AI SCREEEN CONFIGURATION
Designing an AI screening configuration system
Designing an
AI screening
configuration
system
ROLE

UX Designer

UX Designer

UX Designer

EXPERTISE

UX/UI Design

UX/UI Design

UX/UI Design

YEAR

2025

2025

2025

Project description

Project description

Project description

Problem

Intervue enables companies to outsource tech interviews to expert engineers, preserving their internal bandwidth. However, screening 2,000–3,000 candidates to identify the top 5% tech talent remains time-consuming. To streamline this, we introduced AI Agents to conduct initial screenings via web and call-based interviews.

Timeline

From explorations to final designs in 5 weeks while working with multiple projects at the same time

Background

Hiring top-tier technical talent is critical for fast-growing companies. However, traditional interview processes are time-intensive, especially when scaling to thousands of candidates.
Intervue was built to solve this by helping companies outsource their tech interviews to skilled external engineers, protecting internal teams' productivity.
Yet, even with outsourcing, the volume of candidates posed a challenge — companies needed a faster, smarter way to filter potential hires without compromising quality.
This led to the introduction of AI Agents: autonomous interviewers designed to conduct the first layer of candidate screening through web-based and call interactions, helping companies efficiently identify top-performing talent while saving time and resources.

How did I navigate through the problem?

How did I navigate through the problem?

When I started exploring the challenges companies faced with technical hiring, it became clear that the problem wasn't just about conducting interviews — it was about screening at scale.

The primary goal for them was simple — quickly filter out low-fit candidates before investing real time, effort, and money into full interviews.


As I dug deeper, a few major pain points emerged:

As I dug deeper, a few major pain points emerged:

Manual pre-screening was painfully slow and couldn't keep up with scaling demands.

Users felt they had little control over how AI agents behaved or evaluated candidates.

There was a real fear that AI interviews might assess candidates inaccurately without the ability to fine-tune skills, questions, and tone.

Market Gaps Identified


  • Existing AI interview tools lacked deep configuration abilities

  • Most platforms focused only on final interviews, not early screening

  • No emotional personality setting (robotic agents reduced candidate engagement)

What did I come up with?

What did I come up with?

AI Screening Configuration Interface that allows companies to easily create and deploy customized AI interviewers.
Users can set skills, preferred designations, agent tone, question templates, and directly add candidates — all in a streamlined setup flow.

AI Screening Configuration Interface that allows companies to easily create and deploy customized AI interviewers.
Users can set skills, preferred designations, agent tone, question templates, and directly add candidates — all in a streamlined setup flow.

AI Screening Configuration Interface that allows companies to easily create and deploy customized AI interviewers.
Users can set skills, preferred designations, agent tone, question templates, and directly add candidates — all in a streamlined setup flow.

Define skills & requirements

Select key skills (e.g., Python, Java) selected by AI just by uploading your job description file and set seniority levels, designations, and evaluation rubrics to refine the interview process

Interview Agents listing

Makes the user (hiring manager) to pick an AI interviewer specialised in their required domain to conduct seamless, unbiased candidate assessments.

Invite candidates & get Insights

Send interview invites, let AI handle the screening, and receive in-depth reports with recommendations.

Configuration flow

Results

Results

Results

Here, the outcomes and achievements of the project are highlighted, including user feedback, adoption rates, and industry recognition.

Here, the outcomes and achievements of the project are highlighted, including user feedback, adoption rates, and industry recognition.

Here, the outcomes and achievements of the project are highlighted, including user feedback, adoption rates, and industry recognition.

Increased Efficiency

AI flow reduced interview setup time by 60%, from JD to candidate invite in under 10 minutes.

Better Screening Accuracy

80%+ of AI-screened candidates passed final tech rounds, showing high match quality.

Recruiter Productivity Boost

Recruiters handled 2x more candidates weekly with AI handling the initial screening.