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Large Language Models and RAG Pipelines

With “Agolo Inside,” any RAG Pipeline feeding an LLM and GenAI application will be better, faster, and more efficient.

Use Cases

LLMs Thrive When They are Powered with Clean Data. Agolo Makes It Easy to Integrate Unstructured Data Into Any GenAI Application via RAG Pipelines.

Current Challenges: More than than 80%+ of this company's product support-related data is in HTML, PDF, CSV, TXT, and other difficult-to-access unstructured formats. The company embraced LLMs to create several applications, but couldn’t rapidly finetune the LLMs with this unstructured data.

  • Agolo Solution: Agolo transforms an organization's product-related data into a clean entity graph data store. Regardless of the format, Agolo can integrate the data into a RAG pipeline that fetches relevant information from a large corpus of unstructured data in Agolo’s data store. Agolo can load unstructured operational data, including call and chat transcripts, warranty claims & verbatims, knowledge base articles, social media posts, online support forums, product reviews, service tickets, and more. This creates the relevant context to provide clean, up-to-date responses in LLM-based Generative AI applications. The data retrieval model can then search through Agolo’s data store to find the pieces of text that are most relevant to the specific input query.

  • Business Value: With Agolo, any LLM-based GenAI applications are better, faster, and more efficient. LLMs thrive when they are powered with clean data, but most of this data is difficult to access. Agolo makes it easy.