How Raffle works
The Raffle framework
Raffle has been designed to effortlessly understand and interpret user search queries, even when they contain spelling mistakes, synonyms, or varied phrasing. By analysing search inputs, Raffle provides valuable insights, identifies trends, and generates relevant content - empowering businesses to make informed decisions and drive success.
Powered by advanced AI and natural language processing (NLP), Raffle understands queries in everyday language. This is also known as hybrid search, where the search dynamically adapts to different types of information, matching both the meaning and words to deliver accurate results.
With Raffle, businesses can improve customer service, boost productivity, and make data-driven decisions, all with reliable and precise information at their fingertips. It streamlines the search process, allowing users to quickly find the right information across multiple systems. Plus, Raffle is fully customisable, enabling you to tailor tools, sources, and designs to meet the unique needs of your organisation.
How vector search works:
Traditional keyword search works by directly matching the exact words you type to a text on a website. However, this approach has significant limitations; if you use a synonym, make a spelling mistake, or try alternative spelling, the search often fails to find what you need. In fact, traditional keyword search is only effective around 10% of the time, as it relies purely on word-for-word matching, without understanding the context or meaning behind your query.
Vector search takes a much smarter approach. Instead of looking for exact word matches, it measures the “distance” between a query and the relevant content on your site. This means it can understand the meaning behind your words, even if you use a different phrase, make a spelling mistake, or enter a full sentence. It delivers more accurate results by focusing on context, not just keywords.
The real advantage of Raffle’s vector search lies in its customisation. While many vector search models are pre-trained on generic data, leading to average performance, Raffle trains its model specifically on your content. This allows it to deliver far more precise and relevant results, tailored to your users’ needs. In short, vector search doesn’t just find the words - it finds the answers.