The AI you can search: How Elastic is helping enterprises make sense of their data

When Ashutosh Kulkarni, CEO of Elastic talks about artificial intelligence, he doesn’t begin with models or algorithms. He starts with search. “Search is the bridge between chaos and clarity,” he says. “That’s true not just for the internet, but for every enterprise drowning in unstructured data.”

At a time when large language models (LLMs) dominate the AI conversation, Elastic has quietly built leadership around an equally critical aspect of the equation: relevance. The company’s Elasticsearch platform excels at this, connecting generative AI with proprietary enterprise data through a process Kulkarni calls “context engineering”. “LLMs know only what they are trained on – public data,” he explains. “To make AI truly useful for business, it needs to understand private context. That’s where Elastic comes in.”

Rethinking enterprise intelligence with search and AI

Elastic began as the force behind Elasticsearch, the open-source engine that revolutionised how developers indexed and retrieved information. Over the years, it has evolved into a unified platform for search, observability, and security, powered by what it calls the Elastic Stack (Elasticsearch, Kibana, Logstash, and Beats).

For LLMs and GenAI models to be truly fit for enterprise use, they need real-time proprietary context. Elastic is the foundational data platform that powers context engineering, the crucial link that connects an organisation’s proprietary data to its AI model and ensures that AI outputs are accurate, trustworthy, and specific to the business.

“Just like Google helps you find information on the open web, we help you find, analyse, and act on your organisation’s data including logs, metrics, documents, or images,” says Kulkarni. “Our job is to make that data accessible, useful, and actionable. That’s the missing link between LLMs and real-world business outcomes.”

Kulkarni believes the rise of AI will transform not just technology stacks but also entire business models. “We’re moving toward AI-native enterprises,” he says. “Companies are redesigning how work gets done, from recruiting and onboarding to development and support. Every process that was manual or semi-automated is being reimagined.”

But he’s also quick to acknowledge what he calls the efficiency paradox. “AI is transformative, but like every new technology – whether it was the internet or cloud computing – it’s expensive at first,” he notes. “Over time, costs come down. The key is making thoughtful, efficient investments that deliver real outcomes.”

Elastic’s advantage, he adds, lies in deeply integrated intelligence. “You can’t just bolt AI onto a system and expect magic. It needs to be embedded in the product, in the user experience, in how people make decisions.”

Real-world impact: From digital contracts to public safety

Elastic’s technology powers some of the world’s most complex data systems. When electronic signature company DocuSign wanted to help enterprises analyse the millions of agreements flowing through its platform, it used Elastic to make every signed document searchable, creating a completely new revenue stream in the process.

Closer to home, India’s fast-growing digital ecosystem offers equally compelling stories. “Imagine a customer in a Tier II city who takes a picture of a dress, uploads it, and finds similar products in Hindi or Marathi,” Kulkarni says. “That’s multimodal, multilingual AI at work; and that’s how inclusivity happens.”

Elastic’s open-source foundation continues to be one of its biggest strengths. “Community involvement amplifies creativity,” says Kulkarni. “It was our users who showed us that the same engine powering search could also handle observability and security.”

He credits Elastic’s early developer community for its global footprint. “Wherever we go, there’s always someone who’s used Elasticsearch and loves it,” he says. “That’s brand equity you can’t buy, it’s earned through years of open innovation.”

With the recent rise of open-weight AI models like Mistral and LLaMA, he believes open ecosystems will do for AI what Linux did for cloud computing. “Open AI will democratise innovation,” he says. “It’s how smaller players can compete with the giants.”

India: From delivery hub to innovation driver

India now represents Elastic’s second-largest employee base after the US, with teams across Bengaluru, Mumbai, Delhi, and Chennai. The company partners closely with global capability centres (GCCs) of major banks, telcos, and enterprises, many of which have shifted strategic decision-making to India.

“We look at India through two lenses, as a market and as a talent hub,” Kulkarni explains. “The GCC model has evolved from development to decision-making. India is no longer just building software, it’s shaping global strategy.”

He also credits the depth of India’s STEM talent for Elastic’s continued innovation. “The energy, the curiosity, the willingness to learn are all phenomenal. Our India teams are driving projects that impact customers around the world.”

The human side of AI leadership

“AI won’t replace human intelligence; it will expand it,” he says. “Think of it as a continuum, from co-pilots to autonomous agents, with humans always in the loop.”

But he also cautions against complacency. “AI can tempt us to get lazy; it can do your homework, grade your work, even write your code. But that’s exactly when we must double down on first principles and systems thinking. The real danger is not AI; it’s losing curiosity.”

For him, leadership amid this AI boom is ultimately about clarity and gratitude. “Curiosity keeps you moving forward,” he says. “And never forget where you came from. Success isn’t just about being smart; it’s also about timing, choices, and the people who helped you get there.”

Looking ahead

As the world enters the age of generative AI, Elastic’s approach stands out for its grounded pragmatism. It isn’t chasing hype cycles, it’s engineering context. In doing so, it’s redefining how enterprises discover, connect, and act on information.

In Kulkarni’s words, “At the end of the day, AI is only as good as the data it understands. Our mission is to make that understanding seamless, secure, and accessible for everyone.