Data labeling for every industry

Our ability to support very complex computer vision use cases has led to our involvement in projects involving autonomous drones in national security contexts, drone-based assessment of storm damage, agricultural soil preparation and harvesting decision-making, and a host of others.

Our platform can process text in form of documents, transcripts, and handwritten text as well as audio recordings, and return structured and annotated data for credit analysis, audits, and fraud detection. Our CV capabilities have been instrumental in the development of autonomous damage assessment and claims adjustment systems.

From robotics automation to preventative maintenance and quality control, our labeling platform has supplied high quality data to accelerate ML applications across various manufacturing use cases with a high degree of complexity.

Through our annotator training curriculum, quality control, and process transparency, healthcare agencies rely on Alegion to quality data to train ML models for medical diagnosis, anomaly detection, and patient data management without depending on domain experts for all the data labeling.

Training autonomous vehicles requires huge volumes of extremely accurate labeled and annotated data. Our pixel-level segmentation accelerates detailed annotations for these applications, and our platform is purpose-built for enormous scale.

Agriculture is making strategic investments in ML-driven systems that monitor and manage everything related to planting, growing and harvesting. We’ve played a role in everything from identifying rocks in aerial images of fields to determining when berries are ready for picking.

Energy is an infrastructure-intensive sector where the ability to match supply and demand is critical. Organizations wanting to leverage ML’s predictive capabilities have relied on Alegion for systems as diverse as smokestack discharge analysis and preventive maintenance.