Why Real-Time Data Architecture Is the Cornerstone of AI Success

For C-level leaders worldwide, the challenge lies in making sense of the AI disruption. Many are exploring use cases while struggling to align AI with broader business goals. The biggest ‘make or break’ factor? It's Data, and how it’s been handled so far, how it’s used for AI, and the data systems in place.

In this context, Srini Srinivasan, Co-founder and CTO of Aerospike, offers a grounded perspective: AI only works if your data systems can keep up, with speed, consistency, and scale.

Aerospike supports some of the most data-intensive workflows in the world, whether it's powering UPI-level throughput or enabling Flipkart’s Big Billion Days. As AI evolves, Aerospike’s role in data infrastructure becomes increasingly vital.

The Real Shift: From Batch Thinking to Real-Time Execution

“Many systems were batch because they had to be,” Srini says. “Legacy databases couldn’t keep up with real-time ingestion or processing. But that has changed.”

With modern data infrastructure like Aerospike, paired with event-streaming platforms like Kafka, organizations no longer need to choose between scale and speed. Real-time isn’t just possible, it’s essential.

Real-time data processing is key, with decisions often needing to be made in seconds. Use cases like fraud detection, dynamic pricing, instant personalization, and risk mitigation now demand sub-millisecond insights, not for an edge, but for survival.

Vector Databases: Building Intelligence into Data Itself

How is the industry aligning toward a real-time data ecosystem? Srini points to vector search, a key development that allows similarity searches across data types like images, user behavior, or recommendations.

“Think of it as building intelligence into the data layer,” he explains. Aerospike doesn’t just support vectors, it integrates them with key-value and graph models. This allows enterprises to build hybrid applications blending machine learning, search, and transactional logic, all in real time.

For CxOs, it’s a game-changer, faster AI time-to-value with fewer integration headaches.

Customer 360: Moving from Silos to Achieve a Single Source of Truth

In industries like telecom, BFSI, or e-commerce, customer data is typically fragmented across multiple systems. Each business line runs its own stack.

Srini gives a telco example, “You might be using mobile, broadband, and DTH, each with different systems. But the business needs a unified, real-time view.”

Aerospike enables this by ingesting data from legacy SQL, NoSQL, and proprietary systems in real time through Kafka or similar pipelines. The result, a high-performance, unified view of the customer, fueling smarter support, proactive service, and deeper personalization.

If AI Is the Goal: Real-Time Data Is the Enabler

“AI isn’t new to us,” says Srini. “We’ve supported AI/ML use cases for over a decade, from fraud detection to real-time recommendations.”

These use cases demand low-latency access to massive data. With today’s explosion in AI, especially generative and agentic models, the challenge is merging real-time data streams with AI decisioning.

While GenAI handles static content well, integrating dynamic data is still evolving. Aerospike is actively working with customers to bridge that gap and deliver ROI from real-time AI.

India: A Playground for Computing at Scale

If scale is the test, India is the exam room. Aerospike’s infrastructure powers massive workloads, from Dream11’s pre-IPL surge to UPI transactions handled with sub-millisecond latency via partner Mindgate.

“Flipkart recently hit 95 million transactions per second on Aerospike,” Srini says. “That’s personalization at population scale.”

For CxOs focused on emerging markets, Aerospike’s India experience proves real-time platforms can handle the world's largest digital ecosystems.

The Road Ahead: The Data Blend - Cloud-Native, AI-Native, Community-Driven

Aerospike’s roadmap focuses on three key pillars:

Cloud-native deployment – Easy to consume across public clouds or as a service.

AI-first features – Enhanced support for vectors, graphs, and real-time analytics to align with modern AI workloads.

Open collaboration – Partnering with developers, partners, and enterprises to evolve real-time AI solutions.

Message for CxOs

Data architecture is no longer an IT concern, it’s a boardroom issue. Whether you aim to elevate customer experience, automate decisions, or deliver faster insights, your infrastructure must evolve.

Aerospike isn’t just another database. It’s a strategic enabler for the AI-driven enterprise.

“Data is changing. Business is changing. And the architecture that powers both must change too,” Srini sums up.