Account Verification Agent

About the Agent

The Account Verification Agent uses generative AI to automate account verification, cross-referencing customer-provided data with internal records and external databases. This eliminates manual verification, freeing customer service teams to focus on building customer relationships and resolving complex issues. By accurately identifying authentic accounts, the agent streamlines customer onboarding, improves operational efficiency, and strengthens security measures. The agent effectively detects discrepancies and potential fraud, ensuring data integrity and building trust with customers. By automating this critical process, the agent reduces errors, improves accuracy, enhances customer satisfaction, and allows businesses to scale efficiently while maintaining the highest standards of security and service.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Account Verification Agent:

Account Verification Agent input

First Name	Last Name	Date of Birth	Email Address	Phone Number	Address	Account ID
Eddie	Martinez	1985-07-12	eddie.martinez22@gmail.com	+1-513-274-2011	73 Main St	1001
Stephanie	Leblanc	1992-05-22	steph.leblanc12@icloud.com	+61-682-109-1111	416 Elm St	1002

Deliverable Example

Sample output delivered by the Account Verification Agent:

Account ID	Verification Status	Issues
1001	Verified	None
1002	Failed	Address mismatch

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.

Account Verification Agent

About the Agent

Input Data Set

Deliverable Example

Company

Product

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