Wunderman
Thompson

Partnership with STX Next

July 2021 - June 2022

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.

INDUSTRY
Marketing and Advertising
COUNTRY
United States
Part of WPP plc
Wunderman Thompson: AI Tool for QA Testing Brand Assets – Case Study-img
200 offices
in 90 markets
20,000 employees
after the merger

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.

Wunderman Thompson: AI Tool for QA Testing Brand Assets – Case Study
STX Next Delivered
Team Extension
Team Extension
Machine Learning
Machine Learning
Software Engineering
Software Engineering
Cloud Deployment
Cloud Deployment
Architecture Design
Architecture Design
Expert Consulting
Expert Consulting
Microservices
Microservices
Computer Vision
Computer Vision
Deep Learning
Deep Learning
Cloud Services
Cloud Services
Krzysztof Sopyła
Head of Machine Learning and Data Engineering @ STX Next

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.

Challenge

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

Solution

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.

Python
Python
FastAPI
FastAPI
PyTorch
PyTorch
OpenCV
OpenCV
scikit-image
scikit-image
TensorFlow
TensorFlow
Jupyter Notebook
Jupyter Notebook
Detectron2
Detectron2
NumPy
NumPy
pandas
pandas
Celery
Celery
Docker
Docker
Kubernetes
Kubernetes
Pillow
Pillow
Azure Machine Learning Studio
Azure Machine Learning Studio

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