A global leader in data annotation, partnered with Zemoso to co-create a talent management system that automates, unifies, and streamlines talent allocation, and scales as the organization evolves.
Client’s resource allocation process relied on manually identifying available resources among a pool of 5,000 professionals, matching skills to assess suitability, and allocating them to projects.
The data annotation company decided to mitigate operational inefficiencies by partnering up with Zemoso to build a machine learning (ML) powered Talent Management System (TMS).
Zemoso built the TMS from ground-up and enabled Solution Architects with the following:
Zemoso pods opted for a strategic engineering approach for deploying the TMS and resolved the following challenges.
Zemoso’s discovery pod collaborated with the stakeholders for a week of GV-inspired design sprint, nailed the golden user path, and created a high-fidelity, clickable prototype in a two-weeks sprint.
We opted for the Domain-Driven Design (DDD) approach to identify and prioritize essential entities, services, and Non-Functional Requirements (NFRs). We crafted a hybrid architecture — microservices + event-driven architecture to better meet client’s needs
Microservices architecture: For management of RESTful web service.Event-driven architecture: For async capturing and processing of events occurring in different systems.
We created user story maps to understand user needs and requirements, and used the t-shirt sizing approach to estimate the effort, define the scope, and prioritized features accordingly.
Zemoso built an ML algorithm to ensure a systematic and structured process for resource allocation. The algorithm maximized workforce efficiency through:
Throughout the partnership, we implemented CI/CD pipelines to ensure shorter feedback loops and to steadily make progress on product development milestones. Our agile methodologies, and continuous feedback loops allowed us to iterate, streamline development, and refine the product.
Zemoso design and engineering pods built the TMS using a blend of modern tech stack, architectural strategies, and iterative development methodologies. The client decided to test-drive the Talent Management System and assess effectiveness internally.
©2023 Zemoso Technologies. All rights reserved.