Modernizing a Talent Relationship Management Platform for Scalability, Performance, and Smarter SearchStaffing & Recruitment
However, the legacy platform faced critical issues related to performance, scalability, and search accuracy, impacting user experience and operational efficiency. To overcome these challenges, we partnered with the client to rebuild their core application and integrate AI-driven search capabilities with a modernized UI/UX.
challenge
Performance & Scalability Issues
1. The existing application used separate databases for each tenant, making deployments complex and error-prone.
2. Database schema updates required execution across all tenant databases, often causing deployment failures and extended downtimes.
3. Over time, unused features added to the platform caused performance degradation
Inefficient Search & Data Discovery
1. Recruiters faced difficulties in finding people, companies, or job postings due to ineffective keyword-based search.
2. Incomplete or irrelevant results caused missed opportunities and inefficiencies in talent sourcing.
Poor UI/UX and Complex Filtering
1. The interface lacked intuitive design, and the filtering process was tedious, requiring users to navigate unfamiliar parameters.
2. Overall recruitment workflows were slowed down, impacting productivity.
Lack of Streamlined Process
Recruiters encounter limitations in TRM platforms, necessitating enhanced functionality for improved recruitment efficiency.
solution
We adopted a two-fold strategy
(A) Rebuilding the application for scalability and maintainability
(B) Enhancing search capabilities with AI and NLP
A. Modernized Application Architecture
Unified Database with Row-Level Security (RLS)
Transitioned from multiple tenant-specific databases to a single shared database, ensuring data isolation through RLS and simplifying schema updates.
Technology Stack Enhancements
1. Backend: Ruby on Rails with dry-transaction, dry-validation, and Roar for maintainability and clarity.
2. Frontend: ReactJS with Material UI (MUI), Redux/Redux Toolkit for state management, and React Router for client-side routing.
Containerized Development with Docker
Enabled rapid environment setup, reducing onboarding time to just a few hours.
Stable, Long-Term Team Commitment
Given the project’s complexity, we ensured consistency by retaining the same project members from the beginning (nearly 4 years). This deep continuity enabled the team to fully understand the system, maintain momentum, and deliver effectively without disruptions from resource rotation
B. AI-Powered Search & UX Enhancements
NLP-Enabled Search
Integrated Natural Language Processing for conversational, human-like search queries.
Machine Learning Models
Implemented ML-based intent analysis and entity recognition for accurate and relevant results.
Revamped UI/UX
Designed an intuitive interface that made filtering simple and conversational.
Outcomes
Faster Deployments
Up to 40% faster deployments due to a unified database structure and simplified schema updates.
Enhanced Search Relevance
Achieved a 30% increase in search accuracy, leading to more complete and relevant results for users.
Efficiency Gains
Realized a 25% reduction in recruiter effort when refining searches and managing filters.
Quicker Onboarding
Enabled 90% faster developer onboarding, reducing setup time from days to just a few hours with Docker.
Improved User Satisfaction
Observed a 20% increase in user satisfaction, thanks to a modern UI and intuitive workflows.
Let’s work together
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