By tailoring AI models to their intended use cases, companies can significantly reduce operational expenses while also improving performance and customer experience.
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As compute costs rise, the question is no longer whether AI solutions are transformative, but rather how do we make them sustainable?
2025 will see AI Product Support and AI Knowledge Management prioritize expertise and depth over breadth to drive customer loyalty, improve accuracy and performance, and reduce the high costs of AI.
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 If the content is outdated, incomplete, or inconsistent, the AI may produce irrelevant or inaccurate outputs, eroding customer trust.
Retrieval Augmented Generation (RAG) is a popular, low-cost technique to boost GenAI response quality. But for many use cases, it still falls short.
What are entity extraction, entity disambiguation, and entity linking?
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.
How do you bring insights to unstructured data? An overview of how entity graphs build upon knowledge graphs to extract insights and improve information value.
