Illuminate and Improve Your AI
BluelightAI reveals hidden data patterns and refines models for peak performance.
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Meet Cobalt
BluelightAI Cobalt enables developers to understand and optimize their AI/ML models.
With a simple Python API and a powerful visual explorer, Cobalt illuminates training data, detects errors, and identifies key groups to fine-tune for superior performance.
How it works
With just a few lines of code, Cobalt ingests your raw data, organizes it, and empowers you to easily refine the inputs.
With advanced pattern detection, you’ll visualize unstructured data like never before, and discover new insights into your model.
The result? Cobalt gives you more control over your AI models and data, leading to higher performance and fewer headaches.
Key use cases
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Cobalt applies topological data analysis (TDA) to cluster datasets into meaningful substructures. This enables teams to select and refine the most relevant subsets, ensuring the highest quality models.
By setting inclusion criteria for relevant data, teams can efficiently filter and organize large datasets for targeted domain adaptation, separating signal from noise— maximizing fine-tuning impact.
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Cobalt leverages topological data analysis to illuminate previously hidden internal model structures.
Cobalt highlights clusters that produce errors or lack sufficient data, empowering teams to diagnose issues and fine-tune models. The result? Productivity gains and improved model accuracy.
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Cobalt pinpoints clusters of data your model hasn't seen before— unfamiliar hotspots which signal hallucinations or errors.
Armed with these insights, your team can swiftly update models, ensuring accuracy and alignment with new data trends. Cobalt transforms AI blind spots into opportunities for enhanced performance and reliability.
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Deploy to production confidently, knowing that Cobalt gives you greater visibility and control over your models.
Enjoy peace of mind, as Cobalt provides the tools you need to build robust models before deployment, and address issues after deployment.
