Confluent Is Turning Seconds Into Dollars with Real-Time AI …

As enterprises across India chase the promise of generative AI, most continue to grapple with a critical gap in their AI ambitions and how fast they manage to move their data. Confluent’s chief product officer Shaun Clowes believes the answer lies in bringing real-time data in the foundation of production AI.

“Without real-time, rich data, there is no production AI,” Clowes said in an interaction with AIM. “You just cannot build production agents. And people are quickly realising that two and a half years into the AI surge, we still have very few production use cases because organisations can’t bring their data to bear in a safe, secure, and complete way.”

The company’s latest offering, Confluent Intelligence, is built precisely for that, bridging the gap between data and action.

Building AI Agents That Sense, Analyse, and Act

Clowes explained that most AI initiatives today are still trapped in the chatbot mindset, while the next wave of AI will be about agents that can sense, analyse, and act much like humans.

“Humans don’t just wait for someone to ask them a question. They look at the world, detect problems, and take action. That’s exactly what the new generation of AI agents must do,” he said.

Confluent is weaving together technologies like Apache Flink and its new Context Engine to make that possible. With Flink’s anomaly detection capabilities, systems can identify issues like a drop in network activity or a surge in food delivery demand. The Context Engine then feeds relevant, real-time data to AI agents so they can decide and act all in under 10 milliseconds.

“LLMs are both very smart and very dumb,” Clowes added. “They know nothing about the world except what you tell them. If you give them irrelevant data, results get worse; if you give them rich context, they perform beautifully.”

For example, in a telecom scenario with Jio, an AI agent could detect a cell tower outage, analyse the cause, and automatically trigger corrective actions from rebooting routers to dispatching technicians without human intervention. 

Similarly, Swiggy could use the same system to detect delayed orders and trigger automated compensations or notifications based on customer profiles.

Making the Shift from Batch to Real Time

While the benefits are evident, many Indian enterprises still operate in batch mode, processing data periodically rather than continuously. Rohit Vyas, director, solutions engineering, Confluent India, said that the company’s biggest challenge isn’t convincing customers, but simplifying the transition.

“It’s not about convincing them, it’s about making it easy,” said Clowes. “Any data that can be batch-moved can also be streamed. You can start with slow streams and go faster as value accumulates.”

Vyas added that Confluent’s approach combines thought leadership with consultative engagement.

“We often find that many customers confuse streaming with YouTube or Netflix,” Vyas said, laughing. “Data streaming means bringing data ingestion, governance, and processing as close to the time of data creation as possible. That’s what makes it truly real-time.”

He added that customers typically see an ROI boost between 25% and 70%, depending on use case maturity. “If we’ve reduced latency from days to minutes, that’s new revenue, faster cross-sells, and better customer experience,” Vyas said. 

Clowes noted that one large section of the Indian marketplace has even quantified the value of time, referring to the large, high-volume, real-time digital marketplaces in India.

“They can literally measure the return on seconds of data,” he said. “If they shave 30 seconds off their response time, they make an extra million dollars. It’s amazing how Indian companies have mastered monetising real-time.”

Private Cloud for the Regulated Enterprise

While most demand for Confluent’s platform is cloud-native, the company also sees traction among regulated sectors that want the flexibility of cloud in on-prem environments.

“Confluent Private Cloud is designed for customers in air-gapped or highly regulated environments,” Clowes explained. “It lets them run a full cloud-like data streaming platform in their own infrastructure.”

Vyas added that sectors like financial services, telecom, and the public sector are key adopters.

“Customers say, ‘I want to run on-prem, but with the simplicity and scalability of cloud.’ That’s exactly what Confluent Private Cloud offers,” he said.

The Next Frontier

Clowes highlighted the importance of pairing every AI workload with analytics to continuously measure, refine, and enhance outcomes. With TableFlow, Confluent seeks to simplify this process by allowing customers to analyse streaming data in real time as live tables using Iceberg or Delta formats.

“You can literally run queries in your data lake to see what your AI agents are doing,” Clowes said. “That’s a pretty compelling unification of data, analytics, and AI.”

Confluent has got some stiff competition. Amazon MSK is one of the main competitors of Confluent, offering a managed Kafka service that fits tightly with AWS and works well for teams already using Amazon’s cloud. 

Then there is Redpanda, giving users a Kafka-compatible system that aims to be faster, easier to run, and more affordable, especially for smaller businesses. 

A newer option, AutoMQ, takes a cloud-native approach and uses object storage to deal with Kafka’s cold-read issues, though it can add some latency. Older messaging systems like RabbitMQ and Apache ActiveMQ still compete in cases where simple message queues are enough. Some tools, such as Redis Streams and Apache Spark, can also be used alongside or instead of Kafka for real-time or analytics workloads.

As more Indian companies move toward AI-driven operations, Confluent wants to be the key player that helps them turn milliseconds into insights and seconds into dollars.

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