Deployment of a Survey Chatbot by Fastwheel.ai for a Market Research Company.
Project Context and Overview
Fastwheel.ai partnered with a leading company in the survey software industry to enhance their data analytics capabilities using AI. The main objective was to develop a survey chatbot that could reduce the company's operational costs by 80% while improving the efficiency and accessibility of survey data analysis.
Technical Objectives
Enhanced User Interaction with Data: Simplify how end-users, primarily enterprise business teams, interact with and derive insights from survey data.
Cost Reduction: Implement an AI-driven solution to significantly decrease the financial overhead associated with traditional survey data analysis.
Advanced Data Handling and Processing: Facilitate the direct handling of diverse data formats to streamline workflows.
Solution Design
The solution centered around a custom-built AI chatbot utilizing a Retriever-Augmented Generation (RAG) model. This design was chosen for its ability to dynamically generate accurate and relevant responses from a corpus of survey data. Key technical features included:
Local Language Model Deployment: Instead of relying on costly external API calls to large language models like GPT, Fastwheel.ai utilized a locally hosted model. This approach not only reduced costs but also allowed for greater customization and control over data security.
Dynamic RAG Framework: The RAG setup involved a dual-component system where a retriever first extracts relevant data snippets based on the query, and a generator then synthesizes these snippets into coherent, actionable insights. This method ensures that responses are both accurate and contextually relevant.
Data Input Flexibility: The chatbot was engineered to accept inputs in multiple formats—text, Excel, PDF, and multimedia files like audio and video—catering to the diverse needs of survey formats. This was made possible through the integration of advanced OCR and voice recognition technologies, which converted non-text data into analyzable formats.
User-Centric Design and Interaction: A significant focus was placed on the user interface, designed to be intuitive and responsive, with features like interactive dashboards, real-time data analysis, and automated report generation. This design was informed by a series of user stories that detailed typical end-user interactions and needs.
Implementation Phases
The project was executed in several distinct phases:
Initial Consultation and Planning: Detailed sessions with the client to understand their specific needs and the types of surveys conducted, shaping the customization of the model.
Model Development and Training: The LM was trained on a dataset synthesized from historical survey data alongside newly generated synthetic data, which helped improve the model's accuracy and response quality.
Interface Development and Integration: Development of a web-based interface that allowed users to interact with the chatbot, upload data, and receive analysis. The interface included features for saving and sharing insights and configuring the model's response behavior.
Pilot Testing and Iteration: Before full deployment, the chatbot was tested in a controlled environment with real users to gather feedback and refine functionalities.
Outcomes and Business Impact
The deployment of the survey chatbot resulted in:
Cost Efficiency: An 80% reduction in costs associated with survey analysis, thanks to the elimination of external dependencies and reduced labor.
Increased Analytical Speed and Accuracy: Faster turnaround times for survey analysis with higher accuracy and less human error.
Improved Decision-Making: Enterprise clients reported improved decision-making capabilities due to quicker and deeper insights into survey data.
Conclusion
Fastwheel.ai's project with the survey software company demonstrates a successful application of AI in optimizing data analytics processes. This case study exemplifies how AI can be strategically deployed to not only reduce costs but also enhance the overall quality and accessibility of data-driven insights in the survey industry.