About Collibra Unstructured AI

Updated: October 2, 2025

Collibra Unstructured AI automatically discovers semantic taxonomies, tags and enriches unstructured data, generating structured metadata to reduce manual effort. It ensures that unstructured files, such as documents and transcripts, are contextualized and ready for use in AI systems.

Unstructured data represents a substantial portion of the data in an organization, often making up 80% to 90% of the total data estate. This data includes documents, PDFs, transcripts, presentations, contracts, and emails. Despite its volume, unstructured data can be underutilized, inaccessible, or unsuitable for AI applications because it lacks metadata, the structure and context that make data meaningful.

This data is often invisible to governance and analytics programs due to fragmented systems, manual classification processes, and the absence of scalable metadata. These challenges may lead to delays in AI initiatives, compliance risks, low-quality AI outcomes, and poor performance of Generative AI (GenAI) models.

Collibra addresses these challenges by providing an automated enrichment layer that transforms unstructured data into structured knowledge assets to make previously unmanageable content governable.

Beneficiaries

Collibra Unstructured AI is designed for organizations, particularly those in regulated industries, such as financial services and healthcare, that are building Generative AI (GenAI), Retrieval-Augmented Generation (RAG), and agentic workflows. Key stakeholders include:

• AI Engineers: Improve retrieval accuracy for GenAI and RAG systems by automatically tagging and enriching unstructured data with semantic metadata.
• Data Scientists: Ensure documents used to train or augment models meet governance and lineage requirements, while filtering out noisy or duplicate documents before ingestion.
• Knowledge Managers: Make unstructured content discoverable and reusable, and curate documents by themes or compliance needs.
• Compliance Analysts: Monitor unstructured content for policy violations, such as PII exposure, and use traceable metadata for AI audit and transparency requirements.
• Data Platform Engineers: Automate the ingestion and enrichment of unstructured files at scale, reducing manual tagging and accelerating integration with downstream systems.

Key use cases

AI Input Governance

Automate the validation and enrichment of unstructured data used in AI pipelines to ensure responsible AI adoption, reduce regulatory risk, and enforce metadata standards across the AI lifecycle.

RAG and Generative AI Optimization

Use semantic metadata to improve Retrieval-Augmented Generation (RAG) systems, enhancing retrieval accuracy through routing strategies and embedding enhancements.

Enterprise Search

Build structured datasets from unstructured files to support high-accuracy search applications and prevent knowledge search degradation when scaling to thousands of documents.

Data Product Curation

Rapidly curate "data slices" by leveraging metadata to filter millions of documents and identify the most relevant subset for specific business or data science use cases.

Compliance and Risk Mitigation

Classify and tag unstructured content at scale to proactively detect sensitive or non-compliant content before it enters AI workflows.

Capabilities

CollibraUnstructured AI automates the conversion of unstructured content into structured knowledge assets, eliminating the need for custom AI pipelines or extensive manual labeling. The automated workflow connects to unstructured data, such as raw files or vector databases, performs AI-assisted schema generation, applies LLM-based tagging, curates data slices, and syncs the structured metadata. It provides confidence scores and traceability for every generated tag, supporting explainability and enabling human-in-the-loop validation.

Smart discovery

Unstructured AI scans large file repositories, such as SharePoint or S3, using an automated workflow to identify the most relevant unstructured data for specific AI use cases using semantic tagging. This enables automated unstructured data discovery workflows that integrate directly into a governed data catalog.

Automated semantic layer

The semantic layer generates high-quality, contextual metadata without requiring manual effort. It replaces manual tagging and reliance on domain experts with adaptive, GenAI-native metadata creation and enrichment workflows. Algorithms automatically discover meaningful taxonomies and generate domain-specific metadata tags and schemas from the data corpus, often requiring no input from business experts. Additionally, metadata is dynamic and self-sustaining, automatically updating as business definitions and content evolve.

High-accuracy enterprise AI search

By adding structured metadata to unstructured content, Collibra ensures data is governed, contextualized, and ready for use in systems like RAG and AI assistants. This prevents knowledge search degradation and maintains AI accuracy by enriching enterprise search tools and AI systems with semantic context. The extracted detailed metadata tags for topics, entities, and sensitivity classifications at both the chunk and file level, support multimodal tagging of images, charts, and tables.

Integration with Collibra Platform

Unstructured AI capabilities are deeply integrated with Collibra to provide unified governance across all data types:

• Unified governance: Enable governance across structured and unstructured files, transforming them into reusable, governed data assets.
• Lifecycle management: Govern the full lifecycle of AI and data use cases, including discovery, enrichment, policy enforcement, risk management, and lineage tracking.
• Technical stacks: Access functionality through a user-friendly platform for integration with existing AI stacks, such as vector databases and embedding models.
• Deployment: Built to operate across multi-cloud environments.

Benefits and business impact

Unstructured AI delivers measurable benefits by addressing the high costs, risks, and delays associated with manual unstructured data management:

• Accelerated time-to-value: Reduce manual tagging time and taxonomy development costs, cutting the time required to organize and filter files.
• Improved AI performance: Deploy GenAI and RAG applications faster with governed, high-quality inputs, reducing model errors and improving retrieval relevance.
• Risk reduction and compliance: Proactively identify sensitive and non-compliant content before AI consumption, increasing audit readiness with traceable metadata.
• Increased data usability: Expand usable enterprise data for AI applications by enriching unstructured content.
• Enhanced trust: Build trust in AI results with explainable, traceable metadata for each document, improving model transparency and explainability.

Next steps and availability

Contact the Collibra Account Team to become an early adopter and transform unstructured data into structured, searchable, and trusted assets today.