About this Agent

The Lead Assignment Agent is an AI-powered solution designed to streamline the process of distributing sales leads. By leveraging predefined criteria, this intelligent digital agent automatically assigns incoming leads to the most suitable team member. This ensures that each lead is handled promptly and by the right sales professional, enhancing efficiency and increasing the chances of successful conversions. Ideal for large sales teams, this AI agent helps maintain a balanced workload and optimizes the lead management process.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Lead Assignment Agent:

Lead Assignment Criteria

The following criteria were used to assign the leads to the team members:

1. Region-Based Assignment:

- Leads are assigned to team members based on their expertise in specific regions.

- Example:

- Rahul Mehta (North) -> Assigned to Amit Desai

- Priya Singh (West) -> Assigned to Sneha Nair

2. Priority Consideration:

- Leads with high priority are handled by senior team members.

- Example:

- Vikram Sharma (High Priority, East) -> Assigned to Isha Rao

3. Expertise Matching:

- Team members have expertise in handling leads from particular regions.

- Example:

- Rohan Kumar specializes in handling leads from the South region, hence Ananya Patel was assigned to him.

These criteria ensure that leads are handled by the best-suited team member for optimal results.

Lead Name	Region	Priority
Rahul Mehta	North	High
Priya Singh	West	Medium
Ananya Patel	South	Low
Vikram Sharma	East	High

Deliverable Example

Sample output delivered by the Lead Assignment Agent:

Lead Name	Region	Assigned To
Rahul Mehta	North	Amit Desai
Priya Singh	West	Sneha Nair
Ananya Patel	South	Rohan Kumar
Vikram Sharma	East	Isha Rao

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.

Lead Assignment Agent

About this Agent

Input Data Set

Lead Assignment Criteria

Deliverable Example

Company

Product

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