Manage AI-Assisted features - Precisely Data Integrity Suite

Data Integrity Suite
Product
Spatial_Analytics
Data_Integration
Data_Enrichment
Data_Governance
Precisely_Data_Integrity_Suite
geo_addressing_1
Data_Observability
Data_Quality
dis_core_foundation
Services
Spatial Analytics
Data Integration
Data Enrichment
Data Governance
Geo Addressing
Data Observability
Data Quality
Core Foundation
ft:title
Data Integrity Suite
ft:locale
en-US
PublicationType
pt_product_guide
copyrightfirst
2000
copyrightlast
2026

The AI functionality within the Data Integrity Suite allows you to configure AI capabilities while maintaining strict adherence to security and acceptable AI policies. AI Manager supports integrating trusted and vetted Large Language Models (LLMs) so teams can control the models used in their workspace.

Note: You must have the Workspace Manager role to access the AI page. Also ensure that the required LLM connections are available in your workspace. For first-time setup, create a new LLM connection.

To access AI Manager:

  1. Go to Configuration > AI.
  2. The AI Manager is divided into two pages:
    • Features to enable and manage AI capabilities.
    • LLM Connections to create and manage reusable model connections.

These provide a structured approach to manage AI capabilities and LLM configurations at the workspace level.

Enable an AI feature

Use the Features page to enable and manage AI functionalities in the application. Each feature is associated with model configuration and an LLM connection.
  1. Go to Configuration > AI.
  2. Open the Features tab.

    The AI Manager offers a range of AI capabilities that may be tailored to specific operational needs.

    • Generate asset descriptions: The auto generation of asset description helps you create descriptions for your technical assets including datasets and fields making it easier for everyone to understand and find them in Catalog.
    • Country identifier: The country identifier enhances the Identify Country step in quality pipelines to predict Country Name or ISO Code based on a street address.
    • Generate test data for rule conditions: Generates test data from rule conditions to validate expected outcomes for condition logic.
    • Generate code for custom coding: AI assisted code generation for the custom coding in Data Quality pipelines.
    • Generate rule descriptions: Generates rule description based on dataset characteristics, pass conditions and dimension of the data quality rules.
    • Generate rule expression: Generates data quality rules expression based on user input or dataset characteristics.
    • LLM transform step:* Preview Transform data using natural language prompts with the LLM step in the Data Quality Pipeline.
    • Gio™ AI Assistant: Centralized control panel to view and manage all AI agents in one place. Provides unified access to LLM configurations and model selections across all agents.
  3. On the required feature card, click Configure.
  4. Select the associated LLM model or model family based on the feature.
  5. Select a LLM connection to associate with the feature.
  6. Click Test to validate the configuration.
  7. After successful validation, turn on Enable.
  8. Click Save.
Tip: Feature-specific cards can include additional options. For Gio™ AI Assistant, you can select agent modules and one or more model families.
Preview: This feature is currently in Preview for select workspaces and might be subject to change before general availability.

Configure LLM connections

The LLM connections page can be used to configure LLM connections at the workspace level.
Note: Once a new LLM connection has been established, it can be reused across all the features.

To configure this:

  1. Go to Configuration > AI.
  2. Select the LLM Connections tab.
  3. Click Add LLM Connection.
    1. Provide a name and a suitable description to the connection.
    2. Select the LLM provider. Currently, the Data Integrity Suite supports the AWS Bedrock LLM provider which would be extended to other models.
    3. Select the associated AWS region.
    4. Enter the details in the Authorization section. Enter the AWS access key ID and AWS secret access key.
    5. Once all the details are entered, click Test to validate the entered connection details. Upon successful validation a message would appear on the screen.
    6. Click Add. The configured LLM connection would be added to the list.
  4. Click the ellipsis next to the associated connection to Edit, Test or Delete the LLM connection.
Tip: Your AWS account must have read and write access to Bedrock, as well as access to the required LLM families (such as Claude Sonnet and Mistral). All other permissions are optional and are typically included only for convenience.