Galileo

Galileo's Luna Studio turns slow and expensive LLM-as-a-judge evals into live guardrails at scale. The same trained model scores your evals and guards your runtime — in your environment, on your data, in days. In this 2:50 launch film, we walk through training a custom Luna SLM judge end to end: - Start a training run on a common metric (input toxicity) - Bring your test set in from Galileo - Generate synthetic training data with feedback loops on the seed rows - Fine-tune inside your environment, on your own GPUs, with no data egress - Register the trained metric back into Galileo as an eval and a runtime guardrail The result is a low-latency, low-cost SLM judge that runs up to 98% cheaper and 20× faster than an LLM-as-a-judge — fit to your traffic, owned by your team, ready to ship. Luna Studio is built for AI engineers, data scientists, and ML platform teams shipping GenAI agents to production at scale. It runs in your VPC, on-prem, or air-gapped environment — wherever your data already lives. Two surfaces ship together: a guided UI for AI engineers paired with an SME, and an advanced SDK for data scientists who want full hyperparameter control. Already in production with leading enterprise AI teams. 📌 Chapters 0:00 Why Luna Studio 0:18 Start a training run · input toxicity 0:40 Bring your test set in from Galileo 1:05 Generate synthetic training data 1:35 Feedback loop on seed rows 1:55 Generate the final training set 2:05 Fine-tune in your environment 2:30 Register the trained metric 2:45 Recap 🔗 Learn more Luna Studio overview → https://galileo.ai/luna-studio Eval Engineering → https://galileo.ai/evalengineering Book a demo → https://galileo.ai/contact-sales Docs → https://docs.galileo.ai Luna 2 research → https://arxiv.org/abs/2602.18583 📡 Follow Galileo LinkedIn → https://www.linkedin.com/company/galileo-ai X → https://x.com/rungalileo GitHub → https://github.com/rungalileo #LunaStudio #AIEvaluation #LLMasJudge #AIGuardrails #Galileo

Your agent breaks in production. You fix it. Two months later, the same failure quietly comes back. The input that broke it? Gone. That's not a debugging problem. It's a missing dataset. In this 2-minute demo, see how Galileo turns low-scoring production traces into curated datasets you can test against. Filter a live logstream by Context Adherence score, inspect the trace where the agent hallucinated an S&P 500 price, and capture it to a regression suite with one click. Every failure, every edge case, every response worth protecting lives in the Dataset Store and grows with every debug. Built for teams running AI agents in production who are tired of fixing the same bug twice. What you'll see: - Filtering production traces by Context Adherence score - Diagnosing a tool-output vs. agent-response hallucination - Copying traces into a new or existing dataset - Using the Dataset Store as a regression suite for prompt and model changes 🔗 Try it on your own agent: https://galileo.ai 🔗 Docs: https://docs.galileo.ai 🔗 Sign up free: https://app.galileo.ai/sign-up 🔗 Follow Galileo on LinkedIn: https://www.linkedin.com/company/galileo-ai 0:00 Your agent breaks. The input that broke it is gone. 0:15 That's not a debugging problem. It's a missing dataset. 0:22 Live logstream with metrics on every trace 0:32 Filter for traces below 70% on Context Adherence 0:42 Click into a trace to see what went wrong 0:50 The hallucinated S&P 500 price 1:02 Select traces and Copy to Dataset 1:15 Create a new dataset and name it 1:20 The Dataset Store — every failure worth protecting 1:25 Agents don't stay still. Curated datasets keep up. 1:40 Learn more at Galileo.ai