About this Agent

The Resume Screening Worker is an AI-driven solution designed to streamline the recruitment process by automatically screening and analyzing resumes. It identifies key qualifications, relevant experience, and essential skills that match the job requirements, ensuring a more efficient and effective talent acquisition process. By leveraging generative AI, this digital worker enhances the ability of HR teams to quickly pinpoint the most suitable candidates, reducing time-to-hire and improving the quality of hires.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Resume Screening Agent:

Resume of Liam Smith

Name: Liam Smith

Email: liam.smith@gmail.com

Phone: 61-400-123456

Professional Summary

Experienced software engineer specializing in backend development and cloud infrastructure. Strong skills in team management and communication.

Work Experience

1. Senior Software Engineer at TechWave Solutions (2016-2023)

Led a team of 10 developers working on cloud service projects for various industries.

2. Software Engineer at ByteWorks (2013-2016)

Developed and maintained scalable RESTful APIs for e-commerce platforms.

Education

B.Sc. in Software Engineering, University of Melbourne, 2013

Skills

Python, Java, AWS, Docker, Kubernetes, Leadership

Certifications

AWS Certified Solutions Architect

Languages

English (Fluent)

Additional Information

Hackathon finalist, 2020

Job Title	Required Skills	Experience	Education	Certifications
Senior Software Engineer	Python, AWS, Docker, Team Leadership, Microservices	5+ years of experience	B.Sc. in Computer Science	Preferred: AWS Certification

Deliverable Example

Sample output delivered by the Resume Screening Agent:

Field	Data
Candidate Fit Score	90
Key Qualifications Matched	Python, AWS, Docker, Leadership, 7 years of experience, B.Sc. in Software Engineering
Missing Qualifications	Microservices
Recommendation	Proceed
Notes	Excellent fit with strong experience in backend development and leadership. Minor skill gap in Microservices.

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.

Resume Screening Agent

About this Agent

Input Data Set

Resume of Liam Smith

Professional Summary

Work Experience

Education

Skills

Certifications

Languages

Additional Information

Deliverable Example

Company

Product

Contact us

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In this personalized demo, you will:

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Engage in Q&A Session