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Azalea Robotics automates airport baggage handling with intelligent robot operations. The global market for airport baggage handling systems is $20+ billion and growing, presenting a significant opportunity for innovation and market disruption in this sector. Passenger air traffic volume is increasing, driving demand for efficient and reliable baggage handling at airports and putting immense pressure on existing infrastructure. In 2023 alone, airports processed approximately 4.5 billion bags, highlighting the need for advanced solutions to manage this load effectively. Azalea Robotics provides state-of-the-art robotic systems that enhance efficiency, reduce mishandling, and improve passenger experience through more reliable operations. Baggage handling is a critical component of airline ground operations, yet it is fraught with challenges. The work is physically demanding, often leading to long-term injuries among workers. Traditional baggage handling involves repetitive lifting and maneuvering of heavy loads, which can result in long-term health issues. Azalea Robotics addresses these challenges by automating the most strenuous tasks, thereby reducing the risk of injury and enhancing operational efficiency.

Our Mission: We aim to accelerate and automate chemical process development by equipping wet-lab chemists with the power of data-driven optimization and robotic execution of experiments. The Problem: The discovery of novel pharmaceuticals is one of our most important weapons in fighting disease. However, the drug development pipeline is often held up for many months during the design of chemical processes to manufacture these drugs at scale, delaying FDA trials and lengthening the time until drug launch. Designing chemical processes involves the identification of suitable parameters such as catalyst/temperature/solvent. Currently process development is often done via tedious trial-and-error experimentation (slow) or exhaustive screening (expensive and wasteful). Our Approach: In our research, we have developed algorithms for chemical process optimization, which leverage transfer learning and Bayesian optimization. We validated the algorithms in the wet lab, showing an up to 95% reduction in required experiments and cost compared to exhaustive screening. We have made our approaches accessible to chemists through our user-friendly no-code software platform and to automated laboratory equipment with our API.