Assembled, the AI-driven customer support orchestration platform, has announced the general availability of its AI-powered Schedule Generation tool.
The new engine is designed to automate one of the most labor-intensive and error-prone aspects of workforce management (WFM): agent scheduling.
By leveraging intelligent automation, the tool allows support operations to create optimized schedules with a single click. The engine balances complex variables, including demand forecasts, regional labor laws, and the nuances of managing distributed, multi-channel teams.
According to Assembled, the tool can eliminate up to 95% of the manual effort associated with weekly scheduling, allowing CX leaders to pivot toward strategic planning.
Solving the Hybrid Workforce Puzzle
The launch comes at a time when CX managers are increasingly facing the challange of a “blended” workforce—balancing in-house staff, Business Process Outsourcing (BPO) partners, and the growing presence of AI agents.
Ryan Wang, co-founder and CEO of Assembled, commented:
“The rise of AI has made scheduling and managing support agents harder than ever. Now teams are responsible for scheduling in-house teams, coordinating BPO partners, and balancing the impact of AI agents. Most support teams are still trapped in spreadsheets, manually juggling schedules while trying to remember which country requires breaks every 3.5 hours versus 4 hours. Optimal schedule generation is an age-old problem in operations research, but AI advances allow us to handle more complexity in less time. The result: teams get time back to focus on higher leverage planning and strategic work.”
Advanced Architecture for Modern Support
Unlike legacy platforms that often suffer from system lockouts during heavy processing, Assembled’s modern architecture allows for concurrent schedule processing. The tool was developed by a dedicated team of AI researchers to ensure it can handle large-scale, multi-site operations without sacrificing quality.
Key capabilities of the new Schedule Generation tool include:
- SLA Optimization: The engine continuously aligns agent skills and business rules with demand forecasts to ensure Service Level Agreements are met.
- Automated Compliance: It automatically places shifts and breaks according to location-specific labor rules and flags potential violations.
- Infinite-scale architecture: The architecture supports large teams across various channels and shift patterns.
- Dual-purpose scheduling: Beyond generating new schedules, the system monitors existing ones for real-time compliance and SLA risks.
- Advanced working hours: The tool handles cross-midnight shifts and rotational patterns for up to 13 weeks.
Real-World Impact: From Weeks to Minutes
Early adopters, including ServiceTitan, Backcountry, and Pair Eyewear, report significant efficiency gains. Many have transitioned away from cumbersome Google Sheets workflows that previously took weeks to manage.
Jeff Rybicki, CX operations manager at Pair Eyewear, said:
“Schedule generation turns what used to be an hour-plus task every Friday into just five minutes with only a few clicks. It’s done a fantastic job of streamlining everything and it’s easily my favorite Assembled feature.”
Karekin Sarajian, workforce management analyst at ServiceTitan, added:
“Other tools I’ve used would give you a file that was 30 tabs long. You had to do everything in Excel so they could upload it. And you still had to go back and fix it. If you can’t be as specific as you need to be, then you don’t get the effectiveness of the schedules and there ends up being a lot more manual work required.”
The Schedule Generation tool integrates directly with Assembled’s existing forecasting suite, using predictive data to optimize placement and ensure operational excellence.
Founded in 2018 by former Stripe engineers, Assembled continues to expand its footprint in the CX space, serving major brands like Canva, Robinhood, and Intercom. The company recently raised $71 million in funding to further its mission of unifying human and AI workforce management.