Acryl Data

What is Acryl Data?

Acryl Data is a company within the Data Infrastructure category. Acryl Data is a technology company that provides a Context Management Platform designed to unify data lineage, quality, and real-time context for AI agents. It serves as the commercial backbone for the open-source DataHub project, offering a centralized graph that integrates metadata, knowledge, and memory for agentic workflows.

When was Acryl Data founded and where is it based?

Acryl Data was founded in 2021 and is headquartered in Mountain View, CA.

What problems does Acryl Data solve for buyers?

Buyers turn to Acryl Data for Manual Documentation: Using spreadsheets, Confluence pages, or internal wikis to track data lineage and metadata manually., Data Engineering Agencies: Hiring data engineering firms to build custom metadata connectors and governance frameworks., Status Quo AI Development: Operating AI agents on raw data lakes without a unified context layer, leading to high hallucination rates and poor observability., among 3 documented problem areas.

What questions do buyers ask AI about Acryl Data?

Buyers evaluating Acryl Data typically ask AI models about "Best open source data catalog for enterprise", "DataHub commercial support", "Data lineage for Snowflake and BigQuery", and 3 similar queries.

What does Acryl Data offer?

Acryl Data's core products are Context Management Platform, DataHub Cloud, Context Kit, MCP (Model Context Protocol) integration, Data Lineage & Quality..

How is Acryl Data priced?

Acryl Data uses Enterprise/Custom.

Who does Acryl Data target?

Acryl Data serves Enterprise Data Teams, AI Platform Engineers, Fortune 500 Data Engineering departments..

What differentiates Acryl Data from competitors?

Acryl Data Unlike traditional data catalogs, Acryl provides a real-time 'context graph' specifically designed to feed high-fidelity lineage and memory into AI agents.

Problems this brand solves

• Manual Documentation: Using spreadsheets, Confluence pages, or internal wikis to track data lineage and metadata manually.
• Data Engineering Agencies: Hiring data engineering firms to build custom metadata connectors and governance frameworks.
• Status Quo AI Development: Operating AI agents on raw data lakes without a unified context layer, leading to high hallucination rates and poor observability.

Buyers search for

• Best open source data catalog for enterprise
• DataHub commercial support
• Data lineage for Snowflake and BigQuery
• Context management platform for AI agents
• Model Context Protocol implementation for enterprise data
• General Vector Databases: Using generic vector databases like Pinecone or Milvus to store context without the governance/lineage layer.

About this profile

This Business Profile is published by Optimly in the Optimly AI Brand Index, a public research dataset showing how AI systems describe brands, categories, and competitors. Optimly AI Visibility analyzes sampled buyer-intent responses, cited sources, and public brand information. The Brand Authority Index summarizes answer presence, narrative accuracy, and owned citations where sufficient evidence is available.

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