Agents-internal

About this Agent

The Order Verification Agent is an AI-powered digital worker designed to enhance the efficiency and accuracy of order processing in customer support. This intelligent assistant automatically verifies crucial order details, including product availability, customer information, and payment confirmation. By ensuring all information is accurate before processing, the Order Verification Agent reduces errors, improves customer satisfaction, and optimizes the order handling workflow.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Order Verification Agent:

Customer ID	Name	Email	Phone	Address
CUST001	Benjamin Carter	benjamin.c@valantir.com	555-7394	91 Maple Street, Springfield, IL, 62704
CUST002	Olivia Brooks	olivia.br@valantir.com	555-9372	46 Oak Avenue, Metropolis, NY, 10128
CUST003	Alice Johnson	alice.johnson@valantir.com	555-8092	70 Pine Road, Gotham, CA, 90210

Product ID	Product Name	Stock Quantity	Warehouse Location
PROD1001	Wireless Mouse	50	A1
PROD1002	Mechanical Keyboard	0	B2
PROD1003	HD Monitor	10	C3

Order ID	Product ID	Product Name	Quantity	Price	Customer ID	Customer Name	Shipping Address	Payment Status
ORD1234	PROD1001	Wireless Mouse	2	25.99	CUST001	Benjamin Carter	91 Maple Street, Springfield, IL, 62704	Paid
ORD1235	PROD1002	Mechanical Keyboard	1	79.99	CUST002	Olivia Brooks	46 Oak Avenue, Metropolis, NY, 10128	Pending
ORD1236	PROD1003	HD Monitor	1	189.99	CUST003	Alice Johnson	70 Pine Road, Gotham, CA, 90210	Paid

Order ID	Payment Confirmation Number	Payment Status	Amount Paid
ORD1234	PAY1001	Paid	51.98
ORD1235	PAY1002	Pending	0.0
ORD1236	PAY1003	Paid	189.99

Deliverable Example

Sample output delivered by the Order Verification Agent:

Order ID	Verification Status	Remarks	Product Verification (Pass/Fail)	Customer Verification (Pass/Fail)	Payment Verification (Pass/Fail)	Date
ORD1234	Pass	All checks passed	Pass	Pass	Pass	2024-10-11
ORD1235	Fail	Product not available, Payment not confirmed	Fail	Pass	Fail	2024-10-11
ORD1236	Pass	All checks passed	Pass	Pass	Pass	2024-10-11

ZBrain credits and their usage

A credit is a unit of usage on ZBrain. Credits are consumed whenever you perform actions such as embedding documents or querying an app. Here’s how credits are utilized:

Document Embedding:

When you upload a document, it is processed and embedded to create a searchable vector representation. This step consumes credits based on the size of the document and the embedding model used.

Input Processing:

When you ask a question, the relevant context is retrieved from the embeddings and processed by the Large Language Model (LLM). This step incurs a credit cost based on the model and the number of input tokens.

Generating Output

The LLM then generates a response based on the processed input, consuming additional credits based on the number of output tokens.

The cost in credits for various models and processes is detailed below:

Models and Credit Consumption:

Model	Input Cost	Output Cost
GPT-4o	2,500 credits / 1M tokens	7,500 credits / 1M tokens
GPT-4	15,000 credits / 1M tokens	30,000 credits / 1M tokens
GPT-4-32k	30,000 credits / 1M tokens	60,000 credits / 1M tokens
GPT-3.5 Turbo	250 credits / 1M tokens	750 credits / 1M tokens

Embedding Models:

Model	Input Cost
text-embedding-3-small	20 credits / 1M tokens
text-embedding-3-large	130 credits / 1M tokens
ada v2	100 credits / 1M tokens

When you create a knowledge base or query an app, credits are deducted based on the embedding, input, and output token usage. This ensures you only pay for what you use while leveraging the full power of advanced AI models.

Order Verification Agent

About this Agent

Input Data Set

Deliverable Example

Company

Product

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