Better data quality and more data security thanks to vector database
Vector databases make it possible to use unstructured data efficiently to gain deeper insights. They form the basis for intelligent search, the development of innovative services, and advanced AI features such as service automation, product recommendations and autonomous content creation.
Vector database
Respond more efficiently to complex requests
Increase your innovative strength by extracting relevant information from your arbitrary, unstructured data faster and responding more efficiently to complex business requests. In combination with leading providers and a highly secure Swiss infrastructure, we develop solutions that are powerful, secure and sustainable.
Vectors are mathematical objects that play a central role in computer science, particularly in machine learning and artificial intelligence. In vector databases, data is stored as vectors, a series of 32-bit or 16-bit numbers consisting of the characteristics or properties of objects, such as text, images or audio.
Vector databases are revolutionizing the way companies handle data. With their ability to analyze unstructured data in depth, they offer a clear competitive advantage for companies that want to use their data efficiently.
The best vector database solutions
Companies wishing to implement a vector database have various options depending on their requirements:
Open Source: Providers such as Milvus or TypeSense offer a scalable, flexible solution for companies that want full control over their data and infrastructure.
Cloud-based: Ideal for companies that rely on rapid scalability. Pinecone impresses with its rapid deployment, simple integration and 99.9% SLA.
Hybrid: For hybrid setups, PostgreSQL with PGVector enables the integration of vector data into existing relational databases, which is particularly attractive for companies that want to combine traditional database functions with modern AI applications.
For companies with high data protection requirements, there is also the on-premise option, which enables full data sovereignty and compliance with regulations applicable in Switzerland. The choice depends on the desired flexibility, scalability and data protection requirements.
Swiss-based infrastructure
At Mindnow, we rely on a combination of leading cutting-edge providers with a secure infrastructure. In this way, we guarantee not only technological excellence but also an infrastructure hosted in Switzerland that meets the highest data protection and compliance standards. You can rest assured that your sensitive data is processed in a stable and trustworthy environment.
Knowledge graphs: Unbeatable combination
In combination with Knowledge Graphs, vector databases also offer even deeper insights into your data and provide a powerful solution for qualitative and quantitative analyses.
A knowledge graph is a structured representation of knowledge that is organized by nodes and edges. It is used to represent information and its links in a way that is accessible and processable by computers. The knowledge graph makes the semantic relationships between the data explicit and thus enables complex queries and analyses. Knowledge graphs can be used to build recommendation systems.
High-dimensional vectors are usually generated from unstructured data (e.g. text, images, audio) using machine learning or deep learning. These vectors represent semantic information in a mathematically structured form so that they can efficiently calculate similarities and relationships in the data.
A knowledge graph can be integrated into a vector database by representing the entities and their relationships using vectors. The individual entities (e.g. "persons", "products", "places") and their connections (e.g. "works at", "is located in") can be encoded by vectors, which make it possible to calculate semantic similarities between these entities.
Advantages of vector databases
1
Semantic search
Vector databases make it possible to search data contextually and not just on the basis of keywords.
A vector database can enable the search for related concepts or similar entities in a knowledge graph by searching for vectors that are semantically similar. For example, a search query for "doctors in Berlin" could return not only doctors in Berlin but also related entities such as clinics or healthcare providers that are linked to doctors in the knowledge graph.
2
Personalization
Just as for recommendation systems, vector databases are also suitable for customized advertising.
3
More precise analysis
By being able to recognize deeper connections in the data, they deliver more intelligent and relevant results.
4
Optimized for AI
These databases are specially designed to meet the requirements of AI models and ensure smooth interaction with LLMs.
5
Advanced applications
Vector databases form an essential foundation for advanced applications such as semantic searches, service automation, product recommendations and automatic content creation.
Section by Vadim Kravcenko
CTORelevant results and better user experience
Vector databases provide a crucial basis for modern software solutions. Thanks to their ability to store data as vectors, they enable precise and efficient processing of unstructured information such as text, images or audio.
A concrete example is the integration of a vector database into a search system. While a traditional database only searches for exact keywords, a vector database can find semantically similar content. This means that users receive relevant results even if they do not enter exactly the right keyword. Such systems are significantly more robust and offer a better user experience.
By combining with existing relational databases, such as PostgreSQL with PGVector, companies can upgrade their existing infrastructures without having to sacrifice proven database features.
In practice, the use of vector databases leads to applications that find relevant information faster, make better recommendations and recognize complex relationships in the data – all without adding unnecessary technical complexity.
