Geospatial data is fueling innovation in many sectors. Read on if your business needs to scale its data labeling operations while maintaining quality.

Are you considering outsourcing data labeling work while developing medical AI or computer vision solutions? Read these data security tips.

Continuous improvement methods yield high accuracy, high speed, and high value. Here's how CloudFactory practices CI for clients in the finance industry.

Learn how CloudFactory practices quality control for our clients in the fintech and finserv spaces, and how quality control differs from quality assurance.

Doctor shortages are impacting healthcare innovators’ ability to label data for computer vision solutions. Outsourcing the work will help.

Computer vision improves patient care, streamlines medical decisions, and lowers costs. Ask these questions before outsourcing healthcare data annotation.

Outsourcing your fintech data processing work doesn't mean you have to sacrifice quality, i.e., as long as your partner uses sound QA workflow models.

These four use cases examine why using drones to collect data makes drone inspections safer, more accurate, and more efficient than manual inspections.

Recap of our chat with V7 about medical AI data annotation while creating a dataset of COVID-19 X-rays for clinical use and diagnostic AI development.

Learn about the levels of drone autonomy and how advancements in AI are transforming drone operations across various industries.

Just like bamboo, we continue to grow; we are very excited to add 14 more people to our team that connects one million talented people to meaningful work.

Drone autonomy fueled with high-quality data will be the key to scaling drone operations. Read more valuable insights from the drone team at CloudFactory.

The real-time use cases for geospatial data are practically limitless, but data-labeling on such an enormous scale remains a major challenge.

Automated solutions like OCR can help tackle retail data transcription at scale, but maintaining data quality remains a major challenge.

Retail data transcription is time-consuming and tedious work, not to mention notoriously hard to scale. Here’s how retailers can overcome those challenges.

The nuances of language can be difficult for a machine to understand, hence the need for human input to accelerate testing and ensure quality control.