Leveraging enterprise-grade generative AI in cognitive search-Saxon AI
AI and ML / June, 05 2023

Leveraging enterprise-grade Generative AI in cognitive search

Microsoft has integrated ChatGPT with Bing for an enhanced search experience. Can you also build a ChatGPT-like search engine for your business so your employees can interact with your data? Yes, you can, by integrating enterprise-grade generative AI with cognitive search. Before exploring how, for the uninitiated, let’s discuss cognitive search and the challenges it solves for businesses. 

What is cognitive search? 

Enterprise data is vast. Studies estimated enterprise data to be over two petabytes in 2022, with an average annual growth of 42 percent. This data is historical and real-time, coming from consumers, employees, and machines. It is structured and unstructured, across systems located in the cloud or on-premises. Enterprises are producing more and more data.  

Data sitting idly in the servers doesn’t serve its purpose. Data must be accessible for you at the right time. However, traditional search mechanisms fall flat here. They face several challenges in bringing relevant and reliable information faster for users. They have several limitations, such as keyword dependency, limited semantic understanding, inability to handle unstructured data, scalability, etc. 

If you explore two-decade-old information, how good can a traditional search tool be? 

NHS Foundation Trust also had a similar situation. They wanted to leverage their 23 years of enterprise data, part of which was in handwritten notes. For a traditional search engine to read the unstructured data, they would have to use optical character recognition (OCR) to make it structured. That’s an overhead. Also, they have millions of documents through which searching for information using traditional methods is not reliable. But for the trust, 23-year-old data had a lot to offer. 

NHS Foundation Trust’s challenge was not unusual. Most companies face challenges like this. Traditional search doesn’t work well when the data is huge, old, and unstructured. This is where cognitive search comes into the picture. 

Cognitive search uses artificial intelligence technologies, such as natural language processing, optical character recognition, and machine learning, to provide more relevant and personalized search results. Cognitive search tools can index structured and unstructured data, enabling you to leverage your data completely. 

NHS Foundation Trust too turned to cognitive search. They used Azure Cognitive Search, which enabled their employees to pull handwritten PDFs effortlessly. They didn’t have to convert the unstructured data into structured data as Azure searched everything everywhere and gave them more relevant information. That’s the beauty of cognitive search. Let’s understand a few of the many limitations of traditional search tools that cognitive search overcomes. 

Keyword dependency

Traditional search tools rely heavily on keyword matching, which can lead to limited precision. You may still miss relevant information if the search terms do not exactly match the keywords in the indexed content or if synonyms are used in the search phrases. On the other hand, cognitive search tools leverage semantic search capabilities, natural language processing, and contextual learning capabilities to go beyond keyword relevance and provide more appropriate results.  

Lack of contextual understanding

When you discuss last quarter’s performance with your colleague, the conversation naturally flows. You won’t underline ‘last quarter’s performance’ in every dialog. The conversation flows naturally. Traditional search tools cannot come anywhere close to human interaction. They lack contextual understanding. If you simply ask, ‘what’s the price?’, it won’t understand. You have to provide complete context for each query.  

Cognitive search leverages natural language processing and semantic search techniques to understand the intent behind your query. It can interpret synonyms and linguistic variations and are late a query to the previous one to provide more accurate and contextually relevant results.

Information overload

With the influx of data, traditional search tools may struggle to efficiently index, retrieve, and present relevant results from large and diverse data sources. You may be overwhelmed with numerous search results, making it challenging to find the most valuable information. It might feel like searching for a needle in a haystack. Cognitive search tackles this challenge by providing intelligent filtering and advanced faceted search capabilities. It categorizes and tags content automatically, allowing users to refine their search results based on various criteria, such as date, location, author, or content type. 

Inability to handle unstructured data

In enterprise ecosystems, answers to your queries can lie in structured and unstructured data. However, traditional search tools are less effective in handling unstructured data, such as text documents, emails, or social media posts. They may not be able to extract meaningful insights, understand relationships, or interpret the nuances of unstructured textual information. So, you only get results from structured data, leading to incomplete answers and poor decisions. Cognitive search effectively handles unstructured data using entity recognition, sentiment analysis, and document clustering. It can extract valuable insights from unstructured content and present them in a structured and searchable format. 

New-age cognitive search with enterprise-grade Generative AI 

Cognitive search overcomes the limitations of traditional enterprise search and enhances the search experience with more relevant, personalized results. However, the incumbent cognitive search lacks generative AI capabilities. Generative AI makes life easier for users. With existing cognitive search models, you can fetch results from structured and unstructured data sources. However, you may have to go through multiple results to get the right answer to your question. This is time-consuming. In some cases, it is also ineffective. 

On the other hand, cognitive search with Generative AI goes one step further, generating relevant answers to your queries. The AI tool goes through all the available data, structured and unstructured, and generates the most relevant answers in your preferred language.  

For example, if you are using Azure Cognitive Search, you can integrate ChatGPT with the cognitive search using Azure OpenAI Service. This combination enables you to interact with your enterprise data, from across data sources, in natural language. Just like we have ChatGPT in Bing, you can have ChatGPT in your enterprise search, acting on your own enterprise data. 

Source: Microsoft

How will Cognitive Search with Generative AI revolutionize enterprise data? 

Introducing generative AI capabilities into cognitive search will address a few challenges in the incumbent model. Here are a few

Get answers, not references

The incumbent cognitive search brings all the relevant data files in the results page. Although the results are more relevant than in traditional enterprise search, you have to go through the files to find out answers. There’s still room for improvement in this case. Generative AI provides you with results, not just references. It will read through all the data files and summarize the data to give answers. This will also provide citations to support its answers. So, you can have more reliable answers. 

Break language barriers

Cognitive search can crawl and index humongous volumes of data and interpret it. The large language models (LLM) used in the Open AI service help businesses break language barriers. Users can search for information in any language and cognitive search can respond to the search in the same language, although information in that language is not available.  

Enhance search

Incumbent cognitive search learns the context. However, this learning is based on machine learning algorithms that you cannot change. However, in the generative AI-powered cognitive search, you can always update the prompt patterns. So, the search engine learns more effectively and provides more relevant information.

With generative AI-powered search, your employees can access information more easily and make better decisions. You can incorporate this modern cognitive search into your customer support pages so that your customers can find solutions to their problems much faster, enhancing customer experience. Thus, enterprise-grade generative AI will help you unlock the full potential of your enterprise data.  

Want to learn how you can use generative AI for your business? 

At Saxon, we help businesses explore new AI possibilities and drive growth through our InnovAIte program. Under this program, our experts will help you at each phase of innovation – ideation, prototyping, testing, and iterating, to create the most affordable AI solutions for your business.

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