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Buck.ai Documentation

website·Indexed 32 days ago

https://docs.buck.ai/

Documentation for Buck.ai, an AI-powered dispatch and operations platform for service businesses. Includes guides on DispatchIQ, data synchronization with FSM software, integration with ServiceTitan and Twilio, and management of technicians, jobs, zones, and business units.

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What's inside Buck.ai

  1. Understand customer data synchronization in Buck.ai

    Customer data in Buck.ai is synced from your FSM (Field Service Management) software. Buck.ai acts as a read-only database for this information; updates to customer data are only possible by fetching new data through the FSM software integration.

  2. Understand DispatchIQ board types

    Buck.ai's DispatchIQ utilizes two types of dispatch boards to manage capacity:

    1. Scenario-based boards: Snapshots of dispatch operations at a specific timestamp resulting from a scenario.
    2. Real-time dispatch board: A live view of dispatch operations as they currently stand.

  3. Understand and manage Dispatch Scenarios

    A Scenario in Buck.ai is a saved template of dispatch settings used to avoid repetitive configuration. It allows users to define preferences—such as technician start locations, maximum drive distances, and auto-run schedules—and apply them to dispatch boards.

    Permissions: Only users with the Account Supervisor role are permitted to edit scenarios, as changes affect all dispatch boards created using that scenario.

  4. Edit or remove an existing Zone

    To modify or delete a zone, navigate to the Zones overview page and click the three-dots menu associated with the specific zone. From this menu, select Edit to change the zone's configuration or Remove to delete the zone.

  5. View detailed invoice and line item information

    To access the detailed view of a specific invoice, click on the invoice ID from the overview page. The Invoice page provides the following details:

    • Billing address
    • Customer information
    • Business Unit
    • Total invoice amount
    • Invoice items tab: Allows users to view and expand every individual item associated with the invoice.

  6. Manage and filter invoice data in the Invoices Overview

    The Invoices page serves as a read-only database of invoice data, which is used as the foundation for revenue predictions. Users can locate specific invoices by searching for the customer name or customer ID. Additionally, the list can be filtered by business unit, revenue, or invoice status.

  7. Understand Buck.ai data synchronization frequency

    Buck.ai automatically fetches and syncs data from your legacy FSM software every few hours. After the initial data import, data is refreshed every 4 to 24 hours, depending on the specific data type.

  8. Manage technicians in Buck.ai

    Technicians are managed via the Data/Technicians module. Note that only technicians marked as 'Managed by Buck.ai' can be assigned jobs on the platform.

  9. Understand Business Units in Buck.ai

    In Buck.ai, a Business Unit represents a team focused on a specific trade. Unlike many industry approaches, Buck.ai allows a single technician to be assigned to multiple business units and subgroups based on their skill matrix.

  10. Manage service areas using Zones in Buck.ai

    Zones allow you to divide a service area into smaller regions based on collections of zip codes. This enables the definition of specific rules for each area, such as determining which business units or subgroups are assigned to a zone and identifying neighboring zones that can be dispatched together.

  11. Analyze company performance using the Buck.ai Data Dashboard

    The Buck.ai data dashboard provides basic analytics on company performance. Users can toggle the data view to either analyze the entire data set or filter for specific managed entities, such as technicians, business units, and job types.

  12. Integrate Field Service Management (FSM) software with Buck.ai

    Buck.ai requires a Field Service Management (FSM) software (e.g., ServiceTitan, ServiceFusion, FieldEdge) to function.

    Key integration details:

    • Data Requirements: A minimum of 3-6 months of historical job data is required to run optimization models. Users operating on Excel are recommended to migrate to an FSM solution first.
    • Initial Sync: After the first integration, Buck.ai typically requires 24 hours to import data and train models.
    • Refresh Rate: Data is refreshed at intervals between 4 and 24 hours, depending on the data type.