LiveRamp’s AI-Assisted Segment Builder
LiveRamp’s AI-Assisted Segment Builder in Connect turns natural language instructions into segment rules utilizing the LiveRamp Asset Management System.
Note
LiveRamp’s AI-Assisted Segment Builder is currently in limited release and is available to interested customers (depending on suitability).
The LiveRamp Asset Management System captures only metadata, such as table names, table descriptions, field names, field descriptions, dates, etc) and RampIDs that meet a specific criteria after a segment is built.
Note
For Snowflake activation customers, the only metadata captured is the metadata that is exposed through Snowflake directly (such as table names, table descriptions, field names, field descriptions, dates, etc.).
The AI portion of the segment builder is powered by a general‑purpose Gemini model used for inference only—we do not train on customer data. The AI only analyzes metadata (such as table/field names and descriptions, field types, date ranges, and other schema context) that exists in LiveRamp’s Asset Management System. With a “human-in-the-loop” approach, you have to review and approve the logic shown in our visual segment builder before anything is built.
For more information on how the AI-Assisted Segment Builder works, see the sections below.
For instructions on using LiveRamp’s AI-Assisted Segment Builder, see “Build a Segment”.
Data the AI Uses
The AI-Assisted Segment Builder only analyzes metadata that exists in your account LiveRamp’s Asset Management System. It does not analyze the underlying data. The AI-Assisted Segment Builder was designed to operate on structural context (the aboutness of data) rather than row‑level content.
See the table below for specific information on what data the AI-Assisted Segment Builder uses and what it does not use:
Used by the AI | Not Used by the AI |
|---|---|
Table names and table descriptions | Record‑level values (such as actual email addresses, phone numbers, or names) |
Column and field names and descriptions | Full customer profiles, purchase details tied to a person |
Data field types (string, date, numeric) and formats | Any sensitive identifiers (such custom IDs) |
Date ranges | Free‑text values from your rows |
Dataset and table tags and permissions | Enrichment or join keys in their raw values |
Metadata Used in an Example Prompt
Let’s look at an example where your segment building prompt is “Shoppers of ‘Outdoor Apparel’ with ≥3 purchases in the last 90 days; exclude employees and internal test accounts”.
Here’s an example of the metadata in your account that the AI-Assisted Segment Builder might utilize:
Tables names: “orders”, “product_category_map”, “customer_flags”
Field names and field types: “order_date” (date), “order_total” (decimal), “category_name” (string), “is_employee” (boolean)
Descriptions: "order_date" field: "UTC date order placed", “is_employee” field: "internal employee flag"
Dataset tags: “commerce”, “transactions”, “purchases”
Overall Segment Building Steps
Using the AI-Assisted Segment builder typically involves the following overall steps:
You describe the desired segment in natural language (such as, “High‑value shoppers of category X in the last 90 days; exclude employees”).
The AI-Assisted Segment Builder inspects permitted metadata from your data in the Asset Management system (schemas, field names/descriptions, types, and dataset tags).
The AI-Assisted Segment Builder creates a draft segment based on the metadata and displays those in the Visual Segment Builder, taking into account the permissions for that data.
You review the draft segment (rules and rationale) and edit before building, if necessary (editing can be done directly or you can describe your change in natural language).
You tell the AI-Assisted Segment Builder when you're ready to build the segment.
The approved rules run using your data in Connect, and creates a new segment that is registered in the Asset Management system and appears in the “Built Segments” area of the Segments page.
Once the segment has been built, you can use it to perform overlaps or to split the segment into multiple parts (for test and control, for example). You can also distribute the segment to your desired destinations. All Actions and changes are logged for review in the Activity History tab of the details page for that segment.
For detailed instructions, see “Build a Segment”.
Data Access and Security Model
The AI-Assisted Segment Builder works on the following principles:
Metadata‑only reasoning: AI-assisted segment building is permissioned to read schema metadata (names, descriptions, data types, tags, last‑updated dates). It is not permissioned to read raw rows or PII values.
Least‑privilege access: Visibility is limited to only those datasets/schemas you have explicitly configured within Connect. When datasets are removed, access is lost.
No PII egress: The AI-Assisted Segment Builder does not export or transmit your record‑level data to the AI for learning purposes. Prompts are constructed from metadata and your natural‑language request, and the Gemini inference model is used without reinforcement.
Account isolation: All reasoning and rule proposals occur within your account boundary in Connect, aligned with your role‑based access controls.
Logging and traceability: Prompts (metadata context only) are held in the user’s chat history. Creation details of the segment are held within the Segment Details in the LiveRamp Data Catalog.
Data retention: The AI-Assisted Segment Builder stores the final rules/logic, not your data. Retention of segment objects is at your direction within the platform.
Model use and training: The AI-Assisted Segment Builder uses a general‑purpose Gemini foundation model for inference only. We do not train or fine‑tune on your data, and we do not use your customer data or metadata for model training. Prompts to the model contain metadata‑only context (no PII). Product-level telemetry is collected for improvements to the user experience. For more information, see the section below.
Model Use and Training
The Gemini model used by the AI-Assisted Segment Builder works on the following principles:
Foundation model used: The AI-Assisted Segment Builder calls a general‑purpose Gemini model to translate your natural‑language prompt into proposed rules.
Inference‑only: The segment builder does not train or fine‑tune models on your datasets.
No training on your data: We do not use your customer data or metadata for model training. Gemini is used as a runtime inference service.
Metadata‑only prompts: Requests contain only permitted metadata (schema/field names and descriptions, types, tags) and your plain‑English query—never PII.
Cross‑tenant isolation: There are currently no cross‑customer learning capabilities. General telemetry data like thumbs up/down are utilized to improve the product experience.