Hyper AI is your gateway to next-level Generative AI services. With our expertise in Large Language Models (LLM) and Retrieval Augmented Generation (RAG), we’re at the forefront of transforming businesses through cutting-edge natural language processing solutions.
Open LLM models are very powerful tools, yet they no Access to the organization’s Private Data.
and they have an out of date Knowledge, which make them more likely to go into Hallucinations.
Hyper AI provides its customers with RAG so, they can have answers from their privet data with up to date knowledge which makes the model more immune of hallucination,
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Relevant Chuncks
Large Language Models (LLMs)
Provide Personalized Customer Experience
Understand Customer Queries
With LLMs, we excel at understanding the nuances of customer queries, including variations in language, context, and intent, allowing your business to tailor responses based on individual customer needs and preferences.
Real-Time Interaction
LLMs enable your business to engage in real-time, conduct natural conversations with customers, and therefore a more human-like and intuitive customer experience.
Personalization
LLMs help drive high customer satisfaction by providing targeted responses and recommendations based on customer data and historical interactions.
Automated Processes
Improve efficiency using LLM-powered chatbots and voicebots, your business can handle customer queries with greater precision and speed.
Retrieval Augmented Generation (RAG)
Cut Data Analysis Time and Cost
Contextual Understanding
RAG incorporates retrieved information from large datasets into the generation process, ensuring accurate and contextually relevant outputs for insightful data analysis.
Automated Summarization
RAG models can automatically summarize retrieved information, providing concise overviews of large datasets, and therefore, accelerated analysis process and faster decision-making.
Natural Language Interaction
With RAG, analysts can use everyday language to ask complex questions, and they receive detailed answers in a conversational manner, making the analysis process more intuitive.
Efficient Information Retrieval
RAG efficiently retrieves relevant data from vast datasets, streamlining the initial phase of data analysis, and saving time for analysts.