Independent strategic advisory for leaders building GenAI and knowledge graph initiatives that need to survive contact with real, messy, connected enterprise data.
Request a Strategy CallAt the root of this is a data problem. The relationships that matter are only as good as the data infrastructure surfacing them: how it's modeled, integrated, and governed across the systems it spans. Without that foundation, even the right architecture has nothing solid to stand on.
Standard vector search and off-the-shelf RAG pipelines work well for simple lookup tasks. They fall apart when they meet the way real enterprises actually work: multi-hop relationships, regulatory constraints, and knowledge that lives in the connections between systems, not inside any single document.
Getting from a promising demo to a production system that leadership can trust requires treating your data and knowledge architecture as seriously as the model itself, well before the first line of code is committed.
Bechberger AI is led by Dave Bechberger, who has spent over 20 years designing and building complex, distributed data architectures at companies including AWS, DataStax, and Stitch Fix. Over the last decade, that work has focused specifically on graph databases and knowledge graph architecture, and most recently, on real-world applications at the intersection of graphs and GenAI, across industries ranging from bioinformatics and supply chain to fraud detection and energy, as well as advising clients across many other domains.
He is the co-author of Graph Databases in Action (Manning Publications), a regular speaker at industry conferences on graph technology, and certified across the major graph database platforms. Today, that foundation is applied to a newer problem: helping organizations build GenAI and agentic systems that are grounded in real, connected enterprise knowledge, not just vector similarity.
Vendor-agnostic architecture and strategy advisory, tailored to where you are in your GenAI journey.
A focused audit of your data estate and use cases to determine where graph-augmented GenAI will actually help, and where it won't.
End-to-end architecture and schema design for graph-backed GenAI systems, handed off cleanly to your engineering team.
Continuing architectural oversight as your team builds, catching costly missteps before they ship.
A quick gut-check before you reach out.