New initiative will expand Confluent’s partnerships and product capabilities to help companies build real-time AI innovations with data streams

Confluent, Inc., the data streaming pioneer, announced Data Streaming for AI, an initiative to accelerate organizations’ development of real-time AI applications. Achieving real-time AI demands more than fast algorithms; it requires trustworthy, relevant data served in the moment for smarter, faster insights. To help companies unlock the full potential of AI with the freshest contextual data from across their business, Confluent is expanding partnerships with leading companies in the AI and vector database space, including MongoDB, Pinecone, Rockset, Weaviate, and Zilliz. It is also demonstrating product innovations that incorporate the latest advances in AI into its platform, with capabilities like a generative AI-powered assistant that helps generate code and answer questions about the data streaming environment.

“Although there’s significant growth in the number of companies experimenting with generative AI, many face roadblocks from a fractured data infrastructure that lacks real-time data availability and trust”

“Data streaming is foundational technology for the future of AI,” said Jay Kreps, CEO and Cofounder, Confluent. “Continuously enriched, trustworthy data streams are key to building next-gen AI applications that are accurate and have the rich, real-time context modern use cases demand. We want to make it easier for every company to build powerful AI applications and are leveraging our expansive ecosystem of partners and data streaming expertise to help achieve that.”

While the promise of AI has been around for years, there’s been a resurgence thanks to breakthroughs across reusable large language models (LLM), more accessible machine learning models, and more powerful GPU capabilities. This has sparked organizations to accelerate their AI investments. However, a fundamental challenge in modern AI is a lack of access to the relevant, real-time data that AI applications need in a timely, secure, and scalable way.

For the past decade, AI heavily relied on historical data, integrated with slow, batch-based point-to-point pipelines that rendered data stale and inconsistent by the time it arrived. That’s no longer adequate for the real-time AI use cases today’s businesses are trying to launch, like predictive fraud detection, generative AI travel assistants, or personalized recommendations. Compounding the problem are issues with poor data governance and scalability. As a result, the pace of AI advancements is stifled as developers are constantly tackling issues with out-of-date results and untrustworthy AI hallucinations. This isn’t just a technical hurdle; it’s a roadblock to AI innovation.

“Although there’s significant growth in the number of companies experimenting with generative AI, many face roadblocks from a fractured data infrastructure that lacks real-time data availability and trust,” said Stewart Bond, Vice President, Data Intelligence and Integration Software, IDC. “Data management is the most important area of investment as organizations build an intelligence architecture that delivers insights at scale, supports collective learning, and fosters a data culture. Those that get it right have seen a 4x improvement in business outcomes by removing real-time data availability and trust roadblocks through data streaming, governance, security, and integration–so it’s worth the journey.”

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Build a real-time data foundation for modern AI with Confluent

Modern AI applications require multiple technologies and data from numerous domains to seamlessly come together. The Confluent Data Streaming for AI initiative aims to help organizations quickly build and scale next-generation AI applications with a shared source of real-time truth for all operational and analytical data, no matter where it lives.

“We built an AI platform designed for mission-critical use cases—in industries where businesses simply can’t afford to be wrong,” said David Ferrucci, Founder and CEO, Elemental Cognition. “Our interactive reasoning engine leverages a continuous supply of data that’s reliable, trustworthy and current. Confluent’s data streaming platform makes that possible and enables us to mix and match data from different systems and optimize it for contextually rich insights and answers in real time.”

As part of this launch, Confluent has extended partnerships in the AI space and also committed to delivering more AI capabilities within its own platform to further ease the development of real-time applications.

Expanded partnerships

• Connect with Confluent Technology Partners: Confluent is partnering with MongoDB, Pinecone, Rockset, Weaviate, and Zilliz to provide real-time contextual data from anywhere for their vector databases. 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 integrations, Confluent Cloud’s fully managed data streams can now be accessed directly within these partners’ platforms, making it even easier to use real-time data for AI-powered applications. This is just the start as Confluent looks to extend its partnerships in the AI space with the Connect with Confluent program.
• Public Cloud Partners: Confluent is building on its strategic partnership agreements with Google Cloud and Microsoft Azure to develop integrations, proof of concepts (POCs), 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. And, with Azure Open AI and Azure Data Platform, Confluent is planning to create a Copilot Solution Template that enables AI assistants to perform business transactions and provide real-time updates, benefiting industries such as airlines and transportation.
• Services Partners: Confluent is launching POC-ready architectures with Allata and iLink 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 partners deliver that to alleviate the guesswork around building real-time AI applications, drastically speeding up time to value.

Product innovations

• Confluent AI Assistant: To help teams get contextual answers they need to speed up engineering innovations on Confluent, the Confluent AI Assistant turns natural language inputs—like “What was my most expensive environment last month?” or “Give me an API request to produce messages to my orders topic”—into helpful suggestions and accurate code that’s specific to their deployment. This is made possible by combining publicly available information, such as Confluent documentation, with contextual customer information to provide specific, timely responses. It will be available to Confluent Cloud customers in 2024.
• AI for Apache Flink^® SQL: 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. On the main stage of Current, the data streaming industry event, Confluent will demo how Flink can make OpenAI API calls directly within Flink SQL. This unlocks endless use cases, such as rating the sentiment of product reviews or summarizing vendors’ item descriptions. It will help alleviate the complexities of stream processing, accelerating time to insight.

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