Agents-internal

About this Agent

The Email Acknowledgment Agent is an AI-driven solution designed to enhance the efficiency of the recruitment process by automating the sending of acknowledgment emails. It ensures that candidates receive timely and consistent responses based on pre-defined criteria, improving communication and workflow efficiency within the talent acquisition process. By handling routine communication tasks, the Acknowledgment agent frees up HR professionals to focus on more strategic aspects of recruitment and staffing.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Email Acknowledgment Agent:

Candidate Details

Candidate Name	Experience Match	Skills Match	Education Match	Overall Fit	Score Matched
Lyle S. Walsh	Yes	Partial	Yes	Good	55

Deliverable Example

Sample output delivered by the Email Acknowledgment Agent:

Dear Lyle S. Walsh,

I hope this message finds you well.

We are pleased to inform you that your profile has been shortlisted for the Senior Software Engineer at Invescsa Technology. We were impressed with your qualifications and experience, and we look forward to moving forward in the selection process.

Our HR team will be in touch with you shortly to discuss the next steps and coordinate the interview process. Please keep an eye on your email for further details.

Thank you for your interest in joining our team. We look forward to speaking with you soon.

Best regards,

Martin G. Rush

HR-Talent Acquisition

Invescsa Technology

8881238765

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.

Email Acknowledgment Agent

About this Agent

Input Data Set

Deliverable Example

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