Weaviate is a Dutch open-source vector database that stores data as vectors for AI applications. It is the ideal database for semantic search, RAG systems, and recommendation engines.

How does Motran use Weaviate?

Motran builds RAG systems and semantic search engines on Weaviate. We index business documents as vectors so AI models can always provide relevant, source-based answers to questions from employees and customers.

Which sectors benefit from Weaviate?

Weaviate is particularly valuable for the financial sector for searching compliance documents, retail for product recommendations, and healthcare for unlocking medical knowledge bases.

Weaviate.

Databases

Open-source vector database for AI applications. Ideal for semantic search, RAG, and recommendation systems.

By Weaviate

About

What is Weaviate?

Weaviate is an open-source vector database specifically designed for AI applications. It stores data as vectors (numerical representations) enabling semantic search, retrieval-augmented generation (RAG), and recommendation systems. Weaviate is a Dutch company and a pioneer in the vector database market.

Our approach

How Motran uses Weaviate.

Motran uses Weaviate as the core of RAG systems for clients wanting to unlock their business documents through AI. We index documents, manuals, and knowledge articles as vectors in Weaviate, so AI models can retrieve relevant context when answering questions. This results in accurate, fact-based AI answers.

Use cases

How Weaviate adds value.

Vector search for RAG

Vector search for RAG: business documents are stored as vectors so AI models retrieve the most relevant passages for accurate answers.

Semantic search

Semantic search: users find information based on meaning, not just keywords, fundamentally improving the search experience.

AI-driven recommendations

AI-driven recommendations: products, articles, or content are recommended based on semantic similarity to user preferences.

Benefits

Sector benefits.

Weaviate offers great value for the financial sector in searching extensive compliance and policy documents, the retail sector for personalised product recommendations based on semantic similarity, and healthcare for quickly and accurately unlocking medical literature and protocols.