Database startup Neo4j embraces AI to accelerate growth

To make AI possible, you need to create connections between vast amounts of data. This is where technology like graph databases comes into play.

Graph databases handle fast-changing, interconnected data more adeptly than traditional databases designed to store rigidly structured information. Of course, graph databases need to be managed to be useful. Many companies sell products for this purpose, but one of the biggest vendors is neo4j.

Neo4j’s roots date back to the early 2000s, when founders Emil Eifrem, Johan Svensson and Peter Neubauer identified problems with traditional database technology. The trio developed a prototype of Neo4j, the graphical database management software that bears the company’s name.

“We conceived the idea for the first feature graph database during a flight to Mumbai in 2000,” Eifrem told TechCrunch. “We drew this on a napkin; I wish I still had it but unfortunately it has since disappeared.”

Neo4j was launched in 2007 in Sweden, where Eifrem, Svensson and Neubauer were based at the time. In 2011, the firm moved to Silicon Valley to raise venture financing.

Today, Neo4j’s software allows companies to create, edit and distribute graph databases. Like other graph databases, Neo4j’s data is stored as nodes, relationships, and features. Nodes hold information about an entity, such as a person or product; relationships define connections between nodes; and properties add more detail to nodes and relationships.

Neo4j’s graph databases can query data in a way that reflects how real-world entities are connected; This is a boon for AI. Data in graph databases is expressed as a “knowledge graph,” grounding the AI ​​in context that can inform its outputs.

With the rise of AI, Neo4j has invested heavily in what it calls “GraphRAG,” a technique that allows AI to retrieve data from external sources. GraphRAG uses knowledge graphs to represent data in documents and related metadata, improving the performance of AI in some cases.

Neo4j also introduced new vector search features that capture relationships in databases based on elements with similar properties. Vector searches are useful for AI that needs to search for similar text or files, make suggestions, or identify broad patterns.

The increased focus on AI-supporting capabilities has benefited Neo4j. The company says its revenue has surpassed $200 million (double what it was three years ago) and will reach positive cash flow “in the coming quarters.”

Neo4j, which holds 44% of the graph database market (according to a Cupole Consulting Group report) and counts 84% ​​of the Fortune 100 as customers, including IBM and Walmart, plans to add even more AI features to its platform next year .

“Businesses are increasingly looking at AI to understand what it can do for their organisation, but AI results need to be accurate, transparent and explainable to the average person, including builders, inspectors and regulators,” Eifrem said. “Our technology helps organizations achieve successful production deployments faster and more efficiently.”

Neo4j, valued at $2.2 billion, with 800 employees and 1,700 customers, plans to eventually go public. But for now, it is focused on growth. The company recently received $50 million from Neotus Partners to “strengthen its balance sheet.” (To date, Neo4j has raised approximately $550 million in venture capital.)

Even if Neo4j waits years for an IPO, the graph database industry is likely to remain intact. By the way According to Grand View Research, the graphics technology market will be worth $15.8 billion by 2030. And Gartner predictions It is predicted that by 2025, 80% of data and analytics innovations will be made using graphics technology.