DATA SOURCING
DATA SOURCING
Start with the right data
AI is only as good as the data it’s trained with. We assess the coverage and balance of your dataset to assure that it represents the operating conditions under which the AI will be tested and then collect, curate, and if necessary, augment with synthetic data.
DOMAIN COVERAGE
The data accurately and completely covers the task domain that the AI will be applied to.
USER COVERAGE
All users are equally represented to avoid biases according to gender, age, race, politics, religion, etc.
BALANCE
All areas of the domain and all users are equally represented data so the AI algorithm works as expected in all aspects of the application domain.
Data collection
Our team selects and collects data that best aligns with your use case, ensuring relevance while reducing bias. They evaluate whether the data suits the task your AI is meant to perform, identify what will best train the model, and go to great lengths to source the exact data you need.
Data curation
Once the data is collected, we assess the set to check which data is valid, relevant, and helpful to train the model. With the support of our suite of customized data curation tools, we cleanse, filter and format the data, removing it of outliers, distilling out any subsets that you need, and preparing it to be applied to the model.
Data augmentation with synthetic data
Missing values can create biased data and poor AI performance. Especially for edge cases, it can be hard to source a complete and balanced dataset. We generate synthetic data for text, speech and images to augment your existing dataset, improving coverage and balance by creating exactly the data you need.
What is synthetic data?
Real-world data can be expensive and time-consuming to obtain. But when you’re trying to capture something in your dataset that happens infrequently or randomly like piloting a plane in a hailstorm — it might be difficult or even impossible to cover all of your cases.
Synthetic data uses a variety of technologies including Generative Adversarial Networks (GANs), Diffusion Models, and Neural Radiance Fields to artificially produce new data you need according to exact specifications. Starting with the automotive field, synthetic data is gaining traction in many AI applications. Gartner predicts 60% of all data used to train AI applications will be generated synthetically by 2024.
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Let’s work together to build smarter AI
Whether you need help sourcing and annotating training data at scale, or you need a full-fledged annotation strategy to serve your AI training needs, we can help. Get in touch for more information or to set up your proof-of-concept.