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While the promise of AI has been around for years, there’s been a resurgence thanks to breakthroughs across reusable large language models (LLMs), more accessible machine learning models, more data than ever, and more powerful GPU capabilities. This has sparked organizations to accelerate their AI investments, as the potential impact to our economy is in the trillions of dollars.
However, most organizations have struggled to implement a successful AI strategy. The 2023 global trends in AI report by S&P that surveyed over 1,500 AI decision-makers unsurprisingly highlights that one of the biggest barriers to AI innovation is access to clean and trustworthy data. The truth is while you can have a great AI/ML model, these models cannot stand alone. If the data powering these models is not high-quality, reliable, or fresh, there isn’t much value that will come out of these models. Connecting AI models to enterprise data in real time has been one of the most challenging problems data-dependent teams have been trying to solve. Addressing this challenge has become even more pressing with the emergence of GenAI.
For businesses to succeed in this new era of AI, they have to avoid the challenges that legacy data-at-rest architectures impose and build the modern AI stack on a foundation of data in motion. Today, we are thrilled to announce Confluent’s Data Streaming for AI initiative. We’re expanding our ecosystem to include critical AI technologies and committing to a roadmap of product enhancements to help you build AI apps faster. This includes plans for fully managed connectors for the modern AI stack, GenAI API calls built into Apache Flink, and an AI Assistant to help you build faster on Confluent. And this is just the beginning. With this new initiative, we are committing to making Confluent and data streaming a foundational piece of the modern AI stack by solving your AI data problems.
Imagine a world where your AI applications can make instantaneous decisions based on the freshest, most relevant data. Now snap back to your current enterprise reality: a labyrinth of data silos and varying cloud services, connected by a spaghetti-like mess of point-to-point integrations. This makes it a formidable task to actualize the seamless real-time connections that AI applications need for timely and accurate responses.
The root of the issue lies in your outdated data integration methods, which are built on slow, batch-based pipelines. These cumbersome systems take far too long to deliver data, rendering it stale and inconsistent by the time it arrives to feed your AI applications. Compounding these data problems are issues with poor governance and scalability.
The reality is your AI strategy is deeply interrelated to your data strategy. Outdated data infrastructure and integration methods aren’t just a technical hurdle; they can be a roadblock to AI innovation. If you don’t solve the foundational data infrastructure challenges for real-time AI, they will stifle developer agility and put the brakes on the pace of AI advancement. Until you tackle this challenge, your AI capabilities will remain constrained, always waiting for outdated data that has lost relevance.
Many of our customers have already been using Confluent for real-time AI and machine learning across multiple use cases, including predictive fraud detection, generative AI travel assistants, and personalized recommendations. There are a few key reasons enterprises are turning to Confluent’s data streaming platform for AI, including:
With Confluent’s immutable and event-driven architecture, enterprises can consolidate their operational and analytical data from disparate sources to construct a unified source of real-time truth of all their data. This empowers your teams to excel in model building and training, driving unparalleled levels of sophistication and accuracy across a range of applications.
Native stream processing with Confluent enables organizations to transform and optimize the treatment of raw data, at the time of generation, and turn them into actionable insights using real-time enrichment, while also dynamically updating your vector databases to meet the specific needs of your GenAI applications.
Confluent’s decoupled architecture eliminates point-to-point connections and communication bottlenecks, making it easy for one or more downstream consumers to read the most-up-to date version of exactly the data they want, when they want it. Decoupling your data science tools and production AI applications facilitates a more streamlined approach to testing and building processes, easing the path of innovation as new AI applications and models become accessible.
Equip your teams with transparent insights into data origin, flow, transformations, and utilization with robust data lineage, quality, and traceability measures. This fosters a climate of trust and security essential for responsible AI deployment.
Let’s look at the end-to-end flow in greater detail and dive into some of Confluent’s specific capabilities around powering AI.
In recent years, we’ve seen an explosion among enterprises of modular application development and popular adoption of best-of-breed tools. This results in data that is critical to AI being stored across many islands of disconnected systems and applications. With 120+ pre-built connectors, and Confluent’s multicloud and hybrid platform, we can break down any and all of your data silos so you get the freshest, most relevant data for your AI applications.
Over the next several months, Confluent will announce a series of updates to its newly announced Flink service for Confluent Cloud that bring AI capabilities into Flink SQL. Flink will be able to make OpenAI API calls directly within Flink SQL. This can be used for countless possibilities, like rating the sentiment of product reviews or summarizing vendors’ item descriptions. It will help alleviate the complexities of stream processing, accelerating time to insight.
Alongside our long-standing strategic partner MongoDB, Confluent is partnering with Pinecone, Rockset, Weaviate, and Zilliz to provide real-time contextual data from anywhere for their vector search capabilities. This plays a key role in retrieval-augmented generation (RAG). Vector databases are especially important as they can store, index, and augment large data sets in formats that AI technologies like LLMs require. Through these native integrations, you can access Confluent Cloud’s governed, fully managed data streams directly within the tool of your choice, making it even easier to use real-time data for AI-powered applications. This is just the start as we extend our partnerships in the AI space with the Connect with Confluent program.
Confluent is building on its relationships with Amazon Web Services (AWS), Google Cloud, and Microsoft Azure to develop integrations, reference architectures, and go-to-market efforts specifically around AI. For example, Confluent plans to leverage Google Cloud’s generative AI capabilities to improve business insights and operational efficiencies for retail and financial services customers. Confluent is also working with AWS on a generative AI solution that helps streamline the business loan process with real-time, streaming data. This will help more companies scale their AI initiatives in the cloud. This will help more companies scale their AI initiatives in the cloud.
At Confluent, we constantly strive to make it easy for our customers to use our platform. Our award-winning Kora architecture delivers unparalleled elasticity, resiliency, and performance, freeing up valuable engineering time from managing pipelines to focusing on building innovative applications faster and delivering incredible cost savings. Independent research by Forrester details how Confluent’s customers have saved $2.5M and achieved an ROI of 257%.
In the spirit of continuous innovation and improvement, we’re also implementing GenAI capabilities within our own platform. To help teams get contextual answers they need to speed up engineering innovations on Confluent quicker, the Confluent AI Assistant (in private preview) turns natural language inputs like “show me my most unused resources” or “give me a cURL request to list all service accounts” into helpful suggestions and accurate code that’s enriched with context about a specific user’s account. The AI assistant—driven by combining publicly available information such as Confluent documentation with customer-specific implementation details—also answers general questions about Confluent by tapping into its extensive docs pages to answer questions like “what’s the difference between Basic and Enterprise clusters?” This capability, natively built into the product, is coming soon to Confluent Cloud customers.
Confluent is launching POC-ready architectures with Allata and iLink that span Confluent’s technology and cloud partners to offer tailored solutions for vertical use cases. Developing, testing, deploying, and tuning these AI applications requires a specific skill set. These SI partners deliver that to alleviate the guesswork around building real-time AI applications, drastically speeding up time to value.
Allata developed a data mesh accelerator framework in order to build an AI-enabled tool and equip their sales team with the real-time information they need.
iLink built an intelligent chat application to help airlines improve customer service and streamline communication.
This is just the start of the data streaming and AI journey. More products and partnerships will come in the coming quarters as we look to give you all the freshest contextual data from everywhere for your AI applications.
While Data Streaming for AI will be an ongoing initiative for Confluent, we want to hear from you all on what you all need to build. Below are three ways you can get started.
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