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Chatbots for your custom data with RAGs

Turn your text data (e.g. FAQs) into a powerful chatbot giving your customers answer on point.

With the rise of large language models (LLMs) like ChatGPT and Generative AI, new possibilities have emerged. These advancements make transforming text and image data into robust applications more accessible than ever.


Tailored Chatbot Responses with RAGs

One such application is the creation of customized Chatbots using retrieval augmented generative AI systems (RAGs). By integrating your company's internal text data into LLMs like ChatGPT, these systems gain the knowledge to respond accurately to inquiries based on your specific information. Essentially, RAGs fine-tune pre-built LLMs with your data.


Creating Tailored Chatbots

For instance, you can merge FAQs, Confluence pages, or internal documents in various formats (such as .pdf or .docx) with an LLM to develop chatbots tailored to your customers' needs.


Optimized User Experience with Data Security

This approach significantly enhances user experience by providing precise and well-formulated responses derived from your information. Moreover, you can rest assured about data security and privacy. RAGs are designed to safeguard your internal data, ensuring it stays within your control without being shared externally with providers like ChatGPT or OpenAI.


For inspiration, feel free to browse through some of the highlighted case studies below.

Turn your text data (e.g. FAQs) into a powerful chatbot giving your customers answer on point.

Michael Gramlich

Chatbots for your custom data with RAGs

Related Case Studies

RAG approach on Azure with OpenAI

RAG approach on Azure with OpenAI

Use of ChatGPT to increase the efficiency of analysts in the telecommunications industry

Smart Meter Data Warehouse and Insights

Smart Meter Data Warehouse and Insights

Powering a full single customer view by integrating legacy and current datasets

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