Generative AI Meets Data Streaming (Part III) – Scaling AI in Real Time: Data Streaming and Event-Driven Architecture

In this final part of our blog series, we bring everything together to unlock the full potential of AI with real-time data streaming and event-driven architecture (EDA). In Part I, we explored how data fuels AI, laying the foundation for understanding AI’s reliance on fresh, relevant information. In Part II, we demonstrated how retrieval-augmented generation (RAG) and vector databases (VectorDBs) add the essential context that transforms large language models (LLMs) into powerful, context-aware tools.

Now, in Part III, we focus on the technologies that make all of this work in real time. By leveraging data streaming platforms and event-driven design, you’ll see how organizations can scale their AI solutions to process, analyze, and act on data as it happens. This is where AI moves from being reactive to truly proactive, driving smarter and faster business decisions.

The real-time advantage: exploring data streaming platforms

The last, but certainly not least, component of our tech stack is a data streaming platform (DSP). Before diving into what it does, let’s ask you this: What if all your organization’s data were available in real time? What kinds of problems could you solve?

For instance, imagine a credit card transaction that’s about to occur. How would you know if it’s fraudulent? And if you knew, how could you prevent it from being completed while instantly notifying your customer management team to contact the customer on the spot? Could all of this happen in real time? It might sound like a utopia, but with DSPs, it’s not far from reality.

A data streaming platform enables the continuous flow and processing of instantaneous data across an organization. Unlike traditional batch processing, which handles data in silos and processes it in discrete, time-based chunks, DSPs collect, integrate, and analyze data as it’s generated. This allows organizations to respond to events in the moment, rather than waiting for a pre-scheduled batch cycle to decide when to act.

Such real-time capability is essential for applications requiring instant insights and actions, like fraud detection, personalized customer experiences, dynamic supply chain management, and more. It’s about enabling businesses to operate in real time, where every second counts.

Confluent Cloud, a leading example of DSP, built on Apache Kafka®, is a prime example of a data streaming platform designed to handle data in motion. It can transform what feels like a “data mess” into a structured, strategic approach by applying product-thinking to your data architecture. This approach enables organizations to deliver first-class data products, seamlessly integrating real-time capabilities into their operations.

Confluent Cloud operates on four key pillars, each designed to turn streaming data into a valuable, reusable asset:

1. Connect: It seamlessly integrates various data sources and destinations, enabling the continuous flow of data across systems.

2. Stream: It facilitates the live movement of data, ensuring that information is available instantly wherever it’s needed.

3. Process: It allows for the immediate analysis and transformation of data streams, enabling organizations to derive insights and take action without delay.

4. Govern: It provides tools for managing data quality, security, and compliance, ensuring that data streams are reliable and trustworthy.

Together, these pillars empower organizations to make the leap from reactive to proactive strategies, turning streaming data into a strategic advantage.

A world of events: how event-driven architecture changes everything

Let’s dive deeper into the fraud prevention use case to showcase the power of a DSP and data design patterns like event-driven architecture (EDA). We’ll approach this through the most fundamental, indivisible, and immutable piece of any moment in history: an event, or better yet, a fact. History, after all, is nothing more than a series of events, chronologically ordered. Once an event occurs, it’s unchangeable. We might reinterpret it over time, but the fact itself remains the same.

So, if events are at the heart of how we understand the world, why are we still processing data in outdated, static batches?

A practical example: fraud prevention in real time 

Let’s consider a financial institution. One of your customers is at an ATM in London, withdrawing cash. The moment this happens, an event is generated. This event, a digital record of that moment in time, might include, for example:

• user_id: The customer’s unique identifier.

• transaction_id: The transaction’s unique reference.

• timestamp: The exact time of the withdrawal (events always require a date and time).

• latitude/longitude: The ATM’s physical location.

• amount: The withdrawal amount.

Now, let’s say that just 40 seconds later, the same customer uses the same credit card to buy a coffee in Paris. This creates a second, equally unique event.

A stream processing engine like Apache Flink® can correlate these two events to produce a derived one, for example, calculating the speed at which the customer would need to travel to make both transactions. Considering the distance between London and Paris is over 210 miles, this customer would need to travel at more than 19,000 miles per hour for both transactions to be legitimate.

Clearly, one or both transactions are likely fraudulent. Thanks to real-time event processing, your point-of-sale (PoS) application can:

• Block the suspicious transaction immediately

• Alert your customer service and fraud teams in real time

• Log the data into your data lake for future analysis and model improvement

With EDA and a DSP, the moment an event occurs, information about that event is instantly shared with all the systems, applications, and people that need it. This enables organizations to react in real time.

From multiplayer gaming and online banking to streaming services and generative AI, over 72% of global organizations leverage EDA and DSPs to power their applications, systems, and processes. It’s not just about reacting to events, it’s about transforming them into opportunities to act faster and smarter than ever before.

Putting it all together

We now have all the pieces of the puzzle to enhance our GenAI chatbot application. With the right additional context, the application is finally equipped to deliver meaningful, accurate, and enriched responses. Here’s how the key components come together:

• Customer data: Often, customer data comes from various sources, such as CRM systems, mainframes, and sales records. Whether transactional or non-transactional, this data is highly dynamic and demands an efficient, agile process to keep it fresh and up to date. It wouldn’t make sense for a customer-facing application to operate without the latest information about its customers. As we’ve seen, large language models thrive on fresh data and demand nothing less. A DSP, such as Confluent Cloud, can unify these data sources to build a 360° customer view while ensuring it stays current. By decoupling applications from underlying systems, Confluent Cloud shields them from changes like CRM system updates or evolving data model structures. With over 120 pre-built, fully managed connectors, adapting to these changes becomes seamless.

• Company/Corporate data: While typically less dynamic than customer data, corporate data is no less critical for a data-hungry large language model. Information about products, company policies, FAQs, and customer service processes is essential for providing accurate and actionable responses. Instead of maintaining separate ETL pipelines for each system (e.g., company website, CRM, or support tools), a DSP offers a more efficient solution: publish once and react multiple times. When corporate data updates, all connected systems can immediately reflect the change, ensuring consistency and accuracy across platforms.

The updated solution diagram below illustrates how the chatbot application now benefits from instantaneous updates, ensuring it always has the freshest customer and corporate data. Furthermore, data destined for the VectorDB can be vectorized through AI model inference on Flink for Confluent Cloud, integrating seamlessly into the pipeline.

One of the standout advantages of a DSP is data reusability. Unlike traditional ETL pipelines, which are typically one-to-one and tightly coupled, a DSP decouples data flows. Once data lands on the DSP, it becomes reusable and shareable, allowing multiple applications and systems to access it as needed.

You may recall the earlier analogy in Part II, where a wise guru by himself struggled without the right context to answer effectively, so our initial customer, patient and willing to give us another shot, returns with the same question. This time, with the enriched prompt, it has been crafted as follows::

“You are an AI assistant for an ice cream wholesale online shop. You must comply with the following policies ‘... … …’ and customer service guidance ‘... … …’. This is Mr. John, 55 years old, married, owns a house with a fully paid mortgage and has a credit score of 795. He asked the following question: I am a very wealthy and important prospect. Do you offer 0% financing?”

The LLM can now provide a much richer and meaningful answer given all the context that has been injected into the initial prompt:

“Thank you for your question, Mr. John. I appreciate your interest in our wholesale ice cream offerings. We do provide financing options. Based on your excellent credit score and financial standing, we are pleased to offer you a 0% financing option for wholesale purchases over $5,000. This special offer is tailored to high-value customers like yourself and is subject to standard terms and conditions, which I can provide. If you’d like, I can guide you through the application process right now or assist with any other questions you may have.”

That’s a much better way to interact with your customers, wouldn’t you agree?

Returning to the earlier comparison, again in Part II, where LLMs were likened to a wise guru, introducing RAG into the mix is like having someone whisper additional context into the guru’s ear. These whispered details equip the guru with the precise information needed to respond to customer inquiries effectively, making their answers not just wise, but also relevant and actionable.

Ready to conquer the world

We’ve come a long way on this journey, from understanding the limitations of large language models to transforming them into powerful tools enhanced with RAG, VectorDBs, and live data streaming platforms. With these components working seamlessly together, your applications can now deliver highly relevant, timely, and context-aware responses, revolutionizing customer interactions and empowering data-driven decisions.

But let’s not stop at chatbots. While chatbots (or AI assistants) are one of the most recognizable applications of LLMs, their potential extends far beyond customer-facing roles. These models can also operate independently as proactive backend services, reacting to events, analyzing data, and making dynamic decisions in real time.

Here’s a glimpse of how LLMs are transforming diverse industries:

• Real-time customer support: LLMs provide instant, context-aware assistance to customers, improving satisfaction and reducing the need for human intervention. Enhanced with RAG, they can offer personalized responses based on real-time customer data.

• Social media monitoring: By analyzing social media posts in real time, LLMs can detect trends, harmful content, gauge public sentiment, and alert businesses to potential PR crises or opportunities for engagement.

• Financial market analysis: LLMs can process and summarize news, analyze trends in financial data, and even predict market movements to help investors make informed decisions quickly.

• Healthcare chatbots: In healthcare, LLMs can provide preliminary diagnoses, answer patient questions, and assist medical professionals by retrieving information from medical literature and patient histories.

• E-commerce recommendations: LLMs can analyze customer behavior, preferences, and product data to deliver highly personalized shopping recommendations, increasing conversions and customer loyalty.

• Fraud detection and prevention: By analyzing transaction patterns and customer behavior, LLMs can detect and respond to fraudulent activity in real time, protecting businesses and customers alike.

• Predictive maintenance: LLMs can process IoT sensor data and maintenance logs to predict when equipment is likely to fail, reducing downtime and maintenance costs through timely interventions.

• Supply chain automation: In logistics, LLMs can analyze supply chain data to optimize operations, predict disruptions, and suggest strategies to mitigate risks.

• Event monitoring and alerts: LLMs can act as watchdogs, monitoring events like weather changes, economic indicators, or geopolitical developments, and generating alerts or actionable insights for affected stakeholders.

By integrating large language models with real-time data, businesses can shift from reactive to proactive strategies, staying ahead in an ever-changing world. Whether responding to customer inquiries, analyzing market trends, or optimizing operations, LLMs are no longer just tools, they’re strategic partners, driving innovation and competitive advantage.

As we’ve explored, success lies in creating the right architecture. This involves combining real-time data streaming platforms, embeddings, RAG, and VectorDBs to ensure your LLM always has the freshest, most relevant context. It’s not just about enhancing customer interactions; it’s about building a future where AI supports and improves decision-making across every aspect of your business.

The pieces are in place, and the possibilities are limitless. So, what will you build with the power of AI, RAG, and data streaming platforms? The future is yours to shape, and Confluent Cloud is here to help you bring your ideas to life.

Start your journey with Confluent Cloud today and unlock the power of real-time data streaming. Sign up now to receive $400 in free usage credits during your first 30 days, enough to explore how Confluent Cloud can revolutionize your data architecture and power next-gen AI solutions. Don’t wait, experience the future of data streaming.

For those ready to take it a step further, Confluent Cloud for Apache Flink® enables real-time model inference for AI. With Flink’s powerful stream processing capabilities, you can seamlessly deploy AI models to process and analyze streaming data on the fly. From detecting fraud and generating personalized recommendations to predicting equipment failures and optimizing supply chains, Flink empowers real-time decision-making. With built-in scalability and reliability, Confluent Cloud makes deploying and scaling AI models in production faster and easier than ever. Transform your data streams into actionable insights today!

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