AI-CHATBOT / ENTERPRISE AI
AI Data Unification: Connect Your Data and Give AI a Shared Understanding
Connect information across your existing systems into a unified knowledge layer. Give authorized AI agents a consistent way to understand your organization, while keeping your data and infrastructure under your control.
Plan Your Data Unification ProjectStart with your systems, your priorities, and a clear use case.
ONE KNOWLEDGE LAYER FOR YOUR AI AGENTS
AI Data Unification
Connect systems. Establish context. Support AI.
Your systems. One connected foundation for AI.
Conceptual illustration, not a product screenshot.
THE OPPORTUNITY
Help AI understand how your organization fits together
Organizations rely on information spread across customer relationship management platforms, learning systems, databases, applications, and data warehouses. When these systems operate independently, AI agents may have difficulty understanding how their information relates.
AI Data Unification connects those systems through a shared knowledge layer. It describes the people, records, activities, and relationships that matter to your organization in a structured way that authorized AI agents and applications can use.
The result is a consistent foundation for AI initiatives: teams can establish shared definitions and access rules once, then use that foundation across multiple agents and use cases.
Illustrative data unification workflow
Connect your systems
Databases and applications
CRM, LMS, and SIS
Data warehouses
Unify the context
Entities and attributes
Relationships and definitions
Roles and permissions
Support AI agents
Access through MCP
Role-scoped information
Consistent organizational context
KEY CAPABILITIES
Build a connected knowledge foundation for AI
Bring information and organizational context together while maintaining the access controls your teams depend on.
System Connections
Connect compatible databases, business applications, learning systems, and other information sources through MCP-based tools.
Shared Knowledge
Define the entities, attributes, and relationships that help AI understand your organization's information consistently.
Role-Scoped Access
Scope information to the identity and permissions of the person using an agent or application.
Infrastructure Control
Run the knowledge layer within your chosen environment, including your own cloud or on-premises infrastructure.
HOW IT WORKS
Two layers that give AI organizational context
AI Data Unification uses an ontology: a structured representation of the things your organization manages, how those things relate, and the rules that govern access and activity.
Define the things that matter
Describe entities such as customers, employees, students, courses, departments, and products. Define their attributes and the relationships between them.
Focus: a shared vocabulary and connected information.
Define the rules around activity
Describe functions, permissions, and the actions an organization may allow. These rules provide context for how people and agents interact with information.
Focus: consistent rules and organizational oversight.
The current data unification implementation is read-only: connected agents can retrieve authorized information, but do not change source systems through this layer.
CONNECT YOUR SYSTEMS
Bring important information into a shared view
Connectors can bring together information from a variety of systems. The right combination depends on your organization’s applications, available interfaces, and project requirements.
Customer and Business Systems
Connect compatible platforms such as Salesforce, HubSpot, ServiceNow, and Workday to support a more complete view of business information.
Education Systems
Bring context together from systems such as Canvas, Ellucian Banner, PeopleSoft, Slate, and Salesforce Education Cloud.
Databases and Data Warehouses
Connect compatible databases and data platforms, including PostgreSQL, Oracle, and Snowflake, using supported tools.
Collaboration Platforms
Connect information from supported workplace tools such as Slack, Confluence, GitHub, and Zoom.
Service and Support Tools
Include information from compatible service platforms such as Zendesk and ServiceNow in your knowledge layer.
Custom Connections
Plan connections to additional systems through supported databases, APIs, or custom MCP servers.
SECURITY AND GOVERNANCE
Give AI access with your organization's rules in place
A useful AI foundation also needs a clear approach to identity, permissions, and the location of your information.
Read-Only Connections
The current implementation is designed for read-only access. A safety test suite checks that write attempts are denied before a connection is provisioned.
Identity and Permissions
Requests are scoped to the user's identity and role, including row-level access where configured. Agents receive information according to the permissions of the users they serve.
Your Environment
Run the knowledge layer, its cache, and its search index within your chosen infrastructure and security perimeter.
WAYS TO USE AI DATA UNIFICATION
Create a shared foundation for multiple AI initiatives
Once your information and definitions are connected, authorized agents can use the same foundation to support different teams.
Higher Education
Connect student, course, advising, and program information across learning and administrative systems.
Customer Support
Help authorized agents use relevant customer and service information to support more informed interactions.
Employee Assistance
Bring together approved information from workplace systems to help employees find answers and understand processes.
Healthcare Organizations
Connect organizational information and define relationships and permissions around approved healthcare workflows.
Financial Services
Organize information from business systems around shared definitions and role-based access requirements.
Government and Public Sector
Establish a controlled knowledge foundation for AI initiatives across departments, services, and information sources.
OWNERSHIP AND FLEXIBILITY
Your data. Your infrastructure. Your AI strategy.
AI Data Unification is designed to give your organization control over the foundation supporting its AI agents.
Open-Source Foundation
The underlying implementation is open source and MIT-licensed, giving your team access to the code.
Deployment Choices
Run the unified layer in your own cloud, virtual private cloud, on-premises environment, or an air-gapped network.
Model Flexibility
Connect authorized AI agents and applications using different models, including models hosted in your own environment.
GETTING STARTED
Start with the systems and questions that matter most
A focused first project helps your team identify its data sources, establish shared definitions, and plan access for an initial AI use case.
Choose the teams, questions, and information sources that will provide a useful starting point.
Define the important entities, relationships, access rules, and system connections for your project.
Configure a pilot in your environment, test permissions, and evaluate how authorized agents use the shared knowledge layer.
FREQUENTLY ASKED QUESTIONS
Questions about AI Data Unification
What is AI Data Unification?
AI Data Unification connects information from existing systems through a structured knowledge layer. It gives authorized AI agents a shared way to understand entities, relationships, and organizational rules.
What is an ontology?
An ontology is a structured representation of the things that matter to an organization and the relationships between them. For example, it can describe how a student relates to a course, or how a customer relates to a support request.
Does our data have to be sent to a third-party cloud?
No. The unified layer can run within your own environment and query connected systems there. The implementation can also maintain a cache and search index inside your infrastructure; these are not sent to a third-party cloud.
Which systems can be connected?
The service supports connections to a range of compatible databases and applications, including CRM, LMS, SIS, human resources, collaboration, and service platforms. The systems and connection methods for your project can be reviewed during planning.
Can AI agents change information in our systems?
The current data unification implementation is designed for read-only access. It includes safety tests that check whether write attempts are denied before provisioning.
How is access controlled?
The implementation uses user identity and role information to scope requests. Permissions can include row-level controls, so an agent receives information appropriate to the user it serves and the access rules configured for the deployment.
What is MCP?
MCP stands for Model Context Protocol. It provides a way for compatible AI applications and agents to connect to tools and information sources. The unified knowledge layer can be exposed through an MCP server for authorized applications to query.
Can the service run in our own environment?
Yes. Deployment options include your own cloud, a virtual private cloud, an on-premises environment, or an air-gapped network. Your infrastructure and security requirements can be discussed during project planning.
LET'S GET STARTED
Give your AI agents a connected foundation for better understanding
Explore how AI Data Unification can connect your systems, organize your information, and support your organization's AI strategy.
Discuss Your Data Unification ProjectWe can discuss your systems, use cases, access requirements, and deployment preferences.