Imago Platform Uses Graph Databases to Improve AI Knowledge Management

Helander spoke with John Furrier for theCUBE + NYSE Wired: AI Luminaries interview series on theCUBE, SiliconANGLE Media’s livestreaming studio. The discussion focused on how Imago Platform connects business systems into a secure, intelligent hub.

Imago’s first client, a lighting and electrical business, needed help resolving warranty claims across manufacturers with widely varying policies. The company ingested a large collection of PDF documents, but its AI model struggled to produce consistently accurate answers. Because the models generate probabilistic rather than deterministic responses, the approach fell short of the client’s requirements, according to Helander.

“We just couldn’t get the thing to 100% accuracy,” he said. “We got on the Neo4j Startup Program and got some help to start working with graphs, and suddenly … the model started delivering correct results again and again and again.”

Founded 18 months ago, Imago Platform also serves property management teams, car dealerships and retailers in Sweden. Its system connects customer relationship management and enterprise resource planning systems with documents and email, bringing information from disparate sources together.

Helander described a product finder that asks users to enter their needs, such as a car buyer who has three children and commutes. Instead of going through classic check boxes to choose among 300 alternatives, Imago built a needs-based product engine. He said that would not have been possible without both a graph database and a number of advanced search technologies.

Imago Platform’s experience highlights the importance of grounding AI agents in relevant business information, Helander said. Graph databases help establish relationships among disparate sources, giving agents a way to retrieve information in context rather than relying solely on a language model’s general knowledge.

“The models are designed to be helpful,” he said. “If it doesn’t have data, it will generate it for you because that’s the definition of helpful, and then you get hallucinations. Something happens when you provide context to the model. This thing is connected to this thing, which is related to this thing in this way, and suddenly it just seems like a perfect match.”

AI agents can draw on databases, file storage, live data and application programming interfaces to carry out business tasks. As more business logic moves into the AI layer, developers need ways to monitor agent activity and trace the steps agents take, Helander said.

“Suddenly you have agents working in the background because they are deep down into a Kubernetes network or grinding away somewhere where you don’t really have observability,” he said. “You really have no idea how it arrived at that conclusion. Neo4j has saved us there. It can trace the agent’s work. It creates nodes for every step of this.”