Navigating knowledge bases efficiently: The power of Gen AI and Snowflake Cortex AI

Navigating knowledge bases efficiently: The power of Gen AI and Snowflake Cortex AI

Dawid Benski

7th October 2024

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Most companies that rely heavily on document stores for knowledge sharing and team collaboration often end up with many pages created by users.

The rapid growth and constant evolution of these knowledge bases pose significant challenges in finding relevant content. Despite diligent documentation, navigating to the pertinent information remains difficult - one either knows where the document is or the exact keywords to find it.

Real customer scenario

At one of Capgemini's clients, a team operating and building a new data platform was entangled in customer support, reducing its ability to create new functionalities.

Allow me to briefly explain what a typical support request entailed:

1. The customer raises a question to the platform team.
2. A dedicated person from the platform team browses available Wiki documentation and searches for relevant information.
3. Several minutes or even hours later, the person passes the information (a link) to the requestor.
4. The answer may not be clear, prompting the need for another question to be asked.

The weekly effort spent on customer support is increasing, and it is projected to reach 2.5 "FTE" permanently occupied with customer support activity by the end of 2024, as the number of platform users grows. Moreover, the response time for support requests is too long, leading to a poor customer experience.

Talk to your data with Gen AI

The client uses several cloud technologies, including Snowflake, as the core database and data warehouse solution. Capgemini experts were quick to consider Snowflake Cortex AI technology as the key to creating a cutting-edge solution for tomorrow, addressing the client's issues with operational costs.

Why not ramp down on operational costs and ramp up customer interactions to a new level like this?

1. Go straight to the chatbot and ask the question.
2. Still have a question? Ask another question.
3. Is the chatbot not able to answer your question? Contact a relevant person from the platform team.

With this vision in mind, Capgemini set out to implement a Gen AI-based chatbot that could answer customer questions efficiently. The chatbot, powered by the company's extensive knowledge repositories, ensured that the provided answers were accurate and relevant. Additionally, the chatbot referenced the source Wiki documentation link as part of its responses, making it easier for users to find the information they needed.

The solution worked 24/7, ensuring that customers could get help at any time of the day or night. This innovative approach aimed to reduce the burden on the customer support team and enhance the overall customer experience. By leveraging the power of Cortex AI and Retrieval-augmented generation "RAG"-based Gen AI, Capgemini was poised to revolutionize how customer support was handled, paving the way for a more efficient future.

High-level architecture

The RAG architecture Capgemini proposed for the Cortex AI chatbot consisted of three service types:

• Cortex AIFunctions for large language model (LLM) support: EMBED_TEXT_768, VECTOR_L2_DISTANCE, and COMPLETE)
• Snowpark Container Services for retrieval front-end.
• Snowflake tables as a vector store (native support of vectors as data types in Snowflake).

Let me explain some basic terms:

• RAG is an architectural approach that enhances the capabilities of large language models by incorporating an information retrieval system. This system retrieves relevant data or documents and provides them as context for the LLM, improving the accuracy and relevance of the generated responses.
• SnowflakeCortex AI is an intelligent, fully managed service within Snowflake that allows businesses to leverage the power of artificial intelligence (AI) that enables users to quickly analyze data and build AI applications without the need for extensive technical expertise.
• Snowflake Cortex AI Functions are a set of pre-built LLM functions that allow users to perform advanced data analysis and AI tasks directly within the Snowflake platform. These functions include capabilities such as text completion, sentiment analysis, and text summarization.
• Snowflake Container Services is a fully managed container offering that allows users to deploy, manage, and scale containerized applications within the Snowflake data platform.

By implementing a Gen AI chatbot based on Snowflake Cortex AI technology, evaluated by Capgemini, the client can streamline the customer support processes, reduce operational costs, and enhance customer interactions. This innovative solution leverages the power of AI to provide accurate and timely answers, ensuring that users can easily navigate through vast knowledge bases and find the information they need.

Cortex Search

I described the way Capgemini built a search tool for the client's use case. The latest introduction of Cortex Search replaces the need for standalone vector tables and a self-managed embedding process with a fully managed RAG engine. This advancement not only streamlines development but also elevates the quality of outcomes with sophisticated retrieval and ranking techniques that merge semantic and lexical search. This effective approach is undoubtedly a game changer in building Gen AI RAG-based solutions.

Capgemini and Snowflake

The collaboration between Capgemini and Snowflake leverages Snowflake's AI data cloud to enable businesses to unify and connect to a single copy of all data with ease. This partnership allows for the creation of collaborative data ecosystems, where businesses can effortlessly share and consume shared data and data services.

Capgemini and Snowflake are collaborating to develop generative AI solutions that leverage Snowflake's advanced AI Data Cloud technology to drive innovation and enhance business outcomes across various industries.

This strategic relationship has led to Snowflake naming Capgemini the 2023 EMEA Global SI Partner of the Year.

Delivery Architect Director, Capgemini

Dawid is a delivery architect who is focused on Big Data and Cloud, mainly working in sectors like Telco and Automotive. Experienced working directly with customers as well as remote team management, both in Germany and India.