Witness why live, context-rich data is becoming essential to AI-driven business outcomes.
Kishore, let’s start with your journey—what led you to co-found StarTree, and how has your background shaped the company’s direction in real-time analytics?
My journey to co-founding StarTree began at LinkedIn, where I helped create Apache Pinot to power user-facing analytics at scale. I saw firsthand the growing demand for real-time insights, not just for internal teams but for external users as well. That experience shaped StarTree’s mission: to bring real-time, user-facing analytics to every organization. My background in distributed systems and building scalable data infrastructure directly influenced our focus on delivering low-latency analytics with high concurrency, making data instantly accessible and actionable.
AI agents are redefining application intelligence—what distinguishes them from traditional apps, and why is real-time data so critical to their functionality?
AI agents are fundamentally different from traditional applications because they’re designed to operate autonomously, learn continuously, and make decisions in real time based on constantly changing context. Traditional apps follow predefined rules and workflows, but AI agents adapt dynamically, responding to new inputs, user behavior, and environmental changes. For this to work, they need access to real-time data, not just recent data, but streaming, event-level signals that reflect what’s happening right now. This immediacy allows them to personalize interactions, detect anomalies, optimize operations, and take proactive actions as situations evolve. Without real-time data, AI agents risk acting on outdated information, which can lead to poor decisions, missed opportunities, or degraded user experiences.
Mobile apps like Uber and DoorDash set a new bar for concurrency, routinely generating tens of thousands of queries per second from users expecting real-time updates. But that’s just the beginning—autonomous agents will vastly outnumber human generated queries. As swarms of AI agents query, analyze, and decide at machine speed, today’s high concurrency will look like a warm-up act.
What role do technologies like LangChain and NL2SQL play in enabling AI agents to deliver decisions dynamically, and how is StarTree integrating with these innovations?
Natural language to SQL (NL2SQL) has long been hailed as a democratizing force for data—promising to let anyone ask complex questions without needing to write queries. And while the tooling has made major strides, it hasn’t exploded in usage the way many expected. Why? Because even the best translation layer is only as good as the system on the other side of it.
The reality is that most analytic databases—where the answers to business-critical questions actually live—are too slow to support a true conversation. Most questions beget other questions, and if the user has to wait minutes to get an answer it’s not a conversation with your data. That’s not a user experience that drives adoption.
Model Context Protocol (MCP) is rapidly becoming the new standard for enabling LLMs to interact with backend systems like StarTree. Unlike bespoke integrations that are brittle, hard to scale, and require constant upkeep, MCP offers a consistent, well-defined interface—making it dramatically easier to connect natural language interfaces to real-time analytics. That’s why we’ve embraced MCP at StarTree. It’s the foundation for powering intelligent, agent-driven applications that are both robust and scalable.
Real-Time Retrieval-Augmented Generation is gaining attention—how does this approach differ from standard RAG models, and what makes it uniquely valuable in high-velocity data environments?
Real-Time Retrieval-Augmented Generation (RAG) takes the traditional RAG model a step further by ensuring the data retrieved is not only relevant but also up-to-the-moment. Standard RAG relies on static or periodically refreshed datasets, which can quickly become outdated. In contrast, Real-Time RAG taps into live data streams, enabling AI agents to generate responses based on the freshest possible information. This is critical in high-velocity environments like fraud detection, personalization, or operational monitoring, where seconds matter. By integrating with real-time platforms like StarTree, Real-Time RAG empowers AI to act with precision, speed, and situational awareness that static models simply can’t match.
In terms of fraud detection and security, how are real-time vector embeddings changing the game, especially when analyzing transactional anomalies?
In fraud detection and cybersecurity, real-time vector embeddings are unlocking a powerful new paradigm—transforming how we detect and respond to threats. Traditionally, these domains have relied on deterministic rules: fixed thresholds, known signatures, and explicit “if-this-then-that” logic. But that approach breaks down as attackers get more sophisticated and behaviors become harder to codify.
By turning raw transactional data or log streams into vector embeddings, systems gain a probabilistic, pattern-matching engine that goes far beyond rigid rules. Embeddings capture subtle relationships across hundreds of dimensions—things a human analyst would never spot. You’re no longer just asking, “Is this value too high?” You’re asking, “Does this feel like fraud, based on everything we’ve ever seen?”
In real time, that means you can compare every transaction or log event to millions of past examples, instantly flagging outliers that deviate from learned patterns—even if they don’t break any rules. It’s pattern recognition on steroids. And because vector embeddings are updated continuously from live streams, these systems evolve alongside the threats, always learning and adapting. That shift—from static rules to dynamic context—is why vector-powered security is becoming a game-changer.
Observability stacks are shifting rapidly—what trends are you seeing as organizations move away from monolithic monitoring platforms?
Observability stacks are undergoing a significant transformation as organizations shift away from monolithic, all-in-one platforms toward more modular, best-of-breed solutions. This disaggregation allows teams to select specialized tools for metrics, logs, traces, and events, optimizing each layer for their specific needs. Such flexibility is crucial in today’s complex environments, where real-time insights are essential for operational excellence and rapid decision-making. By adopting this approach, companies can achieve greater scalability, adaptability, and precision in monitoring and managing their systems, ultimately enhancing performance and resilience.
How are open-source tools like Apache Pinot and Kafka driving the move toward modular, real-time observability architectures?
Open-source tools like Apache Pinot and Kafka are foundational to the shift toward modular, real-time observability architectures. Kafka enables high-throughput, low-latency streaming of logs, metrics, and event data, while Apache Pinot provides ultra-fast, high-concurrency analytics on that data as it arrives. Together, they eliminate the need for slow, batch-based pipelines and unlock real-time insights at scale. Their open-source nature also allows teams to integrate them flexibly into disaggregated observability stacks, choosing the best tools for ingestion, storage, and visualization. This modularity gives organizations more control, faster innovation cycles, and the ability to tailor observability to their unique operational and business needs.
As more businesses adopt real-time analytics, what industries do you see leading the charge, and what are they doing differently?
Industries like finance, e-commerce, logistics, and ad tech are leading the charge in real-time analytics because they operate in high-velocity environments where every second counts. What sets them apart is their shift from reactive to proactive decision-making, using real-time data not just to monitor but to drive operations. For example, fintech companies are using real-time anomaly detection to stop fraud mid-transaction. E-commerce platforms personalize user experiences on the fly. And logistics companies optimize delivery routes in real time based on changing conditions. These leaders are embracing event-driven architectures and integrating streaming analytics deeply into their core applications, not just as a dashboarding layer.
What we’re also seeing, particularly in these more mature industries, is real-time analytics becoming a first-class citizen within the broader data platform—not a special use case or edge scenario. Instead of treating real-time systems as bolt-ons, leading organizations are building architectures where batch and streaming data coexist by design. This shift signals a recognition that timely insight isn’t optional—it’s foundational.
With performance and scale being so crucial, how does StarTree help enterprises manage massive data streams while maintaining real-time responsiveness?
StarTree is built to handle massive data streams with millisecond-level responsiveness by combining the speed of Apache Pinot with enterprise-grade enhancements. We support real-time upserts, tiered storage, and advanced indexing to manage high data cardinality and ensure fast query performance at scale. Our hybrid ingestion engine allows seamless integration of streaming and batch data, so businesses can analyze fresh events alongside historical context without latency trade-offs. StarTree also enables high-concurrency access, which is critical for powering thousands, or even millions, of customer-facing queries simultaneously. This architecture lets enterprises act on live data instantly, without compromising on performance, scale, or reliability.
Where do you see the biggest breakthroughs coming in real-time analytics, and how is StarTree positioning itself to stay ahead of those developments?
We see two major fronts where breakthroughs in real-time analytics are unfolding—and where StarTree is actively investing to stay ahead.
First, real-time analytics is becoming a cornerstone of enterprise AI strategy. As companies build agentic systems and deploy LLMs, it’s no longer enough to rely on static, batched data. These AI systems need access to fresh, context-rich signals to make timely decisions, whether it’s adjusting recommendations, flagging fraud, or triggering automated responses. Real-time analytics supplies that live context. At StarTree, we’re leaning into this shift by supporting standards like Model Context Protocol (MCP) that allow agents and LLMs to interact directly with analytic systems, and by ensuring our platform delivers low-latency performance at massive scale—something most traditional data architectures simply can’t handle.
Second, we see a clear shift in how companies want to store and access their data—moving toward open formats like Apache Iceberg and decoupled architectures that separate storage from compute. This shift enables greater flexibility, interoperability, and cost-efficiency. Our vision is to make sure real-time analytics doesn’t sit on the sidelines of this transformation. We believe Pinot—and by extension, StarTree—should be just as effective at querying against modern data lakehouses as it is at streaming live data. That means designing for a world where batch and real-time work together seamlessly, where historical and fresh data are unified, and where developers don’t have to choose between latency and scale.
Together, these priorities reflect our belief that real-time analytics isn’t a niche—it’s becoming the backbone of intelligent, adaptive systems. And we’re building StarTree to meet that moment.
A quote or advice from the author
This can be about the industry; or StarTree and its mission/goals specifically; or a combination of both -think of it as closing remarks
The increasing prevalence of artificial intelligence across industries has elevated real-time analytics from a specialized tool to a foundational element for intelligent and adaptive systems. Analyzing data as it’s created for immediate insights and responses is now crucial, as relying solely on historical data becomes insufficient for leveraging AI’s full potential. Businesses must transition to live, contextual insights from real-time data streams, requiring robust platforms for low-latency ingestion, processing, and analysis of large data volumes to enable timely action.
StarTree’s central goal is to design and construct these advanced platforms, integrating real-time and historical data. We believe this synergy is vital for AI to make well-informed and timely decisions at scale. By combining real-time data with the context of historical trends, we aim to empower businesses to develop highly responsive and adaptive next-generation applications. Our commitment is to make real-time analytics accessible and integral to every data-driven organization’s strategy in this AI-driven era.
Kishore Gopalakrishna
Co-founder & CEO at StarTree
Kishore Gopalakrishna is the co-founder and CEO of StarTree, a venture-backed startup focused on Apache Pinot – the open source real-time distributed OLAP engine that he and StarTree’s founding team developed at LinkedIn and Uber. Kishore is passionate about solving hard problems in distributed systems and has authored various projects in the space such as Apache Helix, a cluster management framework for building distributed systems; Espresso, a distributed document store; and ThirdEye, a platform for anomaly detection and root cause analysis at LinkedIn.