> ## Documentation Index
> Fetch the complete documentation index at: https://resources.athenaintel.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Ontology
> Model your organization's real-world entities, relationships, and data as structured knowledge that Athena can reason over.
The Ontology is a structured model of your organization's real-world entities and the relationships between them. Rather than working with disconnected files and databases, the Ontology lets you define what things exist in your world — deals, clients, candidates, companies — and how they relate to each other.
Once defined, Athena's agents can query, create, and update ontology data directly within conversations, turning unstructured workflows into structured, actionable knowledge.
## Why Use an Ontology?
Most organizations store critical information across dozens of tools — CRMs, spreadsheets, documents, emails. The Ontology brings this scattered data into a single, coherent model that both humans and AI agents can work with.
Define the entities and relationships that matter to your business with typed properties and validation rules.
Athena agents can read, create, and update ontology data in conversations — no manual data entry required.
Link related entities together to capture how your data connects, enabling richer queries and insights.
## Core Concepts
The Ontology is built from three primitives. If you're familiar with databases, the analogy is straightforward:
| Ontology Concept | Database Equivalent | Description |
| ---------------- | ------------------- | ----------------------------------------------------------------------------------- |
| **Object Type** | Table | A template defining a category of real-world entity (e.g., "Deal", "Client") |
| **Instance** | Row | A single record of an object type (e.g., the deal "Acme Series B") |
| **Relationship** | Foreign key / Join | A named, typed link between two object types (e.g., Deal → *assigned\_to* → Person) |
Each object type has **properties** (columns) with types, validation rules, and descriptions. One required text property is designated the **title property** — this is the display name shown in tabs, search results, and mentions.
### Object Types
An object type is the schema definition for a category of entity. For example, a "Company" object type might have properties like `name`, `industry`, `employee_count`, and `annual_revenue`. Every instance of the "Company" type will have these same properties.
Athena provides built-in templates for common object types like **Person**, **Company**, **Deal**, **Client**, and **Recruit** to get you started quickly.
### Properties
Properties define the attributes of an object type. Each property has a **name**, a **type**, and optional constraints:
| Type | Description | Example |
| ----------- | -------------------- | ------------------------ |
| **Text** | Free-form string | Name, email, notes |
| **Number** | Decimal value | Revenue, price, latitude |
| **Integer** | Whole number | Employee count, quantity |
| **Boolean** | True/false | Is active, is verified |
| **Date** | Calendar date | Start date, founded year |
| **Enum** | Fixed set of options | Stage, status, tier |
Properties can be marked as **required**, given **default values**, and constrained with rules like minimum/maximum values or string patterns.
### Relationships
Relationships define how object types connect to each other. Each relationship has:
* **Source and target** object types
* A **name** describing the relationship (e.g., "assigned\_to", "works\_at", "owns")
* **Cardinality**: one-to-one, one-to-many, many-to-one, or many-to-many
* **Directionality**: whether the relationship goes one way or both
For example, a "Deal" might have a many-to-one relationship called "owned\_by" linking it to a "Person", meaning each deal has one owner but each person can own many deals.
## How Agents Use the Ontology
Once you've defined your ontology, Athena agents can work with it directly. Here are examples of what you can ask:
### Creating Instances
> **You:** Log a new deal: Acme Series B, \$5M, closing next quarter
>
> **Athena:** Created a new Deal: "Acme Series B" with deal\_value: \$5,000,000, stage: negotiation, and close\_date: 2026-06-30.
### Querying Data
> **You:** Show me all deals in the negotiation stage worth over \$1M
>
> **Athena:** Found 3 deals in negotiation over $1M: Acme Series B ($5M), Globex Expansion ($2.3M), Initech Platform ($1.5M).
### Updating Records
> **You:** Move the Acme deal to closed-won and update the value to \$5.5M
>
> **Athena:** Updated "Acme Series B": stage changed from negotiation → closed\_won, deal\_value updated to \$5,500,000.
### Traversing Relationships
> **You:** Who owns the deals connected to Acme Corp?
>
> **Athena:** Acme Corp has 2 connected deals: "Acme Series B" (owned by Sarah Chen) and "Acme Platform License" (owned by James Wilson).
The ontology gives agents the structured context they need to take precise, data-driven actions instead of relying solely on unstructured conversation.
## Getting Started
Navigate to **Ontology Builder** from the sidebar. This is the visual workspace for defining your data model.
Click **New Object Type** or double-click the canvas. Choose a template or start from scratch. Define properties and set a title property.
Drag from the handle on one object type node to another to create a relationship. Configure the name, cardinality, and direction.
Once your ontology is defined, agents with the ontology tools enabled can create and query instances in any conversation.
Read on to learn about [Object Types](/docs/ontology/object-types), [Relationships](/docs/ontology/relationships), and the [Ontology Builder](/docs/ontology/ontology-builder) in detail.