Natural Solution of Enterprise Search Problem - Give up your RAGe to get ZeroShot AI

NaturalText AI offers a natural way of solving Enterprise Search Problem. With the bonus of getting to know hidden info from human sight. 

This is a continuation from the previous part. Take a look at the previous part. 

Relevancy and Ranking are two metrics basis of the Enterprise Search solution. Given a user query/prompt, what information needs to be returned, how to sort the information. Simple as it seems to be, however, even arriving at a metric or benchmark for evaluating the result itself is complex. In other words, the solution itself is complex, also benchmarking the system itself is complex. 

Let us see how the relevancy and ranking is decided by solutions available now. There are broadly three available solutions and how those solutions calculate ranking and relevance.  

First, Private Enterprise Search for Solr/ElasticSearch uses word count based metrics to find relevancy and ranking. 

Secondly, Public Web Search commonly known as search engines, which uses connections between publicly available documents in this case websites, to find ranking and relevance. 

Third, the new fashion of Retrieval Augmented Generation (RAG) with Large Language Models (LLM), with using public data word counts/probabilities to rank private document results. 

The pattern here is that just words from the documents aren't enough to decide how relevant the document is or just how important it is. There should be another parameter that can give us perfect ranking and relevance. 

How about creating links between documents, as in the case with search engines, via semantic conceptual connections? This will mimic the behaviour of public web search where links between the documents decide the result. Next question would be how to create this and how to create this at scale? 

First let us see what this link meant to be? Let us say, there are three documents, each containing the same kind of information, say text about sports. Task is to know what are the common concepts between these documents. Say one talks about baseball, another talks about rugby, third about basketball. So what could be common themes or concepts, even common words among this? It could be cities where it hosted, how the people travelled or what food the sportsperson enjoyed. Even though it talks about different sports there can be some commonality in between. 

Let us say, the first document has concepts that are found in the other two. The second document and third share different concepts which are not in the first one. Here now, we have a map that, the first document connects two, second and third connected. Once you add how many concepts are shared, then you have rank or weight for that connection. 

Second problem is how to find the correct and relevant information for the given query? If we go by the bag of words or just count of words, the words could be distributed across the document which won't give us relevant information. For that, we need to find the information via grammar of language and document structure. First step could be if the query can be found in sentences, second would be that in the same paragraph, third would be is it adjacent so on and so forth. This is not a distance calculation but a contextual mapping of concepts rather than just looking at the words. 

With these two now, both the ranking and relevancy problem is solved. Connections between documents with number of connections marking the weights and how contextually the query/prompt found in the documents solves search problem. Next question would be, is that enough for businesses? Would all of the business needs require search or something more? Would businesses need to explore the data, find something hidden in the data or something unknown? Another need would be how the businesses wanted to know how the concepts itself are connected. i.e. exploring or searching concepts instead of documents. 

Knowledge graph or variations of it solves the entity explorations problem. Knowledge graph needs entities to be created or extracted along with its relationships. That is called as ontology, creating it can be done via AI or any Natural Language Processing tool specifically created for it. However, searching in concepts is a wholly new idea. 

NaturalText Solutions for Enterprise Search. 

NaturalText offers a complete and full solution for Enterprises on their information retrieval and search problem. 

Most of the enterprises may have some sort of internal search with the majority of the platforms based on open-source softwares as Solr or ElasticSearch. It would be cost effective as the new solution would enhance the existing setup instead of trying to write a new one. NaturalText solutions keep that in mind. 

First solution is to create Document Popularity metrics for every document on the dataset. This popularity metric created by analysing all the data in every document, NaturalText AI reads the documents as a human expert would read, finding the concepts to link the documents with each other. This machine readable result can be plugged into existing systems. 

Second would be Zero-shot creation of Concept Popularity metric based on user specified criteria. This includes chunking or creating tokens based on a sentence, group of sentences or paragraphs. NaturalText AI takes all these chunks to create concept maps. This creates both similar concept groups and ranking for those concept groups. With this data, users can explore the concepts based on ranking or search the concepts for required prompt. 

Third solution is creating Ontologies from data. NaturalText AI offers an easy way of finding and extracting keywords and phrases along with relationships. Variations of a relationship also created using this method. As relationships between entities can be complex and multi-layered. Ontologies can be chained to create a full picture of a topic or multiple topics. If the company required it could only be in the form of triplets as triplet databases are already adopted by companies, resulting ontologies can be split and arranged to be stored in triplet stores. 

Fourth solution is NaturalText Knowledge Graph using NaturalText database and Reasoning from Graphs. NaturalText offers its own highly optimized graph database along with an efficient algorithm for reasoning. Reasoning based on graph algorithms is completely different from LLM based reasoning. The reasoning can be verified using the data or steps instead of just trusting LLM. Graph reasoning is not bound by the datasize. Even with a small database it can generate out-of-distribution insights. 

Finally, all in combine One Complete On-premises Solution for the customers, including a distributed load balancing setup for handling large databases. This idea of combining local and central methods of analysis offers companies the best of both worlds, companies can run their local instances for testing or development purposes and host a cloud based solution for both internal use and for their customers. 

Benefits of NaturalText Zero-Shot AI for Enterprises

Trust : NatuarlText offers unbiased explainable results which can be verified by either human viewing or a seperate programmatic analysis. i.e. results are both human and machine readable. This allows Enterprises to offer solutions with confidence as any errors can be traced back and analysed. 

Safety : NaturalText separates spam, NSFW and other problematic contents from data, so that users can see the correct and relevant information. From finding fake content to removing harmful or toxic content, NaturalText AI offers a safe and good way of conducting business and extracting value from the data. 

Security : Open results can be stored in existing databases, data stays with companies and there is no vendor lock-in. Companies can use results from NaturalText in their own operations directly, thus offering tight integration with existing systems. This enhances the existing systems and workflows making no additional cost on securing systems. 

Integration with existing Systems : Integrating NaturalText AI into existing systems, infrastructure, and software stack is easier. With open and readable results, just process the results into required format or put in a preferred database to be consumed later. 

Easy and Efficient : NaturalText is a symbolic non-statistical AI which works in CPUs as it uses efficient data representation and being a Zero-Shot or on-the-fly AI, there is no training, labelling or any other cumbersome work. Just feed the data, NaturalText AI will do wonders. 

For the Enterprises looking for using their data, efficiently, contact info@naturaltext.com