Local LLMs are suitable for many AI workflows, especially those that run backgroud tasks, process large amounts of data, or handle secure or sensitive data. Here are some use cases where Local LLMs can be beneficial:

• Text summarization and generation:

  Creating concise summaries of documents, generating reports, writing creative content, and drafting or sending emails or messages.

• Data extraction and classification:

  Extracting key information from any sort of documents, reports, or records, including images, scanned documents and unstuctured data, emails or messages.

• Chatbots and virtual assistants:

  Providing personalized support, answering questions based on documents or data you supply, and automating service enquiries.

• Knowledge base management:

  Creating and maintaining internal knowledge bases and providing information to employees.

• Personalized content creation:

  Tailoring content to specific user preferences and needs, making use of internal user or company data.

• Sentiment analysis:

  Analyzing the emotional tone of text data, and providing insights on emails, feedback, reviews, or media posts.

• Offline Processing:

  Being able to run services and provide information without an internet connection, from a local dataset.

• Code generation and review:

  Assisting developers with code completion, automatation test case generation, bug detection, code analysis and review, and documentation.

• Document translation:

  Translating text or documents between languages without needing an internet connection.