In today’s technology-centric world, the hype surrounding generative AI is at an all-time high, and businesses from all corners of the globe are eagerly tapping into its potential. AI is showing up everywhere – it is improving customer service interactions, giving marketing strategies a fresh spin, and breathing new life into product development. 

Chat-GPT has swiftly become a household name, renowned for its remarkable versatility and jack-of-all-trades capabilities. Its emergence has unleashed a treasure trove of business advancement opportunities. Understandably, many businesses find themselves wondering, “How can we use GPT in our business?”. 

In addition to the well-recognised considerations of data security, impact on existing roles, and the need for training and upskilling, the accessibility and utilisation of critical business data has emerged as a significant challenge when evaluating the applicability of AI in a business context. Enabling AI systems to efficiently access and leverage such data is crucial for their successful integration. Although bringing your own business context manually can be explored as a potential solution, an automated method to integrate this data would not only enhance the capabilities of AI but also unlock new realms of potential for businesses, empowering them to make informed decisions and drive innovation. 

One option lies within the world of AI itself – in a feature called ’embeddings’.  

In this comprehensive guide, we delve into what embeddings are, why they are crucial for AI, and how they are revolutionising business data interpretation. 

Demystifying Embeddings 

At its core, embeddings transform discrete categorical variables into a vector of numbers that exist in a multi-dimensional space. In plain language, embeddings help us convert a piece of information, such as a business document, into something that packs more punch than just a dictionary definition of its contents. They wrap up the context, the semantic ties, the implications, and even the subtleties of language into a neat little package. 

                  Source: https://openai.com/blog/introducing-text-and-code-embeddings  

Let us break it down with an example. Consider the words “puppy” and “kitten.” At face value, these words are different and distinct, with no shared letters and different lengths. Yet if we peek behind the curtain, they are both cuddly animals, both have four legs, and so on. In the multi-dimensional space, and certainly in human conversations, they are often associated and relevant to each-other. 

Embeddings: The Secret Sauce in AI 

The magic of embeddings is that it is not limited to individual words. The same technique can work its charm on other complex entities – the difference between two nuanced perspectives, customer values that drive buying behaviour or what combination of internal commercial, delivery and company DNA content would create the most valuable answer for a business outcome.  

Some other use cases: 

• In medical diagnostics, embeddings can play the role of a digital detective, identifying diseases based on symptoms or medical history. 
• In the world of e-commerce, embeddings power recommendation systems that serve up product suggestions to customers based on their browsing or purchase history. 
• In the social media landscape, embeddings can help decode user behaviour, fuel content recommendation engines, or even spot hate speech or fake news.

In a data-rich world, embeddings act as navigational tools to identify patterns and glean valuable insights from large datasets. They are instrumental in modern AI systems, driving everything from personalised movie recommendations to a virtual assistant’s language understanding. 

Leveraging Embeddings for Context Retrieval in Business Data 

The essence of using embeddings lies in supplying your business’s unique context. By transforming your data into embeddings, you can snapshot your company’s information landscape. Embeddings help us extract relevant information from vast data pools, be it transaction history, product details, client records, or past business strategies. 

Consider an instance where an executive asks an AI system a question about a specific area of their business operations, such as “How have sales trends shifted following our recent marketing campaign?” Traditionally, this is handed off to an analyst to consider and source the relevant data, develop a query, and chart the results. What is driving a lot of today’s excitement around AI is the promise that any user can explore and think about their business in real time with no friction.   

Thanks to recent technology that Hamilton Robson has been able to provide, traditional analysis and computations can be performed without fear of AI hallucination and combined with embeddings we can better surface the relevant company data and find documents or records that closely match the context of the question. 

Integrating Context with GPT: Intelligent Responses to Complex Queries 

Now that we have the relevant context in hand, what is next? For some use cases, it’s more than enough to provide the best references and advise on direction for a desired output but with generative models like GPT showing consumers what content generation could look like, for some use cases drafting the answer is a valuable idea. This is where generative models like GPT come in. GPT is not explicitly trained on a company’s specific data, but it is designed to make sense of the context and generate intelligible responses based on that. Once the embeddings help us retrieve the right context for the question, supported by function calling for any necessary processing, we can feed the information to GPT to deliver the response. Then, GPT goes to work, generating an answer that factors in the specific context. In our sales trend example, it could be something like “Following the recent marketing campaign, sales have increased by 15%, particularly in the 18-24 age demographic. Let me know if you want me to explain my working out. Here’s a link to the internal source information.” By using embeddings, GPT can effectively understand and articulate responses about your business’s unique data. 

Shaping the Future of Business Intelligence  

AI with embeddings (and more broadly Cognitive Search) are redefining the future of business intelligence. This powerful duo deciphers vast business data and generates context-appropriate responses. Regardless of the business size or sector, embeddings empower companies to maximise their use of AI. 

As your business grows, your data evolves. With their capability to capture complex data patterns, embeddings help ensure your AI system remains adaptable and scalable. Be it a new market trend, product launch, or business strategy change, your AI system is always ready. 

Get in touch 

We understand that implementing such advanced AI systems can come with its own set of challenges. Ensuring data security, maintaining privacy standards, and overcoming the learning curve of adopting AI technology might be daunting for many businesses. If you are struggling with these issues, or simply want to make sure you are setting up your systems correctly from the start, do not hesitate to reach out to us. 

Our team of experts is equipped to guide you through these complexities, ensuring you can confidently implement and optimise your AI systems while adhering to best practices for data security and privacy. If you’re interested in exploring the potential for your business, we’d love to hear your thoughts. Fill out the form below to start a conversation.