Agents-internal

About this Agent

The Supplier Performance Monitoring Agent is a powerful AI-driven tool designed to enhance supplier management by continuously tracking and evaluating supplier performance. This digital worker provides valuable insights into key performance indicators such as delivery times, product quality, and compliance with contractual terms. By offering real-time analytics and reporting, the agent helps procurement teams make informed decisions, improve supplier relationships, and ensure the highest standards in supply chain operations.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Supplier Performance Monitoring Agent:

Supplier performance input

Supplier Name	Contract ID	Delivery Date	Product	Quality Rating	Compliance Status	Delivery Time (Days)
Anderson Supplies	CNT-12345	2024-09-20	Steel Rods	8/10	Compliant	5
Baker Industries	CNT-98765	2024-09-18	Aluminum Sheets	9/10	Non-Compliant	2
Carter Materials	CNT-45678	2024-09-15	Copper Wires	7/10	Compliant	4
Douglas Co.	CNT-56789	2024-09-22	Plastic Panels	6/10	Compliant	7
Edwards Supplies	CNT-67890	2024-09-17	Glass Sheets	9/10	Non-Compliant	3

Deliverable Example

Sample output delivered by the Supplier Performance Monitoring Agent:

Supplier Name	Contract ID	Average Delivery Time (Days)	Average Quality Rating	Compliance Percentage	Comments
Anderson Supplies	CNT-12345	5	8/10	100%	On-time delivery, good quality
Baker Industries	CNT-98765	2	9/10	0%	Fast delivery, non-compliant
Carter Materials	CNT-45678	4	7/10	100%	Good performance, needs improvement
Douglas Co.	CNT-56789	7	6/10	100%	Slow delivery, acceptable quality
Edwards Supplies	CNT-67890	3	9/10	0%	Fast delivery, non-compliant

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.

Supplier Performance Monitoring Agent

About this Agent

Input Data Set

Deliverable Example

Company

Product

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