A new approach: Collibra Unstructured AI

What is a knowledge product?

A knowledge product is the equivalent of a data product but in the unstructured world.

Solving the unstructured data problem doesn’t mean starting over. It means changing how you engage with the content you already have. It means that making unstructured data AI-ready demands thinking in terms of knowledge products.

That’s where Collibra Unstructured AI is a game-changer.

At Collibra, we define knowledge products as a file or set of files that are:

Pre-vetted for sensitivity (e.g., PII, confidentiality or offensive languages)
Pre-vetted for data quality (uniqueness, relevance, timeliness and completeness)
Enriched with metadata for humans (to support human cataloging and search)
Enriched with metadata for agents (to support AI-based retrieval)

Collibra Unstructured AI provides a pipeline for governance and AI engineering teams to automatically perform each of these steps. Our LLM-native approach to metadata generation enables users to:

Automatically scan large document and file repositories to quickly understand what data exists.
Auto-tag every file for sensitive data markers, with exact evidence showing why specific content was classified as sensitive.
Run ML-based data quality assessments to identify duplicate, redundant or outdated files.
Define custom tagging taxonomies from human- or data-derived sources, and use LLMs to automatically tag content —at both the chunk and file level—to generate structured metadata.
Automatically filter for relevant files and create a knowledge product that can be accessed programmatically by AI or engineering teams.

Use case: An engineering team building a chatbot can isolate final-version internal policy documents from the past five years, without PII.

Enrich the knowledge base with additional taxonomies and metadata.
Publish knowledge products in a centralized marketplace for easy discovery and reuse.

From one month to 20 minutes

In one real-world example, Collibra’s automation reduced the time needed to classify and tag 10,000 files from more than 1 month to just 20 minutes, including (i) Auto-discovering the best taxonomy tailored towards the data, and (ii) Using LLM-based metadata generation to produce structured tags across every file.³

The result of using this kind of unstructured data curation pipeline includes:

Compliance: Confidence that no sensitive files are inadvertently included in any AI use case.
Use case lineage: Clear visibility and control over which files are being consumed by each use case.
Speed of setup: Dramatically less time spent by data science and engineering teams locating relevant files or collaborating with SMEs to build taxonomies.
Model accuracy: Improved AI model and application performance through richer data context and redundant, outdated or low-quality file removal.
Scalability: The ability to leverage more data sources and support higher-volume search without compromising retrieval accuracy, enabled by a structured metadata filtering layer.

One month to 20 minutes?! That’s the difference between AI being viable or not in a production pipeline.

And because Collibra is API-first, everything you enrich and classify can plug directly into your enterprise architecture—from LLM platforms and knowledge management tools to enterprise search engines and AI applications.

Customers, for example, can use unstructured AI as an automated metadata enrichment system for their Microsoft Sharepoint Sites, Google Drive and S3 buckets, ensuring the metadata lives directly next to the raw documents. Or they can build a unified catalog within the Collibra ecosystem that can be accessed by any user.

This is the shift: from manual governance to automated enrichment. From static repositories to intelligent discovery. From opaque content to transparent, AI-ready knowledge assets.

Collibra doesn’t just help you keep up with AI. It helps you lead.

Learn more about Collibra Unstructured AI.

³ Source: Collibra internal research.