Case Study: ProBravo

Situation

In a world flooded with information, finding the right solution to a specific problem can be daunting, especially for non-technical individuals. When faced with a technical issue, these individuals often struggle to identify the correct professional to address the problem, particularly when they don't fully understand what's wrong. While search engines are a common first step, the results can be overwhelming, with no clear indication of which professional is the best fit. The top results in a search may not necessarily be the most suitable, as rankings are influenced by various factors that don't always align with quality or relevance. Additionally, comparing different professional profiles can be a challenge, especially when the information is unstructured or too technical for the average user to decipher. This leaves non-technical people feeling lost and inadequate, uncertain of how to proceed in finding the right help.

Task

The primary objective was to create a solution that would empower non-technical individuals to confidently find and select the best professionals for their needs without feeling overwhelmed or unsupported. The goal was to develop a human-like AI system that could interact with users to gather detailed information about their issues, even if the users themselves were unsure of the technical specifics. Based on the information collected, the platform would generate a list of professionals whose skills, experience, and expertise matched the user's needs. Each professional promoted on the platform would come with an explanation of why they were a good fit for the specific issue, ensuring transparency and trust. Additionally, the platform would allow users to review detailed profiles of the professionals, reducing the risk of hiring someone unknown or unreliable. A feedback system would also be integrated to share previous experiences, helping users make informed decisions.

Action

To achieve this, Large Language Models (LLMs) were employed to evaluate user requests, enabling the development of a chatbot with a human-like interaction style. This AI-driven system was designed to collect specific, relevant information about the user's needs, even when the user's understanding of the issue was limited. Using vector databases to calculate similarity, the platform could then generate a list of professionals who best matched the request, considering factors such as skills, experience, and knowledge.

All professionals in the user's area were evaluated and ranked according to how well they fit the user's needs, with the reasons for their selection clearly explained. To ensure that professional profiles were comprehensive and accurate, AI tools were also used to assist professionals in completing their profiles, including the implementation of speech-to-text algorithms for ease of use. The platform made pricing information transparent to users, eliminating the possibility of unexpected costs, and allowed users to book professionals directly through the system.

Result

The result was the launch of a cloud-based, AI-first matching platform that significantly simplified the process of finding the right professional for non-technical users. Although still in its early stages, the platform received positive feedback from both users and professionals, indicating strong potential for growth. The roadmap for future development is packed with exciting new features, reflecting the dynamic nature of the platform. One notable feature is the system's ability to suggest alternative solutions if no professional is immediately available in the user's area. Users can review these suggestions and choose to be contacted once the AI identifies a potential professional nearby, ensuring that they are never left without support.

Reference

Explore https://www.probravo.com/