Hyland Enterprise Context Engine™

Enterprise Context Engine gives your AI systems the same governed understanding of your business as your employees.

Enterprise Context Engine improves your AI output

The Enterprise Context Engine delivers a governed content and data foundation for your AI models. It connects your content, business objects and entities, along with their sources, to any or all of your AI solutions, so AI can navigate your business content and context as effectively as your most experienced people.

Put governed content to work in your AI

Most enterprise content needs preparation before AI can use it effectively. Enterprise Context Engine makes that content usable while preserving meaningful document structures and by carrying source permissions into the context layer. This gives teams a governed foundation for AI applications.

Give people context they can check

Good business decisions depend on contextual relationships and the evidence spread across your enterprise documents. Enterprise Context Engine connects information around all related documents with supporting source evidence that users can review and confirm.

Reduce repeated work as use cases grow

Every new AI initiative can mean another round of content preparation and business modeling. Enterprise Context Engine lets teams reuse processed content and business models across applications, workflows and agents. This reduces repeated foundation work as AI expands.

Enterprise Context Engine supports AI readiness and industry context

Enterprise Context Engine uses curation and enrichment to make enterprise content usable for AI and analytics. It offers industry ontologies and a contextual knowledge graph, so your business concepts and relationships are connected to your content.

Curated content for AI

Enterprise Context Engine turns raw content into structured, AI-ready data and preserves essential spatial context that tells AI how a document is organized, including what information is in tables and charts.

Enriched content for AI

Enterprise Context Engine adds the semantic layer AI needs in the form of summaries, classifications, metadata, entities and embeddings to improve searchability and AI processing.

Prebuilt industry ontologies

Enterprise Context Engine applies the terminology and relationships used in insurance, financial services, healthcare and other industries. It allows you to extend or customize them to your own business.

Contextual knowledge graph

Enterprise Context Engine connects content, business objects, entities and processes so AI applications can answer questions that span documents, topics and systems.

Original-source evidence on every relationship

Use Enterprise Context Engine to follow the data origin from source document to entity to business object. This turns the knowledge graph into an evidence graph. Users, decision-makers and auditors can find the passage that produced the answer and verify it instead of rebuilding evidence.

Reusable context layer

Enterprise Context Engine creates one governed layer to serve every AI application. Each user only sees content and insights they can access in the original repository. With Enterprise Context Engine, each of your AI tools can reach connected business context and source evidence through out-of-the-box connectors, APIs and an MCP server.

Enterprise Context Engine at work

Enterprise Context Engine supports industry-specific review and decision processes. It connects evidence across documents and systems, and it enables applications to connect to evidence enterprise-wide.

Insurance use cases for Enterprise Context Engine

Accelerate claims and underwriting

Enterprise Context Engine transforms insurance documents into structured, searchable content for your AI. It classifies documents and extracts information so AI-powered workflows can evaluate risk and process claims more efficiently.

Claims coverage and evidence review

Enterprise Context Engine connects reported loss to the insured asset, policy period, coverage terms, exclusions and applicable endorsements. It helps surface conflicting facts and missing evidence with source references.

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Financial services use cases for Enterprise Context Engine

Quality control for mortgage post-closing

Enterprise Context Engine connects the borrower, property, loan terms, income evidence and closing documents to help map the packet to configured quality-control requirements and expose cross-document discrepancies.

Streamline lending and customer onboarding

Enterprise Context Engine curates financial services documents into consistent, machine-usable content. It also enriches that usable content with customer, account, transaction and risk information to support faster work and AI-assisted decisions.

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Healthcare and Enterprise Context Engine

Prep evidence for prior authorizations

Enterprise Context Engine transforms healthcare documents into consistent, structured content. It also classifies records and extracts request, patient, service, provider and evidence details so supporting information can be found and evaluated without manually reviewing.

Make patient financial services more transparent

Enterprise Context Engine gives a 360-degree view of a patient's documented identifiers with encounters, plans, claims, authorizations and denial evidence to help present the account timeline and unresolved evidence gaps.

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Enterprise Context Engine across various industries and offices

Improve contract review and obligation management

Enterprise Context Engine transforms documents from multiple departments like HR and Accounts Processing into structured, searchable content. It identifies critical information so AI-powered workflows can accelerate review and reduce manual tracking.

Resolve invoice and purchase order exceptions

Enterprise Context Engine matches invoice evidence to candidate vendors and purchase order lines. It helps connect receipts, contract terms and relevant correspondence so reviewers can see the cause of an exception.

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Government agencies and Enterprise Context Engine

Modernize casework and constituent services

Enterprise Context Engine transforms government documents then classifies records and extracts constituent, program, case and eligibility information so AI-enabled processes can route work, support caseworkers and improve response times.

Review planning and permit applications

Enterprise Context Engine connects the site, proposal versions, requirements, specialist findings, corrections and decision conditions. It shows which evidence resolves each issue and which dependencies or approval requirements remain open.

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Higher education use cases for Enterprise Context Engine

Simplify admissions and student services

Enterprise Context Engine prepares documents used in higher education for AI-powered processes, then extracts and organizes student name, institution, course, credential and enrollment information to accelerate evaluation, advising and administrative support.

Transfer credit and degree applicability

Enterprise Context Engine connects each student's source course and term to institutional context, course content, approved precedents and degree requirements. It helps separate transferability, course equivalency and applicability toward the student's degree.

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The Enterprise Context Engine introduces a new semantic layer that transforms enterprise content into interconnected business context. This architecture enables AI systems to understand not only what information exists, but also how information relates across an organization.

— Gabriel Keith, Director of Engineering for Content Intelligence, Hyland

Put your mission-critical content to work in AI today

The context your AI needs is already in your unstructured data. Enterprise Context Engine makes your context usable and reusable across any AI system you run.

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FAQs

What is Enterprise Context Engine?

Enterprise Context Engine is a Hyland technology that develops a governed context layer and delivers trustworthy AI across the enterprise. Its curation and enrichment capabilities support AI readiness for organizations by making unstructured content usable in downstream applications. Enterprise Context Engine includes industry ontologies and a contextual knowledge graph that adds business meaning and connected relationships. It is built on a headless architecture, so Enterprise Context Engine applications and agents use this shared context and its supporting evidence to ground responses and decisions.

Do we have to migrate our content?

No, you do not need to migrate your content. With Enterprise Context Engine, your teams can manage content in the repositories where it lives today. Enterprise Context Engine connects to those repositories, so there is no need to rebuild your system in a new place or change how your teams manage content.

Does Enterprise Context Engine respect the permissions in our repositories?

Yes, Enterprise Context Engine respects the permissions of your repositories. Ingestion connectors bring security metadata from your repositories, including access control lists, into Enterprise Context Engine. Each user is mapped to their account and groups in the original repository, so downstream applications and agents only use content the user has access to.

Do I need technical expertise to use Enterprise Context Engine?

Yes, Enterprise Context Engine users need technical expertise. Enterprise Context Engine is designed for builders, including app developers, data engineers and solution builders. It is made up of APIs and an MCP server that integrate into broader architectures and workflows. Business users work with the same context through Hyland Knowledge Discovery™, which draws on Enterprise Context Engine to deliver answers across enterprise content.

What is unstructured data, and why is it a challenge?

Unstructured data is data that isn’t structured in a way that is readable to AI systems. It’s a challenge because there is so much of it within an enterprise (it makes up as much as 80% of enterprise content, according to IBS) and because it is so scattered across repositories (on average unstructured data is found across of 21 repositories, according to Forrester). 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.

Does content readiness matter more than which AI model we choose?

Content readiness matters as much or more than which AI model you use. AI model choice matters, but so does the quality, accessibility and context of the information supplied to it. Most enterprise content was not structured for machines to read, and the meaning carried in a document’s layout is lost to generic extraction. An AI model — even the best AI model —cannot recover and use content what was never made available to it.

What happened to Hyland Knowledge Enrichment™?

The curation and enrichment capabilities previously offered as Knowledge Enrichment are now part of the Enterprise Context Engine. These capabilities provide structured, enriched content for AI, analytics and custom applications. Enterprise Context Engine also includes industry ontologies and a contextual knowledge graph to connect content with business meaning and relationships.

We already have a retrieval pipeline. Why add a context layer?

A retrieval-augmented generation (RAG) pipeline can retrieve and combine information from multiple documents, but with an added context layer like the one Enterprise Context Engine provides, you gain a reusable business model: industry ontologies define business concepts; a contextual knowledge graph connects entities and relationships with source evidence. Applications can reuse that organized context across queries and initiatives, which reduces the need to reconstruct the same relationships for each project. Your existing pipeline can consume this context through APIs.

Do we have to build our own ontology first?

No, you do not have to build a proprietary ontology before using Enterprise Context Engine. An ontology is a business definition for what a term means in your organization and how it relates to other terms. For example, consider the word “hedge.” To a currency trader, it is a risk mitigation strategy. To a fund manager, “hedge” is an investment vehicle. Words means something specific in your business that a general-purpose model has no way of properly and confidently defining. Enterprise Context Engine ships prebuilt ontologies for insurance, healthcare and financial services, so teams reach a first result without defining their vocabulary from scratch. Enterprise Context Engine ontologies can be extended so you could have multiple sets, such as for loans, policies, onboarding, etc.