Automatic identification and resolution of entities within unstructured data sources is crucial to understanding and utilizing data for use in AI systems. Historically this has been difficult to do, and even harder to trust the results. Agolo’s hybrid, human-in-the-loop approach for discovering and compiling entity intelligence, ensures that its best-of-breed, entity graph technology delivers trustworthy, production grade outputs for mission-critical AI use cases.
Beating the current SOTA Knowledge Graph-to-Text (KG-to-Text) model on the WebNLG (Constrained) dataset with a fine-tuned Llama 2 7B Chat model.
Taking the first step toward a new brand
GenAI draws from your enterprise’s knowledge base, documentation, and training data to deliver responses. If the content is outdated, incomplete, or inconsistent, the AI may produce irrelevant or inaccurate outputs, eroding customer trust.
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