Internal Intelligent Assistant for Industrial Machinery

Efficient and Transparent Knowledge Retrieval with LLM-Powered Smart Chatbot.
06/09/2023
Internal Q&A Document Assistant
Manufacturing

Background

An industrial machine manufacturer needs a scalable, intelligent solution to assist technical support “hotliners” in customer troubleshooting.  The client is looking for the right generative AI project to kickstart internal innovation and stay ahead of the curve. Alongside a dedicated NLP consulting mission on a separate but related project, this presents the company’s first foray into machine learning and generative AI, guided by Effixis.

Problems & Challenges

The client sells a wide variety of complex industrial machines with technical manuals exceeding hundreds of pages, making it challenging to provide timely and thorough support. Other technical documentation is embedded in solution diagrams, tutorial videos, and other formats.

The client’s technical documentation covers a range of sources, including PDF manuals, tutorial videos, and solution diagrams.  We developed a unified knowledge base to store this data in a special format for the assistant to access. Developing LLM-powered chatbot solutions presents additional hurdles to correct for hallucinations and false responses. This required careful system design and prompt engineering to minimize the likelihood of such issues. Additionally, we aim to deliver one-on-one training sessions to onboard the app users and outline the strengths and potential weaknesses of our solution.

Solution & Examples

We developed a state-of-the-art intelligent assistant based on LLM technology. The system functions primarily as a “smart chatbot”, with access to proprietary, domain-specific knowledge relevant to the client. It has the capability to semantically search through machine manuals and tutorial videos to accurately respond to user questions. In effect, the chatbot acts as an intelligent agent, autonomously searching and referencing the relevant sources as required by the user’s question. Importantly, the system is built with transparency in mind, making it clear which sources it uses to respond to a question and raising a warning if an answer cannot be found. Additionally, the user interface (UI) allows for additional control to filter by machine type, source, and more.

One exciting feature is the intelligent search through tutorial videos. By searching against the subtitle transcriptions, we can open and display videos at the exact timestamp matching the user’s query. Given the scope and breadth of tutorial videos, this can greatly simplify the searching and retrieval of relevant information.

 

Below, you can find examples that illustrate how our internal intelligent assistant operates and the tangible benefits it brings to the organization.

  • Example 01: the system correctly deflects irrelevant questions and the system automatically flags the response as being unreferenced.

 

The system correctly deflects irrelevant questions.

 

  • Example 02: showing the transparency of the system, clearly displaying each step of the search and response as well as citation sources.

 

Each step of the search and response are clearly displayed.

 

Technology & Tools

Azure
Python
Generative AI (LLM)
Pinecone Vector Database

Results

In the context of the case study, the implementation of the intelligent assistant based on LLM technology yielded significant results, addressing the client’s challenges effectively:

  1. Successful Proof of Concept (PoC) and Partnership: The development of our LLM-powered intelligent assistant not only served as a successful proof of concept but also exemplified a productive partnership. This innovative technology showcased the feasibility of addressing complex information challenges within the organization.
  2. Efficient Support Processes: By providing employees with quick and accurate answers to their technical inquiries, the assistant streamlined support processes. This not only saved valuable time for both support staff and employees, but also optimized resource allocation within the organization, resulting in a more efficient and responsive support system.
  3. Simplified Information Access: Complex technical information became easily accessible and actionable, enhancing user experience and productivity. The intelligent video search with precise timestamps simplified information retrieval, making it a valuable tool for employees.

 

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