Across a company's global operations, its team gathers large volumes of unstructured data during prototyping, testing, and manufacturing. While this data has significant potential for AI model development, its proprietary nature demanded a platform with robust security, full transparency, and strict privacy controls during the labeling process.
After evaluating multiple data annotation platforms, the team selected Databrewery for its intuitive interface, seamless integration with existing infrastructure, and strong data governance capabilities. The platform allowed them to flexibly manage both internal and external labeling teams, adapt workflows quickly, and accelerate model iteration cycles.
The company began using Databrewery for computer vision applications focused on product defect detection. Over three years, the platform evolved into a core part of their AI operations. Today, Databrewery supports AI training data across all business units globally, serving as a centralized, enterprise-wide solution.
A global leader in consumer goods, known for its extensive portfolio of household and personal care brands, manages complex product development, testing, and manufacturing processes. These processes generate large volumes of unstructured data including images, video, and text, which can be a valuable asset when properly refined, tagged, and classified for machine learning.
As the team continued to expand their use of AI, one of their most pressing challenges became finding a scalable, secure, and efficient way to turn this raw data into high-quality training data. Data privacy was critical. Much of the data gathered during product prototyping or manufacturing is considered proprietary intellectual property, and any data sourced from consumer interactions demands strict compliance with privacy regulations. Transparency and traceability in the labeling process were essential, not only for internal governance but also to build confidence in how data was being transformed and used.
After a thorough evaluation of available solutions, the team selected Databrewery as their end-to-end training data platform. Their decision was based on several key criteria, ease of use, seamless integration with existing infrastructure, support for both internal and external labelers, and robust data governance capabilities. Unlike many tools that operate as black boxes, Databrewery offered complete transparency into the data annotation pipeline, allowing teams to retain full ownership and visibility over their datasets at all times.
The platform made it simple to assign roles and responsibilities, track annotation progress, perform quality checks, and make iterative improvements. Whether data was being labeled by in-house experts or trusted external vendors, the entire process was managed centrally within the platform, ensuring that standards were met and adjustments could be made quickly as project needs evolved.
This capability dramatically improved their ability to iterate on model training, reduce development cycles, and deliver AI-powered features to market faster. Over time, the use of Databrewery expanded well beyond the R&D team. The company has now standardized it across all business units, turning what began as a targeted solution into a core enterprise platform for producing reliable, scalable AI training data globally.