For a long time, setting up any kind of search engine relied on indexing and organizing data for it to later be found with exact keyword-matching that did not really allow for a thorough search experience and often left the users without a lot of relevant information.

With the recent advances in AI technologies, especially natural language processing (NLP), data can now be analyzed deeper with a nuanced understanding of its context and meaning.

This allows AI to create vector representations of information and allows vector search to provide search results that go beyond simple keyword matching.

What is Vector Search?

As we mentioned, vector search is a technology that makes huge loads of information searchable in an intuitive way that goes beyond keyword matching. This is made possible with natural language processing techniques that can analyze data (documents, emails, code, etc.) to grasp the meaning and relationships between words and concepts.

Once this understanding is achieved, NLP models can transform data into numerical representations called „vectors“. In essence, these vectors are multi-dimensional arrays of numbers where each dimension captures a specific aspect of the meaning.

This technique creates a high-dimensional virtual space where similar concepts (related topics, ideas, synonyms) cluster closer together. Because NLP allows for understanding the actual meaning of the information, it can cluster together documents that discuss „cars“ and „vehicles“ despite their different keywords.

Vector Searching in Action

When you enter a search query, the NLP algorithms start working. They analyze the user's query and create new vector representations based on the query in the same high-dimensional space in which all the other searchable data resides.

Once in action, the vector search engine doesn't just scan for exact keyword matches. Instead, it calculates the distance between your query vector and the vectors representing all the other documents. 

Documents whose vectors are closest in this high-dimensional space are considered to be conceptually similar to your query. This means even if documents don't contain the exact keywords you used, they can still surface in your results because their underlying meaning aligns with your search intent. 

For example, searching for "social media marketing trends” can lead users to find results about content strategies, influencer partnerships, and video marketing— all because they relate to the broader concept of online engagement.

What vector searching essentially does is enable a cognitive search that focuses on the meaning and context of your query. This shifts the paradigm from search engines simply giving you the matching results to search engines giving you results that you need and that are highly relevant to your query despite not being connected with exact keywords.

With vector search in place, you can ask any question and get all the answers.

How Does Vector Search Benefit Enterprise Search?

Vector search benefits enterprise search in more ways than one. Firstly, having a smart search system that interlinks all company data allows employees to easily find all the data that is related to what they want to find.

For example, a project manager monitoring a new product launch could easily get a comprehensive market analysis with a vector search pulling market research reports, competitor analysis, internal sales data, and customer feedback surveys. All of this would now be possible with a simple search like “Customer sentiment about product X.”

In addition, semantic understanding made possible by vector search can lead to a truly conversational experience, where employees can refine search results with additional queries and questions that lead them to desired results. 

This means that you can use follow-up questions and ask for new, relevant information on the go.

Another way in which vector search benefits enterprise search is that it significantly reduces or even completely eliminates the need for data indexing and taxonomy.

Security has always been one of the concerns when it comes to enterprise search software because businesses often don’t want to trust third-party servers to store their sensitive data.

This is a necessary step for enterprise search software solutions that rely on indexing data to make it searchable, as copying data on their server gives them centralized control of the indexing process and allows for greater processing power than might be available within a company's infrastructure.

With vector search, this step is eliminated altogether because it allows enterprise search software to process the data only in working memory without storing it on 3^rd party servers.

Vector Searching with Akooda

Akooda enterprise search recognizes the benefits of employing NLP technologies in the business environment and makes searching an intuitive and easy experience.

Availability of information is one of the pillars of enterprise success, and that is why we utilize vector search to make sure that no relevant information remains forgotten or unused, even if it isn’t obvious through simple keyword matching.