A Game-Changing Approach
To Natural Language Processing

Meaning-Based Algorithm Delivers High Accuracy

Semantic fingerprints leverage 16k parameters to encapsulate the different meanings of words, sentences or paragraphs. This enables to disambiguate text at a fine-grained level and to match phrases that have similar meanings but different phrasing. As a result, companies get fewer false positives and require less manual intervention to review and correct results.

Semantic Folding Enables Highly Efficient Natural Language Processing

Language models based on semantic fingerprints need orders of magnitude less annotated examples than Transformer models to reach comparable levels of accuracy (a few hundreds versus several thousands). This means that companies get actionable results much quicker, and with less human resources involved.

Semantic fingerprints are sparse distributed vectors that require much less computing power than LLMs to process large amounts of text. Applications leveraging Semantic Folding do not only save IT costs, they also contribute to lower the carbon footprint of IT operations.

Inspect Results At The Press Of A Button

With Semantic Folding, it is easy to debug and fine-tune language models because each semantic feature can be inspected at the document level. This aspect also guarantees the full transparency and explainability of results, enabling enterprises to eliminate biases in their models and to meet compliance requirements (e.g. GDPR).

Natural Language Processing That Scales

Semantic Folding empowers business users to customize and train their models with comparatively little example documents. As a result, companies can implement a NLP project where only little training data exist, and easily scale it to other use cases and departments within the enterprise without the need for dedicated, internal AI expertise.

Semantic Folding can be applied to any language and enables direct cross-language NLP, so that translation efforts become obsolete.