Quote line dataset

Detailed line-item information for customer quotes, capturing the pricing decisions and behavioral data used in Commercial Pricing analysis.

The Quote Line dataset contains detailed line-item information for customer quotes. It represents individual pricing decisions made during the quoting process and provides the core behavioral data used to analyze quote performance.

This dataset is required for application deployment and pricing insights analysis.

→ For the canonical technical schema (data types, validation rules, request/response examples), see Quote Line in the API Guide.

Purpose

The commercial pricing solution uses the Quote Line dataset to:

• Analyze quoted prices at the product and customer level
• Evaluate win/loss behavior in response to pricing decisions
• Measure discounting patterns relative to list price
• Support pricing recommendations for future quotes

This dataset is essential for Quote Pricing use cases.

Required fields

About the Constraint column: every field listed must be provided in your data. Non-null means its value cannot be null; Nullable means the value may be sent as null (or left blank in your source). Primary key, Unique, and Foreign key describe the field's role in the data model; a Primary key is always non-null.

Field	Description	Type	Constraint
quote_id	Unique identifier for each quote.	string	Primary key
product_id	Unique identifier of the product.	string	Primary key
original_quote_id	Unique identifier of the original quote being re-optimised	string	Nullable
quote_status	Status of the quote (won, lost, pending, or archived). Used in model training to learn which quotes converted.	string	Nullable
price_list_id	Price list used for this quote.	string	Nullable
quoted_at	Timestamp when the record was quoted.	timestamp_tz	Non-null
decided_at	Timestamp when the quote was decided — when it was won, lost, cancelled, or replaced.	timestamp_tz	Nullable
customer_id	Unique identifier of the customer.	string	Nullable
merchant_id	Unique identifier of the merchant.	string	Nullable
project_id	Unique identifier of the project.	string	Nullable
region_id	Unique identifier of the region the quote applies to.	string	Primary key
quantity	Quantity of the product in the quote. Used as a feature in model training as the quantity demanded impacts the optimal price.	float	Non-null
selling_price	Selling price for the product. The price on the quote for the product. Used to calculate the discount applied to the quote for model training.	float	Non-null

Custom fields

The fields listed previously are the standard schema for this dataset. A field that your business requires but the standard schema does not include can be added as a custom attribute after the schema is rolled out. Custom attributes are additive, so they extend the dataset without affecting existing fields, and they are validated in the same way as standard fields.

The following request adds a DISCOUNT_REASON column to this dataset:

curl -X POST \
  'https://ingestion.peak.ai/api/v2/schema/QP_QUOTE_LINE_OOTB/add-attribute' \
  -H 'Authorization: YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "solutionName": "QP_OOTB",
    "columnName": "DISCOUNT_REASON",
    "dataType": "string",
    "validations": [
      { "type": "optional" }
    ]
  }'

The table name in the request URL is this dataset's fully qualified warehouse name, which includes the prefix and suffix configured when your solution was rolled out — QP_QUOTE_LINE_OOTB in this example. Retrieve the name for your own solution with Describe a solution's schema.

On standard tables the API prefixes the new column name in the warehouse with C_ (Snowflake) or c_ (Redshift), so DISCOUNT_REASON becomes C_DISCOUNT_REASON and warehouse queries must use the prefixed name.

→ For the full parameter reference, supported data types, and validation rules, see Customizations in the API Guide.

Usage notes

• All referenced identifiers (product_id, customer_id, merchant_id, project_id) must exist in their corresponding datasets.
• Quote lines should reflect actual quoted pricing behavior, not list prices.
• Quote stage information helps differentiate early-stage quotes from finalized quotes.
• Historical quote data improves pricing model accuracy and recommendation stability.
• Units of measure must align with related datasets (Products, Sales).