The AI capability 'In app search' for civil engineering services companies in Deskhero works by leveraging OpenAI Embeddings API and the platform's data. Each ticket, knowledge base article, scraped website, and other relevant data are sent to the OpenAI Embeddings API to generate embeddings vectors. These vectors are then stored in a vector database. When a user performs a search query through the search bar, the query is run against the OpenAI Embeddings API and compared to the stored vectors. The system searches for similar and relevant data within the platform and presents it as search results. Additionally, if the query is formatted as a question, the system uses ChatGPT to generate a human-like reply based on the matched data.
Use case 1
A civil engineering services company wants to search for information about a specific construction material they used in a previous project. They enter the query in the search bar, and the AI capability 'In app search' matches it with relevant data, such as past project details, material specifications, and supplier information, providing a comprehensive answer to their query.
Use case 2
A civil engineering services company needs to find solutions to common design challenges faced in bridge construction. They enter a question in the search bar, and the AI capability 'In app search' retrieves relevant knowledge base articles, past resolutions, and best practices related to bridge design, helping them make informed decisions and overcome design obstacles.
Use case 3
A civil engineering services company wants to access the latest regulations and guidelines related to environmental impact assessments for infrastructure projects. They perform a search using specific keywords, and the AI capability 'In app search' presents them with up-to-date knowledge base articles, legal documents, and industry standards, ensuring compliance with environmental regulations.
