Imago Platform uses graphs to improve AI knowledge management

Imago Platform ran into a problem that keeps showing up in enterprise AI: the model sounded right until someone needed it to be right. Its first customer, a lighting and electrical business, wanted help sorting warranty claims across manufacturers with very different policies. The company fed a pile of PDF documents into the system, but the answers still weren’t consistently accurate.

That failure pushed Imago toward graphs. Magnus Helander, the company’s co-founder, said joining the Neo4j Startup Program helped the team switch the system over, and the results changed fast. The model, which had been missing the mark, started producing correct answers repeatedly. Not once. Again and again.

Helander discussed the work in an interview with John Furrier on theCUBE’s AI Luminaries series. Imago now connects CRM and ERP systems with documents and email, turning scattered business records into something the AI can work with. The company is only 18 months old, but it is already serving property management teams, car dealerships and retailers in Sweden.

The car example makes the point cleanly. Instead of forcing people through a maze of checkboxes, Imago built a needs-based product finder: tell it you have three kids and commute, and it can recommend a car. Helander said that kind of system would not have worked without both a graph database and advanced search tools.

The broader issue is trust. Helander said models are built to be helpful, which is exactly why they can invent details when they don’t have the facts. Graphs help anchor those models in real business context, linking one piece of data to another instead of letting the system improvise. And when agents start doing work in the background, tracing every step matters too; Helander said Neo4j helped there by creating nodes for each step the agent took.