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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