Delivering personalized ad experiences requires accurately identifying content within high volumes of unstructured image data. The team needed a way to scale AI training data across multiple projects from classifying products in complex visual environments to evaluating whether web pages were appropriate for younger audiences.
They implemented Databrewery Annotate to streamline internal collaboration and accelerate image annotation workflows. The platform reduced communication overhead between teams and allowed them to convert raw visual data into structured, model-ready inputs faster and more reliably.
The team saw a 40% increase in annotation delivery speed and a significant boost in label quality. With faster turnaround and higher consistency, they were able to scale contextual ad products more efficiently improving personalization and content safety at once.
One of the world’s top ad platforms is using AI to reimagine how personalized ads are delivered across the web. Their mission is to help marketers and media owners reach consumers through more relevant, trustworthy, and brand-safe ad experiences. By applying machine learning to product recommendations and contextual targeting, they’re developing systems that can understand the content of a webpage and tailor ad placements accordingly all while ensuring the safety and integrity of the brand.
Before adopting Databrewery, the team managing their Publisher Content Analysis initiative faced familiar challenges. Managing unstructured image data through spreadsheets, tracking annotation quality over email, and identifying model edge cases without a clear feedback loop made it difficult to scale. They needed a way to improve collaboration between ML and product teams while applying more modern, data-centric development workflows.
The team turned to Databrewery to support key initiatives like product background removal and web page classification for example, determining whether a page is appropriate for users under a certain age. With Databrewery’s dedicated platform, they were able to build a training data pipeline that allowed product owners, data scientists, and reviewers to work together more efficiently. Communication overhead dropped significantly, and model iteration cycles became faster and more precise.
For projects like background removal, where small pixel-level differences could affect model performance, Databrewery enabled expert human review directly within the annotation flow. Unlike open-source tools or in-house solutions, the platform’s reliability meant that global teams could access it consistently and securely regardless of location.
The impact was immediate. Annotation delivery speed increased by 40%, and annotation quality improved in parallel. With a stable, collaborative infrastructure in place, the team is now exploring expanded use cases including fake news detection, video-based contextual signals, and attention tracking to better understand user engagement.