System Summary

Problem Desciption

The global laboratory automation market was estimated at $7.15 billion in 2025 and is projected to reach $12.25 billion by 2033, due to advancements in AI and robotics are a major factor in the high estimates. It also reflects the growing recognition that laboratories face critical efficiency challenges. Research scientists and laboratory technicians spend a substantial portion of their time on routine manual tasks rather than analytical work. Survey data reveal that each scientist reportedly loses an estimated 12.5 weeks per year (around 3 months) of discovery time to non-core tasks. This inefficiency is particularly acute in high-throughput screening facilities, where drug discovery campaigns may require processing tens of thousands of samples across multiple assay plates.

The challenge extends beyond time allocation. Manual laboratory procedures introduce significant variability and error. Pre-analytical errors were estimated to make up 70% of all mistakes made in laboratory diagnostics, and errors cost an average U.S. lab about $180,000 each year in the pre- and post-analytical stages. The cumulative effect of these factors (wasted researcher time, experimental inconsistency, labor costs, and opportunity costs from delayed discoveries) creates a substantial drag on scientific productivity and innovation. These challenges are compounded during extended experimental protocols, where precise timing and continuous monitoring are critical, forcing researchers to coordinate schedules around incubation periods and often work irregular hours to maintain experimental continuity.

To address these challenges, APEX-Lab (Automated Precision EXperimentation Laboratory) emerges as a transformative solution for modern laboratory environments. APEX-Lab is an intelligent system that autonomously manages experiments using a robotic arm to handle diverse labware. Operating within existing laboratory infrastructure, it transports samples between stations such as liquid handlers and shaker modules with precision, executing pre-programmed workflows without human intervention.

APEX-Lab incorporates integrated computer vision for real-time anomaly detection, identifying issues like improperly seated plates, spills, or equipment malfunctions, and can autonomously pause operations when problems arise. The system continuously records experimental parameters and captures high-resolution video for validation. By automatically collecting experimental data alongside visual observations such as color changes, condensation patterns, and sample characteristics, APEX-Lab creates comprehensive, traceable experimental records that eliminate manual transcription errors and make it easier for researchers to analyze results, troubleshoot issues, and ensure reproducibility.

Use Case