Anomalo

Anomalo

Anomalo is the data-quality and unstructured-data observability platform that automatically monitors structured tables for anomalies AND scores unstructured documents for LLM readiness. Anomalo grew through 2023-2025 as the leading data-quality vendor for Snowflake, Databricks, and BigQuery customers, and in 2024-2026 expanded into unstructured-data quality — measuring whether PDFs, contracts, and other documents are clean enough to feed LLM pipelines.

Production credibility: Approximately $72M+ Series B led by Menlo Ventures with Foundation Capital, Norwest Venture Partners, and Two Sigma Ventures participating. Founded 2018 by Elliot Shmukler (ex-Wealthfront, ex-Instacart) and Jeremy Stanley (ex-Instacart Chief Data Officer). Customers include Discover, Notion, Block, Buzzfeed, Marsh McLennan. Native integrations with Snowflake, Databricks, BigQuery, Redshift.

Key Features

• 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

Ideal Use Case

Enterprise data teams running Snowflake/Databricks/BigQuery that want automatic data-quality monitoring without writing assertions — plus AI teams scoring document quality before feeding to LLM pipelines.

How Anomalo differentiates

Monte Carlo, Bigeye, and Soda are data-observability platforms focused on structured-table monitoring. Anomalo started in the same lane but pivoted to layer unstructured-data quality on top — scoring whether PDFs, contracts, and other documents are clean enough to feed LLM pipelines. For enterprise teams running both structured analytics AND LLM applications, Anomalo is the only major player covering both. The trade-off is that pure-play structured monitoring competitors may be deeper in legacy data-quality scenarios; Anomalo is the modern AI-era pick.

FAQ

Q: What is Anomalo? A: Anomalo is a data-quality and unstructured-data observability platform that automatically monitors structured tables for anomalies and scores unstructured documents for LLM readiness.

Q: Who founded Anomalo? A: Elliot Shmukler (ex-Wealthfront, ex-Instacart) and Jeremy Stanley (former Instacart Chief Data Officer) co-founded Anomalo in 2018.

Q: How much has Anomalo raised? A: Approximately $72M+ Series B led by Menlo Ventures, with Foundation Capital, Norwest Venture Partners, and Two Sigma Ventures participating.

Q: Anomalo vs Monte Carlo vs Bigeye? A: Monte Carlo and Bigeye focus on structured-table data observability. Anomalo covers both structured monitoring AND unstructured-data quality (PDFs, contracts, documents) for LLM pipelines. For teams running both analytics and LLM applications, Anomalo is the dual-coverage choice.

Q: Who uses Anomalo? A: Discover, Notion, Block, Buzzfeed, Marsh McLennan, and other enterprises running Snowflake, Databricks, BigQuery, or Redshift in production.

tl;dr

Anomalo is the data-quality + unstructured-data observability platform. $72M+ Series B (Menlo lead). Founded by ex-Wealthfront/Instacart leadership. Customers include Discover, Notion, Block, Buzzfeed. Covers both structured-table monitoring AND document LLM-readiness scoring.

Related

Looking for more options? Browse the AI/ML Models directory or read our best AI models listicle. Anomalo is also tracked on Crunchbase.