APOLLO Scheduler uses an AI engine based on Integer Linear Programming (ILP) to create optimal agent schedules across demand, skills, contracts, preferences, and service level targets. It is built for the complexity planners deal with every day.
APOLLO Scheduler is developed through a long-term cooperation between CALLOSSEUM (Serbia) and COMPUTD (The Netherlands). Together, they combine hands-on call center operations experience with advanced AI engineering to deliver scheduling that actually works in practice.
APOLLO Scheduler helps BPO call centers reduce manual planning effort, improve service levels, and scale operations without adding scheduling complexity.
BPO call center service provider
CALLOSSEUM is a BPO call center service provider based in Serbia with extensive experience operating multi-client contact centers. As an active user of workforce scheduling software, CALLOSSEUM brings real-world planning practice into the development of APOLLO Scheduler.
Their contribution is grounded in daily operational reality: managing fluctuating demand, multi-skill teams, labor rules, agent preferences, and service level commitments. CALLOSSEUM ensures that APOLLO Scheduler reflects how scheduling is actually done in production environments, not how it looks on paper.
AI product development company
COMPUTD is an AI product development company based in the Netherlands, specializing in translating complex operational problems into scalable AI solutions. COMPUTD designed and built the APOLLO Scheduler, including the AI engine based on Integer Linear Programming (ILP).
Their role is to transform operational constraints and objectives into robust optimization models, product architecture, and enterprise-ready software. COMPUTD ensures that APOLLO Scheduler is not only operationally sound, but also scalable, maintainable, and suitable for third-party BPO environments.
Call center scheduling is one of the hardest operational problems in the BPO world. Forecast accuracy fluctuates, agent skills differ, preferences matter, contracts impose constraints, and service levels must still be met. Most scheduling tools simplify this reality. As a result, planners spend hours correcting schedules, agents feel ignored, and costs rise.
APOLLO Scheduler is built to address this problem from the ground up. Not as a theoretical AI model, but as a practical system shaped by real call center operations.
At the core of APOLLO Scheduler is an AI engine based on Integer Linear Programming (ILP). This approach allows the scheduler to balance competing objectives and constraints at the same time, instead of optimizing one metric at the expense of others.
APOLLO Scheduler optimizes schedules using inputs such as:
The result is a schedule that is mathematically optimal and operationally usable.
APOLLO Scheduler is the result of a long-term cooperation between CALLOSSEUM in Serbia and COMPUTD in the Netherlands.
CALLOSSEUM brings deep, hands-on experience from operating and managing call centers. This includes day-to-day workforce planning, agent management, and dealing with the realities that never show up in spreadsheets.
COMPUTD brings advanced AI and product engineering expertise. This includes designing scalable AI architectures and translating complex operational problems into robust optimization models.
This cooperation has existed for years. It is not a one-off integration or a reseller agreement. The product has been shaped iteratively by operational feedback and technical refinement.
APOLLO Scheduler is jointly brought to market by CALLOSSEUM and COMPUTD for third-party BPO call centers. It is designed to fit multi-client environments, varying labor rules, and diverse operational models.
For BPOs, this means:
APOLLO Scheduler does not replace planners. It gives them a system that works with their reality instead of against it.
APOLLO Scheduler exists because operational knowledge and AI engineering were developed together, not in isolation. The product stands on its own, backed by a cooperation that ensures it remains grounded, practical, and credible.