Data models - Gainable

Understanding data in Gainable

When you describe data in your prompts, Gainable creates data models — structured definitions of what information your app stores. Think of a data model as a template for each type of thing you’re tracking. Data models are also used to merge and normalize external data from multiple sources into a single unified output. When you need to combine contacts from HubSpot and Google Sheets by email, or normalize field names across systems, you create a data model with merge keys and field mappings, then include it in a dataset.

What is a data model?

A data model defines:

What fields each item has (name, email, status, etc.)
What type each field is (text, number, date, etc.)
How items relate to other items (belongs to, has many)

Example: Deal model

When you say “track deals with name, amount, and status”, Gainable creates a Deal model:

Field	Type	Description
name	Text	The deal name
amount	Number	The deal value
status	Options	Selected from predefined choices
createdAt	Date	Automatically added
updatedAt	Date	Automatically added

Collection types

The Data tab in your app groups collections into 3 categories:

Aspect	App Collections	Data Collections	Core Collections
Source	Created in your app	Synced via datasets	Built-in system models
Editable	Yes	Read-only (managed via datasets)	Read-only
Examples	companies, deals, tasks	HubSpot contacts, Google Sheets rows	users, agents, connections
Chat enabled	Yes	No	No

App Collections

Custom collections created when you describe data models. Fully editable and specific to your app. These are what you build.

Data Collections

External data synced via datasets (HubSpot, Sheets, Stripe, etc.). They appear automatically when you attach a dataset to your app. Read-only from the app, managed through the datasets interface.

Core Collections

Built-in system collections present in every app: users, agents, email templates, email logs, user field metadata, and connections. Read-only, managed by the platform.

Field types

When describing your data, you can specify field types to get exactly what you need:

Text fields

For names, descriptions, and other text content.

Prompt: "title (text)" or just "title"
Stores: Any text content
Example: "Enterprise License Renewal"

Number fields

For quantities, amounts, and measurements.

Prompt: "quantity (number)" or "amount"
Stores: Numeric values
Example: 42, 3.14, 1000000

Currency fields

For money values with proper formatting.

Prompt: "price (currency)" or "amount in dollars"
Stores: Numeric value
Displays: $1,234.56

Date fields

For dates and timestamps.

Prompt: "due date" or "created date"
Stores: Date and time
Displays: Formatted date

Option fields (Enums)

For selecting from a predefined list.

Prompt: "status (new, in progress, done)"
Stores: One of the specified options
Displays: Dropdown or badges

Yes/No fields (Boolean)

For true/false values.

Prompt: "is active (yes/no)" or "completed (checkbox)"
Stores: True or false
Displays: Checkbox or toggle

Reference fields

For linking to other data models.

Prompt: "assigned to (user)" or "belongs to company"
Stores: Link to another item
Displays: Name or selector

Relationships between data

One-to-Many

One item has multiple related items.

"Each project has many tasks"
"Tasks belong to a project"

This creates:

Project model (parent)
Task model with a “project” field linking to Project

Many-to-One

Multiple items link to one item.

"Contacts belong to a company"
"Deals are linked to a contact"

Self-referential

Items that reference the same type.

"Each employee has a manager (another employee)"
"Tasks can have subtasks"

Automatic fields

Every data model automatically includes:

Field	Purpose
`_id`	Unique identifier for each item
`createdAt`	When the item was created
`updatedAt`	When the item was last modified

You don’t need to ask for these - they’re always there.

Merging data from multiple sources

When the same type of data lives in different systems — for example, contacts in both HubSpot and a Google Sheet — you can create a data model that merges them into a single unified output.

How merging works

Connect each source to the data model
Define a merge key — a shared field (like email) used to match records across sources
Set source priority — choose which source takes precedence when the same field has different values
Map fields — select which columns to include, rename fields, and set types

The merged output can then be included in a dataset alongside your raw source data.

Example: Merging contacts from HubSpot and Google Sheets

You have contacts in HubSpot (with deal history) and a Google Sheet (with custom scoring). Both share an email column.

Create a Contacts data model
Connect HubSpot and Google Sheets as sources
Set email as the merge key
Set HubSpot as the primary source (higher priority)
Include the data model in your dataset

After sync, each contact has deal data from HubSpot and scoring from Google Sheets, matched by email. If both sources have a “company” field with different values, HubSpot’s value is used because it has higher priority.

Best practices

Be explicit about fields

Build a contact manager with:
- Full name
- Email address
- Phone number
- Company
- Job title
- Notes

Build a contact manager

Specify options for status fields

Status options: draft, pending, approved, rejected

Add a status field

Name relationships clearly

Each task belongs to one project
Each project has many tasks
Tasks can be assigned to one user

Tasks, projects, and users are related

Use meaningful field names

closeDate, expectedRevenue, primaryContact

date1, field2, ref

Viewing your data

Once your app is built, you can:

See all items in list views
Add new items through forms
Edit items by clicking on them
Delete items with confirmation
Filter and search to find specific items

Data persistence

All data in Gainable apps is:

Automatically saved when you create or edit items
Persistent across sessions and users
Backed up regularly
Accessible via API endpoints

You don’t need to worry about “saving” - data is stored as soon as you submit a form or complete an action.

Learn more

Datasets

Group external data sources and attach them to your app

Views

How your data is displayed

Real-time

Live data updates

​Understanding data in Gainable

​What is a data model?

​Collection types

​App Collections

​Data Collections

​Core Collections

​Field types

​Text fields

​Number fields

​Currency fields

​Date fields

​Option fields (Enums)

​Yes/No fields (Boolean)

​Reference fields

​Relationships between data

​One-to-Many

​Many-to-One

​Self-referential

​Automatic fields

​Merging data from multiple sources

​How merging works

​Best practices

​Be explicit about fields

​Specify options for status fields

​Name relationships clearly

​Use meaningful field names

​Viewing your data

​Data persistence

​Learn more

Datasets

Views

Real-time

Understanding data in Gainable

What is a data model?

Collection types

App Collections

Data Collections

Core Collections

Field types

Text fields

Number fields

Currency fields

Date fields

Option fields (Enums)

Yes/No fields (Boolean)

Reference fields

Relationships between data

One-to-Many

Many-to-One

Self-referential

Automatic fields

Merging data from multiple sources

How merging works

Best practices

Be explicit about fields

Specify options for status fields

Name relationships clearly

Use meaningful field names

Viewing your data

Data persistence

Learn more