Set the right foundation for your AI systems, AI agents and data catalogs.
Generic data extraction tools often strip away essential business and industry context, compromising AI accuracy.
Knowledge Enrichment is designed for enterprise content, maintaining the natural structure of your data. Your AI systems then reflect real-world practices and deliver reliable, precise results.
Data preparation is often the most time-consuming and error-prone stage of an AI project.
Knowledge Enrichment eliminates this bottleneck by transforming content into formats that are instantly usable by AI systems. Free your teams to focus on innovation, not cleaning data.
Relying solely on LLMs for content preparation can limit precision and scalability.
Knowledge Enrichment combines advanced AI with the proven technology of Hyland Document Filters. This hybrid approach delivers unparalleled extraction quality and preserves document structure.
Data curation
Context enrichment
Knowledge Enrichment extracts, normalizes and structures your content while maintaining the original document's logical structure and meaning. It then applies AI to enrich metadata with semantic vectors, topic hierarchies and business entity recognition.
This process creates structured, AI-ready data that allows AI systems to understand the context of your documents, leading to deeper insights, improved searchability and better AI performance.
Process over 600 file formats, including PDFs, multimedia, legacy files and code repositories, to achieve full coverage of your organization’s data ecosystem.
Segment documents into meaningful chunks while retaining context, hierarchy and positional references to improve AI understanding and maintain document relationships.
Capture semantic meaning and relationships through high-dimensional vectors to enable advanced search, clustering and recommendation systems.
Enable organizations to identify and mask sensitive information. Configurable policies allow developers to decide what to detect, redact or preserve for downstream AI and analytics use.
Automatically generate rich metadata, from topics to business entities, making unstructured, messy content searchable and ready for action.
Save time by dropping structured, enriched content that requires zero additional preparation directly into LLMs, analytics tools or vector databases.
As enterprises adopt agentic AI, autonomous decisions will rely on high-quality, well-managed data. Knowledge Enrichment curates and enriches data from across the enterprise, providing the context AI agents need to make informed decisions.
Structured data leads to improved AI predictions, better decision-making and faster processing times. Organizations can trust that their insights are based on high-quality input, reducing the risk of AI hallucinations or inaccurate outputs.
By enriching data at the point of ingestion, Knowledge Enrichment turns raw documents into structured, AI-ready content. This eliminates costly and time-consuming preprocessing steps. Teams can launch AI projects faster and with greater confidence in their data foundation.
Transform previously inaccessible content into structured information assets that can be queried. Enhanced accessibility allows businesses to quickly locate relevant information, surface critical insights and make data-driven decisions faster.
Knowledge Enrichment streamlines data preparation workflows, freeing up data engineering resources for higher-value tasks. Ready-to-use outputs eliminate the need for manual data structuring and reduce downstream errors in AI pipelines.
As data volumes grow, Knowledge Enrichment helps teams to process more content without added complexity. The API-first architecture integrates seamlessly into existing workflows, enabling AI adoption at enterprise scale.
Retail companies need to automate metadata generation and identify named entities across product catalogs.
Knowledge Enrichment applies consistent metadata tagging and creates contextualized descriptions, enabling more reliable product data, improved search relevance and better personalization in recommendations.
Processing and validating thousands of claims daily with mixed-structured forms and unstructured documents like medical reports and photographs requires extensive manual work.
Knowledge Enrichment identifies key entities, structures and enriches content while preserving context to accelerate claims processing, reduces manual workload and improves accuracy in fraud detection.
Healthcare providers manage vast amounts of unstructured patient data — physician notes, medical histories, prescriptions and test results — that is time-consuming to process manually.
By transforming unstructured patient records into structured, actionable data, Knowledge Enrichment helps healthcare providers improve operational efficiency, support better patient outcomes and streamline compliance with regulatory requirements.
Universities need to convert and standardize diverse academic content into AI-ready formats for research and other retrieval-augmented generation (RAG) solutions.
Knowledge Enrichment transforms unstructured academic data into AI-ready content that's enriched, structured and accessible, enabling more accurate and comprehensive search results across academic resources and research databases.
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The promise and potential of AI is something every organization wants to align with, but one big part of its implementation is often overlooked: AI-readiness. Before AI can achieve what you expect it to, your enterprise content must be AI-enabled. Here's the AI-readiness framework to consider.
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The tools that redefining content management with AI
Unstructured data makes up as much as 80% of enterprise content and is scattered across an average of 21 repositories. Unstructured data includes documents, emails, images, audio files and more. Unlike structured data in databases, unstructured content is inconsistent and harder to search, analyze or use in business processes and AI applications.
Knowledge Enrichment uses data curation techniques to structure and normalize unstructured content, making it clean, consistent, and ready for use. Powered by Document Filters it extracts and transforms data from over 600 file formats while preserving the original context and logical structure. This ensures content remains meaningful and usable across downstream AI, analytics, and automation applications.
Knowledge Enrichment processes over 600 file formats, including common business documents (PDF, Word, Excel), multimedia files (images, audio, video), emails, scanned documents, code, markup and specialized formats like CAD files (DWG, DGN, STEP, DWF). Most of these formats are provided through Document Filters, with a list located in the documentation.
Unlike many competitors, Knowledge Enrichment uses a combination of both LLMs and deterministic techniques like Document Filters for precision extraction. This approach delivers superior accuracy because it preserves document structure and positional context while reducing hallucination risks. Tables stay tables, headers stay headers, and you get ready-to-use outputs without additional cleaning or formatting. This deterministic extraction method ensures consistent, reliable results that you can trust for enterprise applications.
Knowledge Enrichment includes built-in PII masking capabilities that identify and protect sensitive information (names, emails, addresses, social security numbers, account numbers) across all supported file types. Configurable policies let you decide what to mask or preserve based on your compliance requirements.
No. Knowledge Enrichment goes far beyond traditional metadata tagging. It performs contextual text chunking, preserves positional information, generates semantic embeddings, identifies relationships between documents and extracts meaningful entities while maintaining document structure. The result is content that's not just tagged but truly understood by AI systems. This enables advanced capabilities like semantic search, intelligent recommendations and context-aware automation that simple metadata can't support.
Yes, Knowledge Enrichment is designed for builders — app developers, data engineers and solution builders. It’s an API-driven solution designed to integrate into broader architectures and workflows but with options catering to varying needs.
The data curation and context enrichment capabilities through the Knowledge Enrichment API offers faster time-to-value for organizations that want enriched, contextual output without dealing with the complexity of extraction.
Document Filters, a proven Hyland technology powering Knowledge Enrichment’s data curation, is perfect for technical teams and organizations looking for full control over how content is enriched, structured and delivered for downstream use.
Hyland provides AI-readiness offerings that cater to your organization’s needs and technical resources.
For organizations that want enriched, contextual output without managing complex extraction, the Knowledge Enrichment API comes with both data curation and context enrichment capabilities. This is the perfect solution for organizations that have use cases around AI-ready data, domain-specific linking, or connecting concepts across documents.
For organizations and solution builders that want full control while extracting clean, structured text and metadata from a wide range of file types, Document Filters is the right option. This gives you the ability to perform data curation in your own infrastructure so you can build custom workflows that require normalized content.
Ease of implementation will depend on the needs of your organization.
The Knowledge Enrichment APIs enable organizations to focus on preparing content for knowledge graphs, intelligent retrieval, RAG pipelines or industry-specific LLM workflows without dealing with the complexity of extraction.
Document Filters enables organizations to have full control over how extraction is done and how the data is integrated into their own infrastructure, including on-premises or offline systems. This process may entail additional work.
Knowledge Enrichment is currently available as a cloud-native API designed for seamless integration into modern data pipelines and enterprise architectures. For organizations requiring on-premises deployment, Document Filters offers full control over extraction and enables deployment in your own infrastructure, including on-premises or offline systems.
Yes. Knowledge Enrichment delivers AI-ready output in standardized formats (like JSON) that integrate seamlessly with any AI system, analytics solution, data lake or third-party tool. The API-first design ensures you can use enriched content wherever you need it — whether that's feeding external LLMs, populating data catalogs, powering RAG pipelines or integrating with custom applications.
Knowledge Enrichment and Knowledge Discovery work together to transform how you access and use enterprise content. Knowledge Enrichment prepares your content by converting unstructured data into structured, contextualized, AI-ready data. Knowledge Discovery then leverages that AI-ready data to power AI-driven search and natural language question answering, delivering faster, more accurate responses. When used together, Knowledge Enrichment improves the quality of search results and AI-generated answers in Knowledge Discovery.
