-stakes agents, let the model plan and select references, then hand numeric I/O and calculations to deterministic tools. Keep an **atomic provenance ledger** and remove any value that cannot pass an independent check.
Kepler separates citation from verification: a source shows where information came from, while verification deterministically checks that a value is correct. Its extraction model reached **94% accuracy**, but the remaining errors were still unacceptable for financial work. For high-stakes agents, let the model plan and select references, then hand numeric I/O and calculations to deterministic tools. Keep an **atomic provenance ledger** and remove any value that cannot pass an independent check. Verification does not establish one universal conclusion. It proves that the replayable path followed an organization’s own rules, and the talk leaves personalization of those rules and ontologies as the next major problem.
This sharpens reliability for financial agents by separating traceability from correctness: citations are insufficient unless numeric inputs and calculations can be independently replayed and invalid values removed. It reinforces deterministic tool boundaries and atomic provenance, while narrowing formal verification claims to compliance with an organization’s encoded rules rather than universal truth. The 94% extraction result explains why residual model error remains operationally unacceptable.