Why does Enterprise Information LLM Assistant matter to SMEs?

Enterprise information LLM is the powerful solution for the organization to enhance customer experiences and provides valuable insights from custom's unstructured data.

  • Customization and Specialization
  • Data Privacy and Security
  • Increased operational efficiency
  • Enhanced innovation
  • Proprietary Data Utilization
  • Scalability and Control

Microsoft Enterprise ChatGPT

Microsoft Azure has integrated ChatGPT into its Azure OpenAI Service, offering an enterprise-focused solution that enables businesses to apply advanced AI models to their operations.

  • Privacy: Data is safeguarded, remaining isolated from OpenAI's operations.
  • Control: Network traffic is fully isolated, and enterprise-grade security controls are integrated.
  • Value: Integrating internal data sources and services adds substantial business value.

Microsoft Azure has integrated ChatGPT into its Azure OpenAI Service, offering an enterprise solution that enables businesses to apply advanced AI models to their operations. This integration allows customers to innovate by leveraging AI technologies like DALL-E 2, GPT-4, and Codex, supported by Azure's computing capabilities and security infrastructure.


Disadvantage: Too expensive for SMEs. Due to the cost of Microsoft Enterprise ChatGPT, small and medium size organization can not afford to use this solution.

Enterprise LLM solution for SMEs organization

SMEs (small and medium size organization) needs an affordable Enterprise AI solution which can meet the following requirements.

  • Easy to use and operate: organization can easily upload unstructured data. End-user can use it easily.
  • Affordable: less cost within the SMEs budget.
  • Accurate with reference link: With the reference link, the organization can update the outdated data.


The following technologies are used to build the Enterprise AI Solution

  • RAG: Retrieval Augmented Generation, is an approach that enhances the capabilities of large language models (LLMs) by allowing them to fetch relevant information from external databases or knowledge bases to inform their responses.
  • LangChain: It acts as a versatile interface that simplifies the process of working with these models by providing tools for prompt management, memory, indexing, and agent-based decision-making.
  • LLaMA2: LLaMa models are known for their ability to understand and generate human-like text, which enables them to be used in a variety of applications that require interaction with users in natural language.
  • Fine tuning: Faster and less memory usage for AI training. Less GPUs required.

Solution Capabilities

The prompt flow implements the RAG pattern Retrieval Augmented Generation to extract the appropriate query from the prompt, query AI Search, and use the results as grounding data for the foundation model

  • Prompt Engineering: Adaptable Prompt Structure; Dynamic Prompts; Built-in Chain of Thought (COT)
  • Document Pre-Processing: Text-based: pdf, docx, html, htm, csv, md, pptx, txt, json, xlsx, xml, eml, msg; Images: jpg, jpeg, png, gif, bmp, tif, tiff
  • Custom Document Chunking: Content extraction from text-based documents chunking and saving metadata into manageable sizes to be used in the RAG pattern
  • AI Search Integration : Employs Vector Hybrid Search which combines vector similarity with keyword matching to enhance search accuracy full-text search, semantic search, vector search, and hybrid search


The integration of LLaMa 2, RAG and LangChain enhances its capabilities by allowing it to reference a broader set of information when generating responses. This means that a LLaMa 2-powered application can provide more accurate and contextually relevant information by retrieving data from an external knowledge base before generating a response. This is particularly useful for enterprise solutions where the AI needs to provide information that is not only correct but also specific to the context of the organization and the query at hand.