AI Assistant Case Study: Enhancing Knowledge Sharing with Advanced AI Solutions
Is your team drowning in a sea of documents and struggling to find the right information quickly?
Our client, a mid-sized organization faced this exact challenge. The sheer volume of documents and personnel made locating information hampering employee productivity and causing frustration.
So, navigating complex documentation can be a headache. Broscorp has the solution: an AI assistant for business that transforms the way users find answers in extensive documentation. Our developers leveled up knowledge sharing for our client, and here’s how.
Part I. Project Overview
Client and Business Goals
Our client, a consulting and management services provider, specializes in business applications, automation, cloud, and cybersecurity. Their expertise ensures safe and scalable systems that help businesses thrive.
What objectives did our client aim to achieve?
- Enhance knowledge-sharing efficiency among employees.
- Reduce the time required to find critical information.
- Improve overall productivity across different company departments.
- Implement a scalable solution that can adapt to your company’s increasing volume of documentation.
Additionally, the need for data confidentiality meant that using third-party SaaS tools was not an option. Therefore, our client required a custom solution.
Product Description
Imagine asking a question to your company database and receiving precise answers within just 5-20 seconds. Our AI assistant software makes this possible!
Here’s how it works:
- Receives user queries via chat, interprets the intent, and determines the best approach.
- Preprocesses queries to find the most relevant sources within your company’s documentation, ensuring accurate retrieval of information.
- Summarizes documents and generates comprehensive answers, providing source references for reliability.
Additionally, accessible as a web application and through MS Teams integration, our AI chatbot ensures ease of use across platforms.
Part II. Technology Solution
Implementation Details
We implemented a chatbot powered by a large language model (LLM), namely GPT-4o. Our team can also develop AI assistants on Llama or Mistral, however, the decision to choose them should be based on specific business requirements – they feature different training capabilities and higher expenses. We minimized model hallucination using Retrieval Augmented Generation (RAG) architecture and ensured accurate responses. This virtual AI assistant efficiently searches the knowledge base and provides precise answers, enhancing information accessibility.
Tech Stack
- Azure OpenAI: Provides access to the latest OpenAI models with enterprise-level privacy and a consumption-based pricing model.
- Azure AISearch: A managed vector database offering quick start-up and reliable performance.
- Python-based Web Application: Leveraging Python’s robust tooling for working with LLMs.
Obtaining an enterprise license directly from OpenAI can be challenging, but Azure OpenAI offers a solution with enterprise-grade compliance with regulations like GDPR, CCPA, etc. This ensures robust data privacy and security measures, crucial for handling sensitive information.
Additionally, using GPT from Azure prevents models from being trained on your documents, further protecting your data. Learn how to build a custom Azure AI chatbot using your own data.
Performance
- The chatbot responds within 5-20 seconds, depending on document size. The primary bottleneck is LLM text generation. The GPT-4o model variant best balances answer quality and response speed.
- The application uses an index for efficient searching, updated regularly through automated jobs. Data is ingested from various sources, including:
- PDF and Word documents on SharePoint;
- Proprietary knowledge base system with articles;
- OneNote notebooks.
Results
- Efficiency: The AI chatbot significantly reduced the time spent searching for information. Using this software, employees have learned to handle a substantial portion of issues they used to bring to the management on their own.
- Accuracy: Provided reliable and accurate answers with source references.
- User Satisfaction: Improved user experience by making information retrieval straightforward and quick, demonstrating the effectiveness of AI integration in business.
Challenges
A key challenge in AI assistant development was handling ambiguous queries due to insufficient context. Unlike human experts who can ask follow-up questions, the current system sometimes struggles with ambiguous terms, leading to less accurate answers.
The approach heavily depends on the quality of company documentation. If the processes are not well documented or the documentation is not structured, the assistant will not be able to provide useful answers.
Part III: Use Cases for Your Industry
We can create an AI chatbot to address the specific characteristics of your domain, seamlessly integrate it with your company’s knowledge base, and incorporate any necessary functions to develop a solution that perfectly fits your needs.
Here are some examples of use cases:
- Enhancing internal knowledge sharing for faster decision-making.
- Facilitating employee onboarding with quick access to training materials.
- Streamlining customer support with instant, accurate responses.
- Boosting sales teams with immediate access to product information and FAQs.
- Assisting in research by quickly gathering and summarizing relevant data.
All these benefits can be achieved by enhancing productivity with AI.
Conclusion:
The huge volume of documents makes finding the right information quickly difficult and results in inefficiency and dampened productivity. But time, undeniably, is the most valuable asset for businesses that want to thrive. So, why waste precious hours sifting through massive documentation when you can have crucial information at your fingertips in seconds with an AI chatbot for companies?
Are you interested in building an AI chatbot for your organization?
Contact Broscorp today to schedule a consultation.
