Unidatalab

Unidatalab was hired by SuccessKPI to develop a Workforce Management (WFM) optimization solution. The goal was to predict the workload of customer support agents and intelligently schedule them for improved optimization. Unidatalab's team used Python and PuLP to develop the solution, which was successfully integrated by the client. The team offered multiple solutions, conducted tests, and helped with customization. The client found Unidatalab through an online search and chose them due to their high ratings, budget-friendly pricing, and high level of technical and business knowledge.

In a project for AiFirst, Unidatalab was tasked with developing a robust benchmarking and monitoring system for the company's LL.Ms to ensure accurate, fair, and secure performance. The project was divided into three sprints, including establishing the project environment, designing the benchmarking framework, finalizing the benchmarking pipeline, and implementing various detectors. The client found Unidatalab through a referral and chose them due to their high ratings and budget-friendly pricing.

Unidatalab was engaged by robot_dreams, an educational platform, to develop a robust benchmarking and monitoring system for their Large Language Models (LLMs). The project was divided into three sprints which included establishing the project environment, finalizing the benchmarking pipeline, system testing, optimization, bug fixing, and developing a report generation component. The client found Unidatalab through a referral and chose them due to their established record of projects and budget-friendly pricing. All projects were successfully completed, meeting the clients' objectives and expectations.