Accelerating the Prediction of Consumer Behaviour with Encord

Introducing Customer: Neurons 

Neurons has been a leading company in the field of neuroscience marketing for more than 10 years. With their expertise in understanding how humans react to content, they have developed AI-powered solutions to enable companies to create predictable marketing content at scale. Their mission is to eliminate biases and guesswork from marketing strategies.

As a pioneer in this domain, Neurons faced scalability challenges in their data annotation process due to the sheer volume of label categories and the growing number of assets requiring annotation.

We sat down with Dennis Green-Lieber (Director of Product and Engineering) and Konstantina Kaisheva (AI Product Manager) to discuss how Encord alleviated these challenges.

Problem: Challenges with in-house solution

Neurons, historically, have relied on manual annotation by their internal team using their in-house annotation platform. With a growing volume of assets requiring annotation, they found it increasingly challenging to scale their operations using their in-house solutions. They also encountered difficulties in ensuring precision and consistency when labeling large volumes of data, resulting in delays in getting their models to production.

Solution: Scaling with Encord's end-to-end platform

Seeking a solution to their data annotation challenges, Neurons turned to Encord’s platform. They recognized the need for manual and automated labeling capabilities, so they were drawn to Encord’s comprehensive offering. With Encord, Neurons found a user-friendly platform that facilitated a precise and efficient annotation process. They appreciated the ability to define labeling instructions/ontologies tailored to their specific requirements, ensuring accuracy in their data sets.

Encord's platform offered features for quality assurance, workflow management, and data categorization streamlining Neurons’ annotation workflows. They were particularly impressed by Encord's flexibility, which allowed them to adapt labeling criteria for different content types.

Konstantina, AI Product Manager, noted her happiness with the function of model evaluations. Recognizing its potential as an invaluable tool for the future, she highlighted its ability to provide insights into the performance and accuracy of its AI models.

Result: Efficient and fast annotation

With Encord’s platform, Neurons achieved marked improvements in their data annotation process. With the appropriate workflows and quality assurance, they were able to maintain precision and consistency even when they were handling larger volumes of data. This led to building a higher-quality dataset for their AI models. 

Neuron’s newly launched Copilot, provides actionable insights for marketing decision makers. This is run by AI models which require large volumes of precisely annotated datasets, which Encord enabled them to achieve. By eliminating guesswork and biases, they empowered their clients to make informed decisions and optimize their marketing strategies effectively.

Overall, Encord’s platform proved to be a significant asset for Neurons, enabling them to overcome their data annotation challenges. With a newfound ability to make data-driven decisions, the company saw a tangible improvement in its ability to achieve their desired model outcomes.