Kris Bennatti and Hudson Labs are now giving a no-hallucination guarantee, something she says no other financial AI offers. The reason it's rare is the uncomfortable part: her team found state-of-the-art models return the wrong financial number about 30% of the time once you go past a few reporting periods.
Kris, Brett and Khe get into why precision is still a difficult problem, how a forensic risk score turns fraud signals into a hard number, and why she thinks 2026 is "integrate or die."
"I believe we're the only people on the market that are willing to do a no-hallucination guarantee, which does give you a bit of a sense of how much risk there is in the average financial AI software." — Kris Bennatti, CEO of Hudson Labs
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Timestamps:
[00:00] Intro [00:40] Meet Kris Bennatti, CEO of Hudson Labs [01:22] From Pre-LLM Filings to Founding Hudson Labs [04:16] Why Precision Is Finance AI's Hardest Problem [05:35] The Test: Wrong Numbers 30% of the Time [06:29] Launching the No-Hallucination Guarantee [10:20] Why NotebookLM Feels Better (It's the Search) [12:01] Vector Databases vs. Just Using Claude [13:36] Screening for the "Most Stressed-Out CEOs" [14:16] Have MCP and Connectors Fixed Accuracy? [19:50] From Prompts to Skills [20:50] MCP vs. CLI [23:04] Integrate or Die: The 2026 Business Model [26:52] Where Generalist Models Catch Up (and Where They Won't) [33:36] Inside the Forensic Risk Score [38:47] The Track Record: Score 70+, a 1-in-3 Chance of SEC Action [44:17] The Cost Problem: $100 for Hudson, $13K on Opus [46:26] Fable, Co-work, and the New Token Economics [51:20] The Guidance LLMs Still Miss (It's the Verb Tense) [54:05] Where to Find Kris & Hudson Labs
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