Semantic AI doesn’t create, it classifies. It uses the same neural network and tensor-based technology as generative AI to deliver precise threat classification, with no hallucinations and no synthetic outputs.

Why would your customers’ CFO reference a payment link to a client they’ve never dealt with? Instead of looking for keywords or behavioural signatures, Semantic AI picks up on the inconsistencies in logic or context that rule-based engines can miss.

This isn’t a generic trillion-parameter language model trained on internet noise. Libraesva’s engineers have created an agile 100M-parameter model trained on thousands of curated real-world threat examples. With latency of less than a second, Semantic AI runs on standard CPU hardware, which means no delays, no GPUs and no need to offload to the cloud.

During internal testing, Semantic AI detected fake ADSL subscription scams on Day 0, before any signature updates or pattern propagation. It proactively interprets anomalies in communication, catching threats early and decisively.

The Libraesva Semantic AI engine adds a deeper layer of scrutiny to your customers’ existing protection without adding complexity.

Unlike generative models, which introduce random variability into their results, Semantic AI is discriminative. The same email will always yield the same output for the consistent, reliable classification that’s essential for security reliability.

Libraesva’s agile approach ensures rapid responsiveness to emerging threats without burdening your customers’ system with full retraining cycles.

Semantic AI’s language model is initially trained on the semantics of 100 languages for broad foundational semantic understanding. The base model is then fine-tuned for email classification using curated threat examples – this takes just a few hours, and updated models can be deployed within minutes.