Key Takeaways

• Console's Schema is a categorization framework that unifies request types and custom fields, reading the natural-language request to infer its type automatically instead of making users pick a category from a dropdown.

• Request types (laptop refresh, access request, security incident, onboarding) are fully customizable and can map directly to Jira issue types, flipping the traditional model where users classify their own tickets on a form.

• Custom fields attach dynamically per request type, so a laptop refresh surfaces device and serial number while an access request surfaces app, access level, and justification, giving agents structured context upfront.

• AI Fill auto-populates those custom fields by querying connected systems in natural language, for example pulling device data from Jamf or deal data from Salesforce, and updates in real time as the request evolves.

• Categorization-driven analytics work out of the box: because every ticket is structured on arrival, teams filter by request type, SLA status, or channel source and track trends without any custom reporting setup.

What Is Console’s Schema System?

Console’s categorization framework is called the Schema. The Schema combines request types and custom fields into a single unified system.

Rather than requiring users to select a category from a dropdown, Console reads the natural language request and infers the request type automatically. Setting a request type automatically pulls in the relevant custom fields for that type.

This flips the traditional Jira model. Instead of users filling out a form that defines what kind of request it is, the AI reads the message and does it for them.

Request types are fully customizable. Teams can rename, add, or edit them to match their own nomenclature.

 Examples include:

• Laptop refresh

• Access request

• Salesforce issue

• Security incident

• Onboarding

If Jira is the backend ticketing system, request types can map directly to Jira issue types and fields.

Custom fields

Each request type has its own associated custom fields that are relevant only to that type. Custom fields are dynamic. A laptop refresh might show device type and serial number fields. An access request might show app name, access level, and justification.

Custom fields support multiple data types:

• Freeform text

• Dropdowns

• Booleans

All custom fields are fully editable and renameable by admins.

Because the Schema automatically applies the correct custom fields when a request type is identified, agents receive structured context upfront without manual information gathering. Custom fields are also the underlying data powering analytics filtering and reporting.

AI Fill for custom fields

Console can auto-populate custom fields by querying connected systems. This functionality is called AI Fill.

AI Fill is configured per field using natural language instructions, for example: pull device info from Jamf for the requester. Admins use a hashtag (#) to reference specific actions from connected integrations inside the AI Fill prompt.

Examples of AI Fill include:

• Populating an assigned laptop field by calling the hardware asset action from Omnissa or Jamf

• Pulling account name, account owner, and open opportunities from Salesforce when a RevOps ticket mentions a deal

• Retrieving relevant user data from connected systems

Console parses the API response and maps the relevant data to the correct custom field automatically.

AI Fill functions like a step inside a playbook. Multiple fields on the same ticket can each have their own 

AI Fill logic pulling from different systems simultaneously. As the conversation between the user and agent evolves, AI Fill data can update in real time to reflect new context.

Categorization-driven analytics

Because every ticket is consistently categorized on arrival, the underlying data is structured and ready for reporting. Console provides out-of-the-box analytics with no custom setup required. 

Tickets can be filtered and sorted by:

• Request type

• Assignee

• SLA status

• Channel source

• Playbook triggered

Custom dashboard views can be saved and shared so teams open the view most relevant to their work.

Consistent categorization enables trend analysis over time, such as comparing how many laptop refresh requests came in last quarter versus this quarter. Both Kanban view and table view are available so teams can choose the visualization that fits their workflow.

FAQs

How does the Console Schema decide a request type without a dropdown?

Console reads the natural-language request and infers the request type from what the message actually says, rather than asking the employee to pick a category. The Schema is Console's categorization framework, and it pairs each request type with the custom fields relevant to it, so identifying the type (laptop refresh, access request, security incident, onboarding) automatically pulls in the right fields. This is the inverse of the traditional model where a user fills out a form that defines the request; here the AI reads the message and does that classification for them.

How customizable are request types and custom fields?

Fully. Teams can rename, add, or edit request types to match their own nomenclature, and each type carries its own custom fields that admins can edit and rename. Custom fields are dynamic, so a laptop refresh shows device type and serial number while an access request shows app name, access level, and justification, and they support several data types: freeform text, dropdowns, and booleans. Because the correct fields apply automatically when a request type is identified, the customization sets up once and then structures every matching ticket without further manual work.

How does AI Fill auto-populate custom fields, and what can it pull from?

AI Fill fills custom fields automatically by querying connected systems, so an agent does not have to look the data up. It is configured per field with natural-language instructions (for example, pull device info from Jamf for the requester), and admins reference specific integration actions with a hashtag inside the prompt. In practice it can populate an assigned-laptop field from Omnissa or Jamf, or pull account name, owner, and open opportunities from Salesforce when a ticket mentions a deal. Console parses the API response and maps each value to the correct field.

Does the Console Schema work for MSPs managing multiple clients?

Yes, though the categorization has to stay isolated per client. An MSP serves many client organizations, each with its own request types, custom fields, and connected systems, so the Schema is configured per client environment rather than shared across all of them. Done well, every client's tickets get categorized against their own taxonomy without one client's setup bleeding into another's. For the broader picture, see how AI ticket management for MSPs handles multi-client volume.

Do I need to set up analytics manually to report on ticket data?

No. Because every ticket is categorized consistently on arrival, the underlying data is already structured, and Console provides out-of-the-box analytics with no custom setup required. Tickets can be filtered and sorted by request type, assignee, service level agreement (SLA) status, channel source, and which playbook was triggered. Custom dashboard views can be saved and shared, both Kanban and table views are available, and consistent categorization is what makes trend analysis possible, such as comparing how many laptop refresh requests came in last quarter versus this one.