AI tools aren't here to replace human agent support teams. What they can do is supercharge productivity, improve job satisfaction and retention, and make every human agent an expert from day one.
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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.
AI is evolving at a pace that makes it difficult to stay current. DeepSeek is the latest disruption in the AI landscape. This article will help you discover the strengths, weaknesses, and ideal use cases of 3 AI tools to help you determine which AI best suits your needs.
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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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 If the content is outdated, incomplete, or inconsistent, the AI may produce irrelevant or inaccurate outputs, eroding customer trust.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.
