Agents-internal

About this Agent

The Vendor Onboarding Agent is an advanced AI digital agent crafted to automate and streamline the vendor onboarding process. This solution effectively handles document collection and verification workflows, significantly reducing manual effort and minimizing errors. By automating these crucial tasks, it ensures a quicker and more accurate onboarding process, enabling procurement departments to concentrate on strategic initiatives and foster strong vendor relationships.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Vendor Onboarding Agent:

VendorID	W9FormSubmitted	BusinessLicenseSubmitted	InsuranceCertificateSubmitted	Status
V001	Yes	Yes	No	Pending
V002	Yes	No	Yes	Incomplete

VendorID	W9FormVerified	BusinessLicenseVerified	InsuranceCertificateVerified	VerificationStatus
V001	Yes	Yes	No	Pending
V002	Yes	No	Yes	Incomplete

VendorID	CompanyName	ContactName	Email	Phone	BankAccountNumber	RoutingNumber	Address
V001	Acme Corp	Sarah Davis	sarah.davis@acme.com	123-456-7890	123456789	987654321	123 Main St, Springfield, IL
V002	Global Solutions	Joshua Wilson	joshua.wilson@global.com	234-567-8901	234567890	876543210	456 Elm St, Austin, TX

Deliverable Example

Sample output delivered by the Vendor Onboarding Agent:

VendorID	CompanyName	OnboardingStatus	ApprovalDate
V001	Acme Corp	Approved	2024-10-10
V002	Global Solutions	Rejected	N/A

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 Onboarding 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