AI Data Collection Case Study

For many companies, their model is one of their highest costs. This means it’s crucial to train them for optimal performance; ROI is vital. Training models can require high volumes of AI data collection, often delivered in tight timelines to reduce development costs. Another AI data services challenge is obtaining high-quality data. While synthetic AI data solutions may be easier, faster, and cheaper to procure, they’re also more likely to result in poorer model performance.

These are a few problems with synthetic model and LLM training data:

• Inaccuracy: May not represent real-world data correctly, leading to biases and inaccuracies from the model

• Generalization: Might not train models to generalize well with real-world scenarios, missing the complexity of real-world data.

• Bias/fairness: Could encourage models to perpetuate harmful and unfair biases.

• Regulatory/ethical: May not meet regulatory or ethical standards and may be derived from sensitive information.

• Difficult to interpret: Is often difficult to interpret, especially its origin process. Thus may be harder for end users to trust.

• Limited usage: Inapplicable to many real-world scenarios, so not as useful for training high-performing models.

One of Lionbridge AI’s customers, a platform that connects creative talent with brands, needed 20,000 high quality data points to train its model in less than a week. Read our case study to learn how we used our Aurora AI Studio™ platform to collect and deliver the customer’s 20,000 data points — then, because they were so pleased with the results, an additional 8,000 data points.

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Want to learn more about Lionbridge’s AI data collection services? Ready to explore the best AI data solutions? Let’s set up a meeting to discuss how you can maximize ROI from your model.