Wunderman Thompson

Founded in 2018, Wunderman Thompson is a global marketing communications agency based in New York. It was formed when their parent company WPP plc merged holding company J. Walter Thompson with digital agency Wunderman. WPP Open Brand Guardian, created by Wunderman Thompson, is an AI-driven platform that gives marketing, creative, and production teams total content quality assurance at scale.
Part of WPP plc
July 2021 - June 2022
200 offices
in 90 markets
20,000 employees
after the merger
United States

Project Details

Brand Guardian complements any corporate asset assurance system with an AI-driven custom rules engine that reduces the time necessary for manual asset checking.  Wunderman Thompson’s tool promises:

  • Brand governance introducing consistency across your digital product
  • Inclusion through diversity and accessibility assurance
  • Compliance with legal, industry, and market requirements
  • Creative excellence by applying best practices to boost your content performance

In short, Brand Guardian reviews creative assets—images, video, and text—and provides valuable and immediate insights. It can be plugged into the existing production process and the current toolset.

STX Next Delivered

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Wunderman Thompson first approached STX Next with two main areas of collaboration where we could support them:

  1. Supporting the design of a machine learning (ML) system that can adapt to various customer requirements in the context of their individual policies, compliance regulators, accessibility assurance of advertising assets, packaging, etc.
  2. Assisting and guiding Wunderman Thompson through the process of implementing a thorough methodology leading them to become an AI-driven company
The collaboration was based on complete openness and trust in our people and their skills. As a result, we were able to jointly develop a number of new approaches and significantly improve the implementation process of the models and the architecture of the overall solution.
Krzysztof Sopyła
Head of Machine Learning and Data Engineering
STX Next


The main task for us was to introduce a robust ML solution that will help assess content quality at scale and accelerate the process of onboarding new clients. We identified the main bottlenecks: the data gathering process, ease of introducing new evaluation rules, and speed of asset processing.

Our work focused on redesigning the architecture, allowing existing models and algorithms to be reused and run on a large scale. Part of the solution was introducing ML-focused microservices with better-defined responsibilities. By doing so, we were able to more easily auto-scale with an independent group of computing endpoints in Kubernetes.

We addressed the challenge of detecting undefined, custom image parts (such as logos, fonts, and particular objects). In addition, we created a framework for generating new training datasets (using data augmentation and synthetic data). The framework accelerates training and provides agile and robust solutions for client requirements. New, artificially created data solves the problem of detection.

We also suggested a messaging system for running most machine learning and computer vision tasks by multiple Celery workers in parallel. It allows computing each “rule” as a separate task and processing the entire asset analysis pipeline, significantly improving performance.

Jupyter Notebook

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