Enabling AI requires making connections between massive amounts of knowledge. This is where technology-like graph databases come into play.
Graph databases handle rapidly changing, interconnected data higher than traditional databases designed to store rigidly structured information. Of course, graph databases must be managed to be useful. Many corporations sell products for this purpose, but one in all the larger suppliers is Neo4j.
Neo4j's roots return to the early 2000s, when its founders – Emil Eifrem, Johan Svensson and Peter Neubauer – identified problems with traditional database technology. The trio developed a prototype of Neo4j, the corporate's eponymous graph database management software.
“The idea for the primary property graph database got here to us during a flight to Mumbai in 2000,” Eifrem told TechCrunch. “We drew it on a napkin – one which I might still prefer to have, but unfortunately it has since disappeared.”
Neo4j was launched in Sweden in 2007, where Eifrem, Svensson and Neubauer were based on the time. In 2011, the corporate moved to Silicon Valley to boost enterprise capital.
Today, Neo4j software enables organizations to create, orchestrate, and deploy graph databases. Like other graph databases, Neo4j stores data as nodes, relationships, and properties. Nodes contain details about an entity, similar to an individual or a product. Relationships describe 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 – a boon for AI. Data in graph databases is expressed as a “knowledge graph,” which embeds the AI in a context that may influence its results.
With the arrival of AI, Neo4j has invested heavily in what it calls “GraphRAG,” a method that permits AI to retrieve data from external sources. GraphRAG uses knowledge graphs to represent data in documents and associated metadata, in some cases improving the performance of an AI.
Neo4j has also introduced recent vector search capabilities that capture relationships in databases based on elements with similar characteristics. Vector searches are useful for AI that needs to go looking for similar text or files, make recommendations, or discover general patterns.
The increased concentrate on AI-supporting features has paid off for Neo4j. The company says revenue has exceeded $200 million – double what it was three years ago – and can end in positive money flow within the “coming quarters.”
Neo4j, which dominates 44% of the graph database market (in response to a report from Cupole Consulting Group) and counts 84% of the Fortune 100 amongst its customers, including IBM and Walmart, plans so as to add much more AI capabilities to its platform next 12 months.
“Companies are increasingly taking a look at AI to know what it will probably do for his or her business – however the AI results have to be precise, transparent and explainable to the typical person, including builders, auditors and regulators,” said Eifrem. “Our technology helps corporations achieve successful production deployments faster and more efficiently.”
With a valuation of $2.2 billion, 800 employees and 1,700 customers, Neo4j intends to eventually go public. But without delay the main target is on growth. The company recently secured $50 million from Noteus Partners to “strengthen its balance sheet.” (To date, Neo4j has raised around $550 million in enterprise capital.)
Even if Neo4j waits years to go public, the graph database sector should remain robust. After According to Grand View Research, the graphene technology market will probably be value $15.8 billion by 2030. And Gartner Forecasts that by 2025, 80% of knowledge and analytics innovation will occur using graph technology.
