Traditional RAG (Retrieval-Augmented Generation) and embedding-based tools rely on semantic similarity searches. While this approach works for simple information retrieval, it falls short when analyzing complex business conversations.
Our pipeline is expensive to run because we prioritize accuracy and context over simple semantic matching. This investment in infrastructure means you get insights that actually drive business decisions.
“We had an AI-native enterprise customer with 5 engineers spend 6 months trying to build a similar solution using RAG and embeddings. They couldn’t get anywhere close to what BuildBetter produced in our reports. Their contract with us was 50x cheaper than what they’d already spent trying to build it themselves.”