The context engine for unstructured data
Deasy Labs automates every step in preparing unstructured data curation for AI
How Deasy turns unstructured data into AI-ready knowledge
- 1. Connect
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Connects to raw files in cloud sources like SharePoint and S3, or to existing vector databases. Deasy ingests, OCRs, chunks and normalizes all unstructured content.
- 2. Understand
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Tags every file using LLMs and ML models to generate high quality metadata, extracting topics, document types, authors, dates, sensitivity and quality signals.
- 3. Define your taxonomy
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Allows users to rapidly create custom business taxonomies, OR auto-generates a schema which Deasy learns from your data with a proprietary clustering algorithm.
- 4. Tag at scale
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Applies the taxonomy across all content to create a structured, filterable database view of your unstructured data.
- 5. Curate and publish
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Slices content by relevance, topic, time, quality and sensitivity to create AI-ready knowledge bases for RAG, search and agents.
- 6. Maintain
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Continuously monitors your source systems, tags new content and updates data slices with the relevant files so your AI always runs on fresh, trusted information.
Our metadata layer powers AI, cataloging and compliance use cases
Data and AI teams use Deasy Labs as their horizontal metadata capability to support a range of use cases
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Continuously curate knowledge bases for RAG and agents
Deasy turns large, messy document sets into knowledge bases that stay relevant over time. Data is deduplicated, enriched with context, filtered for risk and maintained automatically as source material changes.
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High-speed discovery and categorization across sources
Deasy scans unstructured data across storage systems and file types to quickly surface what’s there and how it should be categorized. This eliminates the need for manual audits or one-off scripts to understand the shape of a dataset before you can use it.
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Relevance scoring for specific use cases
Deasy scores files based on how relevant they are to a given use case so teams can narrow in on the subset of data that improves retrieval and model output.
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Contextual metadata enrichment for filtering and retrieval
Deasy enriches unstructured data with context about what each asset contains and what questions it can answer. That metadata makes it possible to filter precisely and retrieve the right data consistently, without relying on prompts or brittle heuristics.
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Sensitive data detection before indexing or embedding
Deasy detects and classifies sensitive data before it reaches embeddings or models. This allows teams to exclude risky content by default rather than trying to control it downstream.
Why Deasy Labs?
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Save time. Cut costs.
You can brute-force document tagging with LLMs—but it’s slow, expensive, and hard to repeat. You burn tokens, build one-off pipelines, and rely heavily on domain experts.
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Your AI toolbox for data
Deasy lets AI engineers tag and contextualize unstructured data at scale—without burning tokens, building one-off pipelines, or over-relying on domain experts.
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Manage and maintain with ease
You can review, adjust, or override at any point—without rebuilding the system as your data or use cases evolve. New data is continuously tagged, filtered, and added to the right use cases as it appears.
Deasy Labs was acquired in 2025 by Collibra, the global leader in enterprise data and AI governance—bringing Deasy’s unstructured AI capabilities into a mature, trusted governance and catalog ecosystem. This means greater scale, long-term stability, and native integration into the broader enterprise data stack.
Deasy fits into and enhances your workflows.
Deasy is available as a set of APIs or a no-code platform, and we support native integrations with:
See how leading AI companies use Deasy
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Google Cloud
Using Deasy Labs with Vertex and Gemini for enterprise search
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LlamaIndex
Improving RAG with Advanced Parsing + Metadata Extraction
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Qdrant
Managed metadata service for your vector database
