We were excited to host GraphTalk Manufacturing and Automotive in Germany for the second time, bringing together engineers and data leaders from across the industry in Munich on 2 July 2026.
The day opened with a clear thesis: manufacturers sit on a mountain of siloed data, and AI projects stall without a way to make that data AI-ready.
From there, customers took the stage to show what that looks like in practice. BMW Group walked through its AI Engineering Cockpit, a knowledge graph connecting specs, tests, defects, and milestones across previously disconnected tools, with a multi-agent system on the roadmap to support the full development lifecycle. Airbus and CIMPA (Sopra Steria) demoed AeroTrace, a supply chain risk tool that traces how a single part disruption ripples through the aviation supply chain. Reply shared how they replaced fragile institutional knowledge with an operational graph connecting systems, processes, alerts, and schedules, cutting triage time significantly after a major supplier transition. Siemens showed the PortfolioXpert, a knowledge graph that grounds product search across millions of records so customers get precise answers instead of keyword guesses.
Every talk landed on the same point: retrieval and embeddings can find the right document, but they can't reason about how your systems, parts, and processes connect. Knowledge graphs close that gap, and the results customers are already seeing, faster triage, sharper search, clearer risk tracing, show what's possible when your data has structure. Missed the sessions or want to go deeper on any of the talks? [CTA: View the session recordings and decks].
Showed a knowledge-graph architecture connecting engineering data (specs, tests, defects, milestones) across previously siloed tools (AIDA, Octane, CDH/S3). Built with Neo4j plus AWS Glue for ETL, surfaced via a Quick Suite AI assistant and dashboard. Long-term vision: a multi-agent system (spec, project, test, problem, quality agents) supporting the full development lifecycle from concept to release.
Tackled portfolio information scattered across Siemens.com, the Xcelerator Marketplace (2k+ offerings), and internal product masters (3M+ records). Built a knowledge graph to ground conversational/RAG search in structured product, benefit, and use-case relationships, enabling more precise answers (e.g., comparing SIMOTION Scout vs. Sinamics Startdrive) than keyword or plain-AI search.
Addressed a real knowledge-transfer risk from a mandated supplier/provider rotation, where operational understanding was siloed in a few people and documentation had drifted out of date. Moved beyond plain embeddings/RAG (which located docs but not relationships) to a Neo4j graph connecting systems, processes, alerts, and schedules. Measured results: 30–40% reduction in triage time, ~3,000 tokens per agent query.
Framed the day around four top use-case categories in manufacturing/automotive: shop-floor visibility, supply chain and order optimization, requirements/engineering traceability, and sales/marketing enablement. Cited Siemens research that 31–94% of enterprise data is siloed and AI projects can fail without AI-ready data. Core argument: knowledge graphs turn fragmented, unstructured data into structured, relationship-aware context that both humans and AI agents can reason over.
» IDC Report – The Business Value of the Neo4j Graph Intelligence Platform: Discover how organizations achieved an average 230% ROI while accelerating AI, analytics, and business outcomes with connected data.
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