When using chatbots or language models to analyze your own information, the biggest challenge isn't technical. It's a very simple one: how do you get artificial intelligence to understand your data without exposing it? One of the strategies we use at Cloud Levante to achieve this is embeddings.
An embedding is a way of converting information — text, images, or audio — into numbers that represent its meaning. That way, the AI doesn't work with words or sentences, but with relationships between concepts.
Imagine you have a confidential document. Instead of handing it over as-is, you do this:
That's an embedding: a translation of the text's meaning into numbers. The AI never sees the original content, only a mathematical version that lets it know:
It's like telling someone "this is about billing" without showing them the invoice.
Thanks to embeddings, systems can:
This makes models more efficient and more controllable.
Let's be clear: embeddings alone do not guarantee anonymity or absolute security. That's why, at Cloud Levante, embeddings are never used in isolation — they're one more layer within a broader strategy for security, confidentiality, and data privacy.
Embeddings do not replace a complete security strategy. But combined with other confidentiality, privacy, and data-control measures, they make it possible to analyze information with language models in a much safer way.
At Cloud Levante, they are just one of the many layers we use to protect data when designing and training systems that work with real, sensitive information.
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