PraetorUQ is our cutting-edge Uncertainty Quantification (UQ) framework purpose-built for LLMs. It integrates a suite of state-of-the-art techniques alongside proprietary algorithms to deliver robust, reliable performance in high-stakes domains such as finance where accuracy and trustworthiness are non-negotiable.
As AI-generated content becomes more convincing, yet potentially deceiving, and misinformation spreads rapidly, the need for reliable, real-time fact-checking has never been greater.
Modern LLMs generate fluent answers, but offer no indication of how confident they are. Users can’t tell when to trust the model—and when to double-check.
Current fact-checking workflows—manual or model-assisted—are too slow and reactive. LLMs may surface incorrect claims faster than humans can verify them.
PraetorUQ evaluates and quantifies the reliability of LLM outputs, empowering users to make informed decisions with confidence. Key features include:
Hallucinations appear unpredictably, risking user trust and credibility.
Labor-intensive, costly, and difficult to scale for real-world applications.
- To pinpoint specific spans likely to contain hallucinations
- To deliver efficient, scalable, and trustworthy content validation
See the difference our fact-checking service makes by comparing original content with fact-checked versions.
Warning: This content contains potential inaccuracies and has not been verified for factual correctness.
Validation: This content is fact-checked by PraetorUQ with over 90% confidence.
See how our tailored solutions can work for your team!