How do you know whether an AI agent is doing its job or quietly failing in production?

Galileo is building the trust layer for AI. Its evaluation and observability platform is how enterprises measure whether the output of an LLM or an agent is good or bad.

Galileo started before "LLM" was even a word. When Atin showed his prototype to Stanford's Chris Ré, his own first question was "what is a language model?" Today its customers include Reddit, Airbnb, P&G, Comcast, and six of the Fortune 50.

Atin spent a decade in big tech before co-founding Galileo with Vikram Chatterji in early 2021. He worked on the knowledge graphs behind Siri at Apple, then became one of the leads and architects of Michelangelo, Uber's AI platform, that hosts thousands of models across pricing, ETA, and demand.

That Uber experience taught him the lesson the whole company is built on; that in AI, observability and evaluation are the real bottleneck, and bad data is catastrophic.

As ChatGPT turned every AI output into something a user sees directly, the measurement problem went from academic to mission-critical. So Atin made a contrarian bet: instead of using giant LLMs to judge other LLMs, Galileo built Luna, small 1-3B parameter models that run evals at breakthrough latencies of 100 milliseconds and below.

If you are excited about how AI actually gets shipped, trusted, and controlled inside real enterprises, this episode is for you.

00:00 - Trailer
01:14 - From India to Apple, Uber, and Galileo
01:34 - Where the name "Galileo" came from
02:38 - Building Siri's early knowledge graphs at Apple
03:29 - Becoming an architect of Uber's Michelangelo
05:15 - Why every AI output is now mission-critical
06:45 - How Atin and Vikram zeroed in on Galileo
07:42 - "What is a language model?"
09:38 - Building the world's first feature store at Uber
11:27 - Language models and tokens, explained simply
14:19 - Where the observability insight came from
15:53 - Quantifying uncertainty and hallucinations
16:36 - The first customers and first use case
19:15 - How the product evolved from a data scientist tool
23:18 - Why ChatGPT changed everything for Galileo
23:57 - The enterprise AI adoption curve, 2021 to 2026
26:35 - Why they built the Luna model
28:32 - Turning LLM "writers" into "calculators"
28:51 - Attacking the latency problem
31:48 - Luna: the modeling and infrastructure innovation
33:09 - What evals are, and why they blew up
34:26 - The case for small language models
36:58 - What "general reasoning" really means
40:39 - AI usage is exploding — and why that matters
43:08 - Online vs offline: the "it worked on my machine" problem
44:33 - The evals flywheel and evals-driven development
46:56 - Galileo in a nutshell
47:39 - What real agents in production look like today
49:30 - A sales intelligence platform, powered by Galileo
50:47 - The agent control product
52:12 - Building GTM as a hardcore engineer from India
54:43 - Garbage in, garbage out: nailing the ICP
55:42 - How the pitch changed from customer 1 to 20
57:27 - Why Atin switched from CTO to CPO

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This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.

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