Applicant Scoring

Score applicants based on CVs, job description, and structured scorecards.

Model Selection

Input

List of PDF URLs containing applicant CVs/resumes to be evaluated. Each CV will be analyzed and scored individually.

URL to a PDF file containing the applicant's CV or resume

https://storage.googleapis.com/tryitnow-ai-storage/5adec113-7bc5-4c0a-88d8-0c5eef0bbba1_Example-Resume.pdf

The complete job description including required skills, qualifications, responsibilities, and any other relevant criteria that will be used to evaluate applicants

Let AI generate score cards based on job description. If enabled, you don't need to manually define score cards.

Define custom scoring categories and evaluation criteria. Each score card represents a different aspect to evaluate (e.g., Technical Skills, Experience, Education, Cultural Fit). Applicants will receive a score (0-100) and detailed rationales for each score card.

The name or title of this scoring category (e.g., 'Technical Skills', 'Work Experience', 'Education')

Detailed evaluation criteria for this score card. Describe what aspects should be considered and how they should be evaluated (e.g., 'Evaluate programming languages, frameworks, and technical certifications relevant to the role')

The name or title of this scoring category (e.g., 'Technical Skills', 'Work Experience', 'Education')

Detailed evaluation criteria for this score card. Describe what aspects should be considered and how they should be evaluated (e.g., 'Evaluate programming languages, frameworks, and technical certifications relevant to the role')

The name or title of this scoring category (e.g., 'Technical Skills', 'Work Experience', 'Education')

Detailed evaluation criteria for this score card. Describe what aspects should be considered and how they should be evaluated (e.g., 'Evaluate programming languages, frameworks, and technical certifications relevant to the role')

The name or title of this scoring category (e.g., 'Technical Skills', 'Work Experience', 'Education')

Detailed evaluation criteria for this score card. Describe what aspects should be considered and how they should be evaluated (e.g., 'Evaluate programming languages, frameworks, and technical certifications relevant to the role')

Output
John Doe
View CV
Strong Match90
Based on 4 score cards

Technical Skills

95
/ 100
Rationales
positive

Demonstrated strong proficiency in Python, FastAPI, and PostgreSQL through extensive experience and explicit skill listing.

positive

Led schema-driven API design, developed multi-tenant SaaS APIs, and delivered REST APIs, showcasing excellent API design skills.

positive

Holds AWS and GCP certifications, completed an MLOps cloud computing certificate, and has experience with Docker and Kubernetes, indicating strong cloud infrastructure knowledge.

positive

University focus on distributed systems, experience with scalable backend architectures, and projects like 'Event Processing Toolkit' highlight deep understanding of distributed systems.

Experience Match

90
/ 100
Rationales
positive

Over 4.5 years of relevant professional experience, including Senior and Staff Backend Engineer roles, directly aligning with the 'Senior Backend Engineer' requirement.

positive

Experience directly matches key technologies (Python, FastAPI, PostgreSQL) and domains (cloud, distributed systems) mentioned in the job description.

positive

Experience in designing high-throughput backend platforms, real-time analytics, and AI-assisted systems is highly relevant to the role.

neutral

While experience is strong, the total years of experience might be on the lower end for some 'Staff' level roles, though perfectly suitable for a 'Senior' role.

Communication

95
/ 100
Rationales
positive

The CV is exceptionally clear, well-structured, and easy to navigate, enhancing readability.

positive

Writing quality is professional and concise, with impactful bullet points detailing achievements and responsibilities.

positive

Clearly articulates complex technical contributions and their impact (e.g., 'reducing production incidents by 40%', 'optimizing p95 latency by ~30%'), demonstrating strong communication skills.

positive

Publications on 'Designing Contract-First APIs for Scale' and 'Reliable Background Processing' further showcase the ability to explain complex technical topics.

AI/ML Knowledge

80
/ 100
Rationales
positive

University focus on 'applied machine learning' and a Professional Certificate in 'Cloud Computing & MLOps' demonstrate foundational and practical AI/ML knowledge.

positive

Experience supporting 'AI-assisted decision systems' and developing a 'Resume Analyzer (ATS)' with 'explainable scorecards' indicates practical application of AI workflows.

neutral

While strong in AI workflows and MLOps, there is no explicit mention of experience with vector databases, which was a bonus requirement.

About Applicant Scoring

Score applicants based on CVs, job description, and structured scorecards.

How It Works

1Upload

Provide your inputs in the Playground tab — upload images, enter text, or configure parameters.

2Run

Click the run button to process your inputs through AI models — no coding or setup required.

3Get Results

View and download your AI-generated results instantly, right in your browser.

Inputs

(4 fields)
Applicant CVsarrayRequired

List of PDF URLs containing applicant CVs/resumes to be evaluated. Each CV will be analyzed and scored individually.

Job DescriptiontextareaRequired

The complete job description including required skills, qualifications, responsibilities, and any other relevant criteria that will be used to evaluate applicants

Auto-generate Score Cardsboolean

Let AI generate score cards based on job description. If enabled, you don't need to manually define score cards.

Score CardsarrayRequired

Define custom scoring categories and evaluation criteria. Each score card represents a different aspect to evaluate (e.g., Technical Skills, Experience, Education, Cultural Fit). Applicants will receive a score (0-100) and detailed rationales for each score card.

Outputs

(1 field)
Scoring Resultsarray

Comprehensive scoring results for each applicant, including individual scores per category and detailed rationales explaining the evaluation

API Access

Integrate this use case into your application via our REST API. Switch to the API tab to see the endpoint, request format, and code examples in multiple languages.