Agents-internal

About this Agent

The Prospect Segmentation Agent is an AI-driven solution designed to automatically categorize prospects into distinct segments based on their engagement and interaction history. This advanced segmentation process enables businesses to tailor their marketing and communication strategies to effectively target each group, enhancing overall engagement and conversion rates. By leveraging historical interaction data, the AI agent ensures accurate and dynamic segmentation, allowing for more personalized and impactful outreach efforts.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Prospect Segmentation Agent:

Prospect data (1)

Prospect Name	Emails Opened	Clicks	Time on Website (mins)
Ethan Wright	13	22	85
Olivia Bennett	9	18	47
Daniel Carter	3	10	36
Sophia Mitchell	7	15	65
Liam Turner	5	8	23

Deliverable Example

Sample output delivered by the Prospect Segmentation Agent:

Prospect Name	Emails Opened	Clicks	Time on Website (mins)	Segment
Ethan Wright	13	22	85	Highly Engaged
Olivia Bennett	9	18	47	Moderately Engaged
Daniel Carter	3	10	36	Low Engagement
Sophia Mitchell	7	15	65	Moderately Engaged
Liam Turner	5	8	23	Low Engagement

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.

Prospect Segmentation Agent

About this Agent

Input Data Set

Deliverable Example

Company

Product

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