Anomalo
Monitors data tables for quality problems automatically, and scores unstructured documents for whether they are fit to feed a language model.
Highlights
- Automatic anomaly detection for structured tables — no manual rule writing
- Unstructured data quality — scores documents, PDFs, contracts for LLM-readiness
- Native integrations with Snowflake, Databricks, BigQuery, Redshift
- Founded 2018 by Elliot Shmukler (ex-Wealthfront) and Jeremy Stanley (ex-Instacart CDO)
- Approximately $72M+ Series B led by Menlo Ventures with Foundation Capital, Norwest, Two Sigma
- Customers include Discover, Notion, Block, Buzzfeed, Marsh McLennan
- Open-source Spark-based data-quality library plus commercial SaaS
External link — opens anomalo.com in a new tab. Anomalo is a third-party product; we are not affiliated with it.
About Anomalo
What it is
Anomalo watches data warehouse tables and flags anomalies without anyone writing rules for each one — missing rows, distribution shifts, values that stopped arriving. It has extended the same idea to unstructured data, scoring documents for whether they are in a state a language model can usefully consume.
Why it's different
Automatic monitoring is the distinction worth paying for. The traditional approach is writing assertions per table, which means the checks only cover what somebody already thought to worry about, and coverage decays as the warehouse grows. Learning what normal looks like scales where that does not. The LLM-readiness scoring is the newer and more speculative half — it addresses a genuine problem, since most failed RAG projects fail on document quality rather than on the model, but "readiness" is a judgement and its scoring is not a settled science. The core data-quality product is the mature part.
How people use it
It is used by data teams whose warehouse feeds dashboards and models that people trust, where a silently wrong table is worse than a missing one because decisions get made on it. The pattern is broad automatic monitoring across everything, with hand-written checks reserved for the handful of tables where you know exactly what must never happen. The alerting matters as much as the detection, since a flagged anomaly nobody reads is a log line.
Written by the n3os team. We are not affiliated with Anomalo.
This listing was written from public information, without Anomalo’s involvement. If you own it and something here is wrong — or you would rather not be listed at all — email us and we will correct or remove it.
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