At a time when every tech firm is rushing to claim its place in the generative AI landscape, Neo4j is quietly steering the conversation in a different direction, one that places context and connection above raw computation.
At the Neo4j GraphSummit in Bengaluru, company leaders reflected on the growing awareness around GraphRAG, its critical role in explainable AI and the reason why India now sits at the centre of the global graph revolution.
Bridging the GraphRAG Confusion
When the term ‘GraphRAG’ comes up in tech discussions, it often triggers puzzled reactions. Neo4j’s team acknowledges this confusion and sees it as a messaging challenge that’s also an opportunity.
Kristen Pimpini, vice president and general manager for APAC at Neo4j, explained how the understanding of graphs has evolved. “One of the things that we typically do and see is the awareness of Graph, [and it has] evolved over the last five years,” he said.
“All of a sudden, Graph is now relatively mainstream in the wider tech and developer community.”
He added that industry analysts, including those at Gartner, are now talking about this technology, and they are actually helping differentiate by not being a vector RAG technology.
Siddhant Agarwal, developer relations lead of APAC at Neo4j, added that the rise of GraphRAG goes beyond semantics.
He explained that the growing appreciation for GraphRAG and knowledge graphs stems from their ability to provide explainability and enhance the accuracy of search results. While traditional RAG offers some level of improvement, it falls short in capturing comprehensive context and facilitating multi-hop reasoning, both of which are effectively addressed by GraphRAG.
He pointed out that Neo4j’s implementation does not discard vector search but “complements it with graph traversal”, enabling semantic plus contextual search within one unified system. “You can now do vector and graph search together in Neo4j; everything is under one roof.”
A Platform, Not a Competitor
As Big Tech and AI startups race to release agentic frameworks, Neo4j is adopting a more collaborative approach. “Alliances are key in any software,” Pimpini said. “We are a piece of the puzzle. Just like an LLM is a piece of the puzzle.”
That philosophy extends even to hyperscalers and data giants. He highlighted the company’s strong partnerships with Snowflake, Databricks and the major hyperscalers, viewing them as collaborators, despite potential product overlap.
He stated that these partners already have the necessary infrastructure and data, which allows Neo4j to connect with them and provide superior solutions.
Agarwal reinforced this sentiment in the context of AI agents. “Others are offering agents as a box solution,” he said. “What we’re offering is the platform as a box solution. Within the Neo4j Aura console, you can deploy your own agentic application.”
This distinction between plug-and-play tools and foundational platforms marks Neo4j’s positioning within the agentic AI ecosystem.
He highlighted that their core business revolves around leveraging the power of knowledge graphs to help customers achieve their AI goals and enhance their applications. While other players offer similar solutions, these are often an add-on to their primary business.
For Neo4j, knowledge is central to its mission—to help customers unravel their data and present it effectively, whenever and wherever it’s needed. This focus is its defining differentiator.
Rather than targeting the broad consumer market directly, Neo4j’s product enables better consumer experiences for its clients. This focus is paramount to its business.
In that sense, its competitive products in the market are often more complementary than competitive. For Neo4j, graph capabilities aren’t its core offering, but a ‘nice-to-have’ feature. It, however, allows customers to explore their data more deeply, a need that will only become more complex.
India: From Developer Hub to Growth Engine
While the global AI conversation often gravitates towards Silicon Valley, Neo4j’s sights are firmly set on India.
“What we want to do is identify markets where there’s mature adoption, India being one of them,” he said.
He explained that India boasts a vast community base, utilising their open-source product for an extended period. This widespread adoption spans various sectors, from enterprises and startups to commercial businesses across the country.
“We have been investing considerably over the last five years. We’ve got people on the ground…We’ve also established support and professional services here.”
Agarwal added that Neo4j’s newly reimagined startup programme is specifically designed to empower innovators. “It’s part of a $100 million global commitment from our CEO, announced at GraphSummit London,” he said.
The commitment also includes expanding Neo4j’s product portfolio, with offerings such as Neo4j Aura Agents and MCP servers.
Beyond enterprise clients, the company is also placing a strong bet on developers. From local meetups in Bengaluru and Pune to its GraphAcademy portal, Neo4j is building deeper awareness.
Developers can take free courses, earn certifications, and even participate in Notes Conference, a 24-hour virtual marathon of graph content “by graph developers, for graph developers”.
From Proof to Payoff
The larger question now is where India, and indeed the world, is headed in this data-driven, AI-fuelled age.
Pimpini believes the next phase will shift focus from proof-of-concept to profit. “Over the next three years, we’re going to see a lot of internal use cases start to turn external,” he said.
He said that the industry is going to see internal use cases turning external, as companies become more confident to launch products. It’s not about replacing jobs; it’s about complementing them.
“The goal is for organisations to start generating a return on investment with AI, and then begin monetising their work,” he said.
Pimpini acknowledged that while there are a lot of proof of concepts going on, some of them very exciting, the real transformation will come when AI applications begin delivering measurable business value.
The company’s optimism rests on the growing demand for explainable, context-aware AI, a space where GraphRAG might soon become a non-negotiable layer.
In the end, Neo4j’s message stands out as almost contrarian amid the clamour of today’s AI race: don’t just build smarter models, build deeper connections.
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