Hiring Platform Design & Dev for Talent-Searching Company
- AI Development Custom Software Development UX/UI Design
- $10,000 to $49,999
- Apr. - July 2024
- Quality
- 5.0
- Schedule
- 5.0
- Cost
- 5.0
- Willing to Refer
- 4.5
"Overall, their ability to manage the project and meet our expectations has been outstanding."
- Human Resources
- Noida, India
- 51-200 Employees
- Online Review
- Verified
Neuronimbus developed an AI-powered hiring platform for a talent-searching company. The team incorporated a candidate application tracking system into the platform and developed various AI-driven features.
Thanks to Neuronimbus, the hiring platform was launched, and the client used it to run their primary business. The team had an excellent project management style and maintained clear communication with the client. Their teammates were always proactive and responsive to the client’s requirements.
The client submitted this review online.
BACKGROUND
Please describe your company and position.
I am the Co-Founder of TalentOnLease.
Describe what your company does in a single sentence.
AI-Powered Tech Hiring Platform-Making available the Right Resources at the Right Time and at Right Cost.
OPPORTUNITY / CHALLENGE
What specific goals or objectives did you hire Neuronimbus to accomplish?
- To Develop our AI Tech Hiring Platform
- To Get the Technical Support in Gen AI Releated Work
SOLUTION
How did you find Neuronimbus?
Referral
Why did you select Neuronimbus over others?
- High ratings
- Pricing fit our budget
- Great culture fit
- Good value for cost
- Referred to me
- Company values aligned
How many teammates from Neuronimbus were assigned to this project?
6-10 Employees
Describe the scope of work in detail. Please include a summary of key deliverables.
The project aims to design, develop, and implement a comprehensive AI-driven tech hiring platform that incorporates a Candidate Application Tracking System (ATS). The platform will streamline recruitment processes, optimize candidate experience, enhance employer decision-making, and automate hiring workflows through AI-driven insights. Objectives:
- Develop an intuitive AI-powered platform to match employers with the best-fit candidates.
- Automate the initial stages of candidate screening, focusing on both technical and non-technical skills.
- Provide detailed candidate tracking capabilities to streamline the hiring process.
- Ensure data-driven decision-making through AI tools that analyze candidate profiles and predict job suitability.
- Improve recruitment efficiency and reduce the time-to-hire by automating repetitive tasks.
Deliverables:
- AI-Driven Features
- Automated Resume Screening:
- Use AI algorithms to screen resumes based on skills, experiences, and qualifications.
- Categorization of skills into technical and non-technical.
- Filtering and ranking candidates using AI-driven matching algorithms.
- Candidate Skill Matching:
- AI-based candidate-job matching, leveraging skill tags and job descriptions.
- Personalized job recommendations based on candidates’ profiles.
- Machine learning algorithms for continuous improvement of match accuracy.
- Interview Prediction & Scheduling:
- Predict candidate performance in interviews based on historical data and resume patterns.
- AI-driven automated interview scheduling and reminders.
- Chatbots for Candidate Interaction:
- AI-powered chatbots to engage with candidates, answer FAQs, and guide them through the application process.
- Provide real-time feedback to candidates about their application status.
- Automated Resume Screening:
- Application Tracking System (ATS) Features
- Centralized Candidate Database:
- Store and manage candidate profiles, resumes, and applications in a structured database.
- Search and filter candidates by job role, skills, qualifications, and more.
- Customizable Candidate Pipelines:
- Customizable hiring stages (application, screening, interview, offer, etc.).
- Visual pipeline tracking of candidates’ progress through the hiring funnel.
- Collaboration Tools for Hiring Teams:
- Role-based access for HR teams, recruiters, and hiring managers.
- Real-time collaboration and notes on candidate profiles.
- AI recommendations on candidate fit based on role-specific criteria.
- Reporting and Analytics:
- Generate customizable reports on candidate sources, time-to-hire, conversion rates, and more.
- AI-driven analytics to identify hiring trends, inefficiencies, and candidate demographics.
- Centralized Candidate Database:
- Candidate Experience Features
- User-Friendly Candidate Interface:
- Mobile-friendly application process with real-time application tracking.
- AI-powered assistance for resume building and skill tagging.
- Personalized Job Alerts:
- AI-driven notifications for job openings based on candidates' profiles and career goals.
- Application Feedback Loop:
- Provide candidates with AI-generated feedback on their resume, skills, and application status.
- User-Friendly Candidate Interface:
- Technical Requirements
- Platform Architecture
- Backend:
- Build a scalable backend using cloud infrastructure (e.g., AWS, Azure, Google Cloud) for efficient handling of large-scale candidate data.
- AI and machine learning models hosted in a secure and optimized environment.
- Frontend:
- Develop an intuitive, mobile-responsive UI using modern web frameworks (e.g., React, Angular).
- Cross-browser and device compatibility.
- Database:
- A secure, scalable database (e.g., PostgreSQL, MongoDB) to store user profiles, job listings, and applicant data.
- Security and Compliance:
- Adhere to data privacy regulations such as GDPR and CCPA.
- Secure handling of candidate data with end-to-end encryption.
- Backend:
- AI/ML Integration
- Data Collection:Leverage large datasets for training AI models, including anonymized candidate performance and hiring outcomes.
- Natural Language Processing (NLP):Use NLP to parse resumes, job descriptions, and application inputs.
- Machine Learning Algorithms:AI/ML algorithms for candidate-job matching, predictive analytics, and personalized job recommendations.
- Platform Architecture
- Project Timeline
- Phase 1: Discovery & Requirements Gathering (2-3 weeks)Define key features, platform architecture, user personas, and detailed technical requirements.
- Phase 2: Design & Prototyping (3-4 weeks)Develop wireframes, mockups, and a prototype of the platform.
- Phase 3:
- AI Model Development & Backend Integration (6-8 weeks)Build and train AI models for candidate screening, job matching, and analytics.
- Develop the backend architecture and integrate the ATS functionality.
- Phase 4:
- Frontend Development & ATS Implementation (6-8 weeks)Develop the user interface for candidates, recruiters, and hiring teams.
- Implement the ATS functionality and integrate with the AI models.
- Phase 5: Testing & Iteration (4-6 weeks)Conduct extensive platform testing for functionality, security, and performance.
- Phase 6: Launch & Maintenance (Ongoing)Launch the platform and provide ongoing support, AI model optimization, and feature updates.
- Key Performance Indicators (KPIs)
- Recruiter Efficiency: Measured by reduction in time-to-hire.
- Candidate Matching Accuracy: Percentage of candidates successfully placed based on AI recommendations.
- User Satisfaction: Based on candidate and recruiter feedback.
- Platform Stability: Uptime and performance under heavy load.
- Budget Estimate
- Provide a rough estimate of development costs, broken down into the following categories:
- Discovery & Prototyping
- AI Model Development
- Platform Development (Frontend/Backend)
- Testing & Quality Assurance
- Ongoing Maintenance and AI Model Optimization
- Stakeholders & Roles
- Project Manager: Responsible for overall project execution and timelines.
- AI/ML Engineers: Develop AI algorithms for resume parsing, job matching, and predictive analytics.
- Frontend/Backend Developers: Build the platform’s user interface and integrate the backend with AI functionalities.
- UI/UX Designers: Ensure an intuitive and user-friendly experience for both candidates and recruiters.
- QA Engineers: Conduct platform testing to ensure optimal performance and security.
- Data Privacy Officer: Ensure the platform complies with relevant data protection regulations.
RESULTS & FEEDBACK
What were the measurable outcomes from the project that demonstrate progress or success?
- Application Platform is UP and running and we are handling our primary business through this platform.
Describe their project management. Did they deliver items on time? How did they respond to your needs?
We have been very impressed with their project management. They demonstrated excellent organizational skills and maintained clear, consistent communication throughout the project. Deliverables were consistently provided on time, and they adhered to the agreed-upon schedule.
They were highly responsive to our needs and requests, making adjustments quickly whenever necessary. Their proactive approach to solving potential issues and ensuring our satisfaction was truly commendable. Overall, their ability to manage the project and meet our expectations has been outstanding.
What was your primary form of communication with Neuronimbus?
- In-Person Meeting
- Virtual Meeting
- Email or Messaging App
What did you find most impressive or unique about this company?
What impressed us most about this company was their exceptional blend of technical expertise and outstanding UI/UX capabilities.
Are there any areas for improvement or something Neuronimbus could have done differently?
While our experience with Neuronimbus has been largely positive, there are always opportunities for improvement.
RATINGS
-
Quality
5.0Service & Deliverables
-
Schedule
5.0On time / deadlines
-
Cost
5.0Value / within estimates
-
Willing to Refer
4.5NPS