Kepler CEO on Verifiable AI for Finance
Kepler CEO Vinoo Ganesh explains how the company builds verifiable AI for finance, focusing on atomic provenance and scope determinism.

Visual TL;DR
LLMs predict next token, finance needs deterministic certainty for every number
From the article 4 mentionsIn the complex world of finance, where every number must tell a verifiable story, the probabilistic nature of large language models presents a significant challenge.
numbers are worthless until their exact origin and derivation can be proven
analysts still put in hours because off-the-shelf LLMs lack robust safeguards
From the article 2 mentionsHe elaborates on why human analysts still perform crucial tasks.
company builds AI systems designed for deterministic demands of financial sector
From the article 9 mentions"Why analysts still put in the hours" is because current AI models, while brilliant at synthesizing information, falter when exact, verifiable figures are needed.
ensuring every figure's origin is traceable back to its fundamental source
From the articleAtomic Provenance: This tenet ensures that every number extracted is directly tied to its original source.
defining precise boundaries for AI operations to prevent unverified outputs
From the article 2 mentionsScope Determinism: This principle strategically deploys AI where it is most effective, on non-deterministic tasks.
Kepler's competitive advantage lies in its robust verification capabilities
From the articleThe ultimate goal for Kepler, Ganesh explains, is to build a grounded system where the competitive advantage, or "edge," comes not from generating novel content, but from rigorously verifying existing information.
every figure is traceable, trustworthy, and fully auditable for regulatory compliance
From the article 2 mentionsVinoo Ganesh, CEO of Kepler, sat down to discuss how his company is building AI systems designed for the deterministic demands of the financial sector, ensuring that every figure is traceable and trustworthy.
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Written by
Daniel SingerEditor, StartupHub.ai
Daniel Singer is the editor of StartupHub.ai, a technology expert and thought leader on AI and its applications across sectors, from fintech and healthcare to developer tooling and consumer software. He writes and tests the tools covered here thoroughly and regularly, and built StartupHub.ai to give founders, operators and buyers a clearer read on what they are actually being sold.