Agents-internal

About this Agent

The Purchase Order Prioritization Agent is designed to optimize the purchase order management process by intelligently prioritizing orders. Leveraging data on vendor performance, delivery schedules, and procurement urgency, this AI digital worker ensures that purchase orders are fulfilled in a timely and efficient manner. By focusing on the most critical orders, organizations can enhance their supply chain operations, reduce delays, and improve overall vendor relationships. This solution is perfect for procurement teams looking to streamline their processes and ensure that essential goods are delivered without interruption.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Purchase Order Prioritization Agent:

Purchase order input

Purchase Order ID	Vendor Name	Order Date	Delivery Date	Procurement Urgency	Vendor Performance
PO1001	Acme Supplies	2024-10-01	2024-10-15	High	Excellent
PO1002	Global Tech	2024-10-03	2024-10-17	Medium	Good
PO1003	Metro Distributors	2024-10-04	2024-10-20	High	Fair
PO1004	Northern Goods	2024-10-05	2024-10-25	Low	Excellent

Deliverable Example

Sample output delivered by the Purchase Order Prioritization Agent:

Purchase Order ID	Vendor Name	Priority Level	Reason for Priority
PO1001	Acme Supplies	1	High urgency, Excellent vendor performance
PO1003	Metro Distributors	2	High urgency, Fair vendor performance
PO1002	Global Tech	3	Medium urgency, Good vendor performance
PO1004	Northern Goods	4	Low urgency, Average vendor performance

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.

Purchase Order Prioritization Agent

About this Agent

Input Data Set

Deliverable Example

Company

Product

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