Thanks to Snowflake Cortex AI, running a simple database query now allows me to achieve results that, just a few years ago, seemed possible only in science fiction movies.
Dear reader, you probably didn’t expect an answer to the headline question in the very first sentence of this article, did you? Well… I can only apologize for the spoiler. However, I wanted you to realize that instead of struggling with configuration, navigating between interfaces, or building additional dependencies, you can simply sit back and enjoy the results.
How Cortex AI Helps in Customer Opinion Analysis – Case Study
But how does it work in practice? Let me briefly describe a case I recently encountered. I had the opportunity to implement a Cortex AI-based solution that helped quickly contain a reputation crisis spreading across social media.
Imagine millions of customer reviews about your store’s products and services flowing into your cloud data warehouse every hour. Everything runs smoothly until suddenly, a grain of sand clogs the system. Negative reviews start pouring in at an increasing rate. At least, that’s what the social media team claims—and they know what’s happening online. How can you prevent a potential crisis? Simple: you need to find the root cause of customer dissatisfaction.
Fortunately, you have access to all recorded reviews in a structured format within Snowflake. We start by segmenting them into ten categories—ranging from the best to the worst. For this, we use two simple Cortex LLM functions: translate and sentiment. The first one translates the review text into English (let’s be honest, LLM models work best in this language), while the second determines the sentiment of each review on a scale from -1 to 1.
Pretty cool, right? With just a few queries, we can identify which stores had the most dissatisfied customers and determine that, for example, a small group of connected suppliers is responsible for delivering goods to those locations.
Identifying the cause is one thing, but what about solving the problem? Again, Cortex LLM comes to the rescue. The complete function allows us to define issue categories and save them in a separate table. Then, AI can generate, again using complete, a personalized email to the supplier, outlining the identified problems and suggesting solutions. And all of this is based on your own data and the power of LLMs available in Snowflake.
Security and Accessibility – Key Considerations
I hope this simple example sparks your imagination. However, there are two additional critical aspects to consider: data security and accessibility for non-technical users.
Regarding security, it is crucial that your data and its processing results are not shared with other customers, are not used to train external models, and are not provided to third parties. You maintain full control over your data at every stage of processing.
And what about users who are not familiar with SQL or analytical tools? A developer can simply use Streamlit to create a user-friendly application. With Python, they can build a simple program that visualizes customer satisfaction on a scale from 1 to 10 stars, displays the stores with the most negative reviews on a chart, or even automatically generates and sends an email to the supplier with suggested solutions. The user doesn’t even need to know they are interacting with a data warehouse—just a few clicks, and the program, powered by AI, does the rest.
This is just a small glimpse into the possibilities of Cortex AI and Snowflake’s functionalities. And the list of innovative features is long. It’s worth mentioning how easily you can implement chatbots that allow you to query both structured and unstructured data in natural language. Yes, you can even “talk” to your confidential reports stored in PDFs—securely! But I’ll save that for another time… Stay tuned!