Agents-internal

About this Agent

The Vendor Data Validation Agent is an AI-powered digital assistant crafted for the Treasury department's Vendor Management sub-department. This sophisticated agent automates the critical process of validating vendor data, ensuring all information adheres to predefined accuracy and compliance standards. Utilizing advanced algorithms, it swiftly cross-checks vendor details, identifying and flagging discrepancies or non-compliance issues. This enhances the reliability of vendor data, streamlines the vendor management process, reduces manual effort, and minimizes errors, ultimately boosting efficiency and trust in vendor interactions.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Vendor Data Validation Agent:

Vendor Master Data 20241003

Vendor ID	Vendor Name	Contact Person	Phone Number	Email Address	Address	Contract Start Date	Contract End Date	Payment Terms	Account Manager	Warehouse Location	Warehouse Manager
VENDOR_084	FreshHarvest Co.	William King	555-3801	freshharvestco.@suppliers.com	202 Capitol Mall, Miami, CO, 21201, United States	2021-12-23 16:26:56.032064128	2025-01-23 09:45:48.697394792	Net 30	Emma Harris	156 Nicollet Mall, New Orleans, MA, 70112, United States	Michael Johnson
VENDOR_054	FrozenBite Foods	John Doe	555-5486	frozenbitefoods@suppliers.com	136 Boylston St, Anchorage, VA, 99501, United States	2021-03-20 17:30:25.250501000	2024-08-24 06:18:02.164328656	Net 60	Ava Smith	425 Ocean Dr, Phoenix, GA, 80202, United States	James Anderson

Deliverable Example

Sample output delivered by the Vendor Data Validation Agent:

Vendor ID	Vendor Name	Validation Status	Discrepancy
VENDOR_084	FreshHarvest Co.	Valid	None
VENDOR_054	FrozenBite Foods	Invalid	Contract expired

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.

Vendor Data Validation Agent

About this Agent

Input Data Set

Deliverable Example

Company

Product

Contact us

Get more information

In this personalized demo, you will:

Explore ZBrain Platform

Discover Workflow Integrations

Engage in Q&A Session