Agents-internal

About this Agent

The Contact Information Verification Agent is designed to streamline the lead generation process by automatically verifying the accuracy of contact information within your lead database. This AI digital agent ensures that your sales and marketing teams have access to up-to-date and accurate contact details, reducing the time spent on manual verification and increasing the efficiency of your prospecting efforts. By maintaining a clean and reliable database, your organization can improve communication with potential clients and enhance overall lead quality.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Contact Information Verification Agent:

Input data with phone and industry (1)

Name	Email	Phone	Industry
Rajesh Kumar	rajesh.kumar@healthcareindia.in	9876543210	Healthcare
Anjali Patel	anjali.patel@financeservices.co.in	9876543211	Finance
Arjun Singh	arjun.singh@retailmart.in	9876543212	Retail
Priya Sharma	priya.sharma@educationplus.edu.in	9876543213	Education
Amit Verma	amit.verma@techglobal.com	9876543214	Technology

Deliverable Example

Sample output delivered by the Contact Information Verification Agent:

Name	Email	Phone	Industry	Verification Status
Rajesh Kumar	rajesh.kumar@healthcareindia.in	9876543210	Healthcare	Verified
Anjali Patel	anjali.patel@financeservices.co.in	9876543211	Finance	Verified
Arjun Singh	arjun.singh@retailmart.in	9876543212	Retail	Verified
Priya Sharma	priya.sharma@educationplus.edu.in	9876543213	Education	Unverified
Amit Verma	amit.verma@techglobal.com	9876543214	Technology	Verified

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.

Contact Information Verification Agent

About this Agent

Input Data Set

Deliverable Example

Company

Product

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