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Overview
Monte Carlo is a San Francisco company founded in 2019 that created the data observability category. Its platform learns the expected behaviour of tables and pipelines, detects freshness, volume and schema anomalies and traces the lineage of a broken dashboard back to its cause. It has extended monitoring to the vector stores and unstructured data feeding generative AI systems.
News
The Open vs. Closed AI Debate Misses the Point: Most Orgs Can't Measure Either
The discussion around enterprise AI often focuses on the choice between open-weight models, like Meta's Llama and models from Mistral, vs. closed frontier models, such as Anthropic's Claude and OpenAI's GPT models. While closed frontier models promise sta
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Report
LLM Evals: What They Are and How to Get Started
TL;DR: LLM evals are a structured way to grade an AI system's outputs against a standard of quality, the discipline that turns the assumption the AI is working into proof. Every eval has three components: a test case, a response, and a grader (code-based,
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Prompt Versioning: Why Your AI Prompts Need the Same Rigor as Your Code
If your team is building anything powered by LLMs, there's a good chance your prompts have already changed a dozen times since launch. Maybe someone tweaked a system message to fix a tone problem, or maybe an engineer added a new instruction to stop the m
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How We Measure What AI Says About Us: An LLM Visibility Audit
We built a category once and could see ourselves winning. Building the next one, we couldn't see the scoreboard. The first category we built, we could see ourselves winning. Data observability didn't exist as a market until we helped make it one, and the
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How AI Anomaly Detection Catches the Problems Your Tests Miss
Your test suite is a record of every failure you've faced. You wrote a test because something broke once, and you never wanted it to happen again. Which is great, except it means your tests are always backward-looking. The failures that actually keep you
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