hiringcycle.ai
hiringcycle.ai is a recruitment product that brings AI enablement to the hiring workflow, helping HR teams work with more consistent, better-informed processes.
BRAND
hiringcycle.ai
CLIENT
NBS HR
LOCATION
Istanbul, Turkey
INDUSTRY
Human Resources
RELEASED
2023
OVERVIEW
About the Project
HR teams can lose promising candidates during early screening, and building long and short lists after a job posting is one of the most time-consuming parts of talent acquisition.
The product was shaped to help HR teams use their time where judgement matters, support candidate evaluation, and keep the process consistent and traceable, from job definition through to shortlist.
The product was shaped to help HR teams use their time where judgement matters, support candidate evaluation, and keep the process consistent and traceable, from job definition through to shortlist.
01
Leveraging Artificial Intelligence
We needed to effectively incorporate AI support across all stages of the recruitment process, starting from defining requirements and creating job postings to generating interview questions and evaluating candidate suitability after the interview.
02
An Easy-to-Learn and User-Friendly Job Posting Interface
We were expected to design a user-friendly interface that facilitates the process starting from defining the open position by HR employees, progressing with the answers to questions asked by the AI, in an intuitively learnable manner.
03
Designing the Video Interview Experience
To reduce phone interviews and prevent losses at this stage, we needed to design an interview experience during the candidate pool creation phase that candidates find easy to use and feel comfortable with.
HOW WE DID?
Expertise
Team
2 * UX Designer
2 * UI Designer
4 * Front-End Developer
1 * Backend-End Developer
1 * Project Manager
2 * QA Engineer
Tech
React
Remix
NestJS
Typescript
Vitest
GraphQL
LLM Models
Storybook
Figma
Project Timeline
~6 Months
USER INTERVIEW
To design a product that HR professionals can effectively manage the recruitment process with, we started by listening to and understanding our target audience first. For this purpose, we identified personas and conducted interviews with professionals whom we segmented.
Segment
Recruitment Group Manager
Segment
Head of HR
Segment
Recruitment Specialist
Segment
Strategic Director
Benchmark Analysis
While listening to our target audience, we also conducted competitive analysis. We utilized similar applications in the industry and took note of the pain points we identified. Additionally, we examined products that stood out for their ease of use, even if they were from different sectors.
BRAINSTORMING & IDEATION
We held product development meetings with our project owners at Hiringcycle and BREW and our analysis, UX/UI teams, and development leader. Each team shared their vision of the ideal product based on the main briefs. We gathered the ideas and approaches from these meetings on our inspiration board.
USER FLOW & WIREFRAME
Before starting the designs, we created user flows from simple to complex. Based on these flows, we first made low-fidelity wireframes and then moved on to high-fidelity wireframes close to the UI level.
Solutions & Key Features
“In line with the needs of HR managers, we designed a dashboard to save time in the hiring process and simplify the assessment of candidates' skills.
Gülay Tekneci
Sr. UI Designer at BREW
Simple Interface
The control panel of hiringcycle.ai offers a clean interface designed according to the needs of HR managers, avoiding information overload. It aims to help HR professionals stay focused and complete their routine tasks quickly, thereby increasing efficiency.
AI-Powered Job Posting
When creating job postings for the project, we analyzed the users' selections for the role they're seeking with AI and provided suggestions on how to craft the most suitable posting.The AI turned the HR professional’s selections into text appropriate for the employer, the position, the candidate, and the platform where it will be posted. This sped up the process and ensured the job posting was tailored to the target audience.
UX AUDIT
Video Interview UX Analysis
During our beta tests, we observed that the number of candidates participating in video interviews was quite low compared to the number of invitations sent. To address this, we initiated a UX review to increase candidate participation in video interviews, improve their experience, and find potential solutions.
Our goal was to provide a seamless interview experience in one-way online video interviews, ensuring that candidates—regardless of their experience level—do not experience extra stress. This experience was crucial as it allowed candidates to focus on the interview process, which in turn significantly influenced their application journey and outcomes.
We aimed to measure the effectiveness of our work through increased video interview participation rates and user satisfaction surveys.
Our goal was to provide a seamless interview experience in one-way online video interviews, ensuring that candidates—regardless of their experience level—do not experience extra stress. This experience was crucial as it allowed candidates to focus on the interview process, which in turn significantly influenced their application journey and outcomes.
We aimed to measure the effectiveness of our work through increased video interview participation rates and user satisfaction surveys.
RESEARCH
User Interview
We conducted 30-minute user interviews with individuals who had experienced Hiringcycle.ai's video interview feature, encompassing users with diverse experiences and positions. Each interview was conducted separately, interactively, and online.
First, we created a set of questions, focusing on the goals of our discussion and the specific points we wanted to address. Ensuring the questions were open-ended and supplemented with follow-up questions like "Why?" and "How?" was a key aspect of our approach.
We collected the outputs from these interviews on our findings board and organized them into groups based on related topics.
First, we created a set of questions, focusing on the goals of our discussion and the specific points we wanted to address. Ensuring the questions were open-ended and supplemented with follow-up questions like "Why?" and "How?" was a key aspect of our approach.
We collected the outputs from these interviews on our findings board and organized them into groups based on related topics.User Behavior Analytics
To observe the behaviors of a broader candidate pool and make data-driven decisions, we chose Posthog. We created graphs and watched recordings to help analyze the current situation.
At the heart of our UX process, we listed potential solutions. Considering the project budget and time constraints, we identified the solutions we could implement in the first phase.
At the heart of our UX process, we listed potential solutions. Considering the project budget and time constraints, we identified the solutions we could implement in the first phase.
"Using the UX insights gathered from various tools was our primary resource when making design decisions and planning developments. This approach enabled us to achieve a project that was both satisfying and effective in terms of user experience."
Selin Çelik Sevim
UX Designer at BREW
FINAL DESIGN
Design Outputs
Candidate Video Interview Experience
To facilitate the candidate's video interview experience and help them adapt quickly to the process, we designed informative screens. During the interview, seeing their video on the screen made candidates uncomfortable and distracted them from focusing on the questions—reading and understanding them became more difficult. As an additional improvement, we implemented a feature allowing candidates to minimize or even close their video feed as they wish.
During the interview, seeing their video on the screen made candidates uncomfortable and distracted them from focusing on the questions—reading and understanding them became more difficult. As an additional improvement, we implemented a feature allowing candidates to minimize or even close their video feed as they wish.
Candidates were struggling to manage their interview time and couldn't find time to prepare for the next question while transitioning from one question to another, increasing their stress levels. To address this, we designed an improvement where candidates can track the remaining interview time.
We also added a short break period between questions to allow candidates to catch their breath and prepare for the next question.
Candidates were struggling to manage their interview time and couldn't find time to prepare for the next question while transitioning from one question to another, increasing their stress levels. To address this, we designed an improvement where candidates can track the remaining interview time.
We also added a short break period between questions to allow candidates to catch their breath and prepare for the next question.
Video Interview Evaluation Process with Artificial Intelligence
Before HR professionals begin evaluating the interview, we designed a preview stage where they can watch the candidate's video feed. If inappropriate or insufficient content is detected, the HR professional can tag the candidate, exclude them if necessary, or even schedule another interview with the candidate.During the interview evaluation stage, we established a structure where HR professionals can add their comments along with the feedback generated by artificial intelligence regarding the answers.
In the evaluation process, which proceeds in a question-and-answer format, we developed a shortcut for professionals to rewatch the candidate's response to a question if needed.
Authorized HR managers can track the candidate's evaluation process, review the assessments made by HR professionals, and make changes if necessary, ensuring continuity throughout the process.
Process Analysis
We visualized statistical data for all stages of the process starting from sending interview invitations to candidates accepting and attending the interview under the "Overview" section. This allowed HR professionals to quickly grasp the entirety of the project.
One of the challenges HR professionals faced when building the candidate pool was the inability to merge old data from other applications with current data. Similarly, they struggled to transfer processes conducted through different channels. To address this, we developed a function that allows data import from powerful third-party applications.
If a candidate applies for multiple open positions and these applications are managed by different professionals for different projects, the status of applications can be tracked from each project screen. This prevents process overlaps.
We developed auxiliary functions to easily manage shortlists and longlists. With these functions, users can move any candidate between lists, download their CV, view score details and video recordings, or delete candidates from the system.
If a candidate applies for multiple open positions and these applications are managed by different professionals for different projects, the status of applications can be tracked from each project screen. This prevents process overlaps.
We developed auxiliary functions to easily manage shortlists and longlists. With these functions, users can move any candidate between lists, download their CV, view score details and video recordings, or delete candidates from the system.
DESIGN
Design System
Basic Style Elements
Some fundamental components from the design system file
DEVELOPMENT
Development Approach and
Technology Choices
What technologies were used?
We handled both the Frontend and Backend development of the application.For the backend development of the application, we utilized frameworks and technologies such as NestJS, TypeScript, Hasura GraphQL Engine, and PostgreSQL.
For front-end development, we chose the React library and Remix framework. The product experience was designed for different user types, with clear, consistent interfaces that make the workflow easier to follow.
We continuously delivered our applications to users securely using Continuous Integration/Continuous Deployment (CI/CD) pipelines. To make our applications scalable and high-performing, we adopted a container architecture and ran them on Platform as a Service (PaaS). Through containerization, we ensured that our applications and dependencies could be distributed as a single package.
We utilized Natural Language Processing (NLP) models for functions such as job posting creation and video interview evaluation. During intensive R&D and productization processes, we used both commercial and open-source models for data validation and custom model generation purposes. We conducted tasks such as training models in the Turkish language, parameter optimization, and fine-tuning existing models.
How does it work?
The application consists of the following components:AI-Powered Job Posting Creation
The application can create a job posting from start to finish with recommendations made by AI for the desired role.Control Panel
Starting from creating job postings to receiving applications for roles and managing video interview processes, the dashboard used by HR professionals ensures the completion of the hiring journey. During this process, HR personnel can generate available metrics to optimize the hiring processes.Candidate Video Interview Process
Candidates can apply to posted job openings and then quickly progress through the video interview process. Videos are processed and evaluated by artificial intelligence, and candidates are scored on various metrics.These components together efficiently manage processes from creating job postings to evaluating candidates, providing a seamless hiring experience.
"We provided an end-to-end integrated solution for the application. We rapidly developed prototypes according to user needs and transformed them into products. We made the operation-intensive parts more efficient by using artificial intelligence models."
Osman Orhan
Software Development Lead at BREW
Ahu Bade Özkan
Co-Founder, hiringcycle.ai
“BREW’s involvement has been invaluable.”
Collaborating with BREW has been an outstanding experience for us. As a sister company that has significantly contributed to the development of our project and as a valued partner of our product, BREW's involvement has been invaluable. Our teams, already well-acquainted through our ongoing partnership, adopted a proactive approach to ensure the smooth management of the project. Professionalism was upheld at every stage, supported by clear and open communication through emails, virtual meetings, and one-on-one sessions. This transparent communication greatly enhanced the efficiency and success of our collaboration.
RESULTS
A simple and efficient usage,
unbiased equal opportunity
This end-to-end solution, built with principles of rapid prototyping and understanding user needs, offers a simple and effective usage for different user types from job posting creation to evaluating candidates via video interviews.
By blending modern NLP techniques with strong domain expertise and technical proficiency from project stakeholders, we created a unique solution. This agile approach accelerated the hiring processes, providing time and cost savings for HR teams and unbiased equal opportunities for candidates.
By blending modern NLP techniques with strong domain expertise and technical proficiency from project stakeholders, we created a unique solution. This agile approach accelerated the hiring processes, providing time and cost savings for HR teams and unbiased equal opportunities for candidates.
CONTACT
CONTACT