AI's impact extends far beyond tech. When DeepSeek’s R1 launched in Q1 2025, it shook global markets—Nvidia lost $600 billion in a day, and investors questioned the future of AI giants. But the ripples didn’t stop there. From breakthroughs to ethical dilemmas, AI is reshaping industries at an unprecedented pace.
The accuracy of LLM responses is crucial when implementing AI solutions for your business. GraphRAG solutions have proven to be effective at achieving critical accuracy rates.
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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Retrieval Augmented Generation (RAG) is a popular, low-cost technique to boost GenAI response quality. But for many use cases, it still falls short.
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.
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.
