Low-code AI-powered search engines: the future of search for any application
A low-code AI-powered search engine for any data, and any-data source, to use in my application? Is this possible?
Searching is a huge world for companies and applications as well, and indeed, it is complex.
Any organisation has the colossal challenge when indexing, searching and getting information: there are infinite internal archives which store information, in a variety of formats and in different languages. Nowadays, the great part of solutions related to searching and getting access to so much quantity of data, either are extremely complex or require highly specialized developer teams (with the training, infrastructure and maintenance service particularly high costs), or are partial solutions, which “only” have the aim of searching, and finally leave all the complex, difficult and tedious indexing task to the developers.
As a result, the horizon of new generation powered-search engines which solve efficiently the lack of present engines are already a fact, a reality. They are suitable and capable systems that not only automatically index any kind of data, but also offer AI searching algorithms.
As a matter of fact, in this current context — the new generation of AI- powered search engines and the “low-code” concept — Nuclia solution takes special relevance. Nowadays, it is possible for any company to set up a multi-language semantic search engine on unstructured data (in different formats, archives and languages) and integrate it to any application in record time and with low-cost implementation.
But what exactly is “low-code”? How can I apply it to my searching needs and give access to my unstructured data?
Low-code is, mainly, just another way to make software. It allows developers to expand parts of an application in a faster and more efficient way reducing code typing.
In a way, some of the things that have to be developed by typing code, with the “low-code” are “developed” from a set of graphical interfaces that allow to structure and configure functionalities, characteristics and behaviours of the parts of the software being built.
Thanks to this way of programming, development teams save much of the issues related to infrastructure and the implementation of the heaviest and most tedious parts that any software has and, as a result, can focus on that 20% of functionality that differentiate one application from another, so, in short, they can focus on what really brings the most value.
A good way to understand this new paradigm of programming is car factories: robots that automate car production lines, do not design the vehicle’s engine or program the on-board computer, but greatly speed up the vehicle manufacturing process instead. This is exactly what low-code does: it allows developers to focus on the most important things in the product: from engine design, to the study of aerodynamics or safety tests, while leaving to robots the most repetitive and boring tasks.
So, as a developer and as a company, what are the main advantages of this new generation of low-code AI-powered search engines?
While a huge list of the benefits of adopting these solutions for developers and businesses could be made, today we will focus on 4 major direct benefits:
1) Radical time and cost savings.
The adoption of a low-code solution allows developers to forget about the most tedious and ungrateful parts involved in implementing a search system. In this sense, companies experience very significant cost savings because their development teams can focus on what really adds value to business logic.
2) Limit the need to have expertise within the team of a large different technology stack.
Having a part of the team dedicated to understanding complex software to be able to offer both “basic” and search functionality is hard to justify. Using an AI-powered search engine API ensures that search functionality is ready to be integrated in minutes.
3) It allows you to forget about the deployment, maintenance, indexing processes and scalability required by traditional search engines.
Traditional search engines (Elastic, Solr, etc.…) require the infrastructure maintenance where they are installed and the ability to scale it if necessary. They also require, in their out-of-the-box format, that they be provided with data in a specific format in order to be able to index content.
The knowledge required to do so is not straightforward. Using an AI-powered search engine API, developers and businesses can forget about maintenance, scalability, and data indexing processes.
4) The traditional “keyword” search model is no longer enough. It is the time of semantic search and artificial intelligence.
Traditional search engines base their technology on “keyword” searches. This way of searching for information has been overtaken by the ability offered by multi-language semantic search that is able to search for information between files of any format and language, and give as results, specific paragraphs and / or specific minutes of a video where you are talking about what you are looking for.
With a low-code solution, all these search features can be integrated into any application in minutes.
If you are interested in learning more about how to integrate a semantic search into your application, we encourage you to visit Nuclia, and feel free to contact us if you have any questions or comments.
