Account Information Update Agent

About the Agent

The Account Information Update Agent uses generative AI to automate account updates, capturing changes to addresses, contact information, and customer preferences from customer interactions. This eliminates manual data entry, freeing customer service teams to focus on complex inquiries and personalized support. The agent enhances customer satisfaction and improves service delivery by processing information in real-time and ensuring data accuracy. The agent's rigorous validation process maintains data integrity, minimizing errors and supporting personalized marketing and service offerings. Seamless integration with existing enterprise systems streamlines workflows and ensures a unified approach to account management, ultimately creating a smoother and more efficient customer journey.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Account Information Update Agent:

Account updates

Customer ID	First Name	Last Name	Address	City	State	Zip Code	Email	Phone Number	Preferences
1001	John	Smith	12 Elm St	New York	NY	10001	john.smith@valantir.com	548-098-4390	Email Only
1002	Emily	Johnson	46 Oak Rd	Los Angeles	CA	90001	emily.j@gmail.co	321-654-0987	SMS Only

Deliverable Example

Sample output delivered by the Account Information Update Agent:

Customer ID	First Name	Last Name	Update Status	Notes
1001	John	Smith	Success	Address and preferences updated
1002	Emily	Johnson	Failed	Invalid email format detected

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 Information Update Agent

About the Agent

Input Data Set

Deliverable Example

Company

Product

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In this personalized demo, you will:

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