Dr. Bedir Yilmaz, a computer-vision PhD turned AI Architect at lcmd, explains how the company's first attempt at a construction-site conversational agent — a RAG pipeline built over AI-generated prose narratives of project activity — could answer "what already happened" questions but failed at the sense-making questions site experts actually needed, like what's threatening a project's timeline. That gap pushed him toward a Neo4j knowledge graph, built using agentic construction techniques, which now spans 108 projects with roughly 25 million nodes and 83 million relationships in private beta. The graph powers "Consultant in a Box," a natural-language agent that generates Cypher on the fly and surfaces fleet-wide construction wisdom — like which trade typically precedes drywall — without exposing any single customer's private project data.

Topics Covered

• lcmd: a Stuttgart-based lean construction management platform spun out of Drees & Sommer, used on complex multi-year builds like Germany's largest particle accelerator for antiproton research and the Denmark–Germany rail tunnel
• From computer vision PhD to construction AI: Bedir's path from crowd counting/density estimation research into an AI Architect role with no prior construction background
• The original goal: a conversational engine letting construction site teams talk to their own project data
• Why the app's collaborative, highly efficient source data wasn't usable by LLMs in its raw form, and the first fix: using coding agents to transform it into prose narrative corpora for a traditional RAG pipeline
• Where RAG hit its ceiling: it could answer "what already happened" questions (who's the most influential person, what happened last month) but site experts needed sense-making questions about the past and future — like what's threatening the project timeline — that similarity search can't answer because it ranks by linguistic closeness, not construction-domain logic
• The graph moment: realizing the missing connections were logical, not linguistic, and that a knowledge graph was the way to represent that construction logic
• Building the graph with agentic construction, inspired by Andreas Kollegger's Coursera lectures on agentic knowledge graph construction, and reaching near-parity with lcmd's own API within about an hour of applying what he'd learned
• Current scale: 108 projects loaded into one connected graph (~25M nodes, ~83M relationships) — in private beta, not yet GA
• Why a shared, multi-tenant graph works: project schemas are structurally similar (trades, tasks, dependencies), letting lcmd answer cross-project "wisdom of construction" questions without surfacing any customer's private data
• "Consultant in a Box" — lcmd's natural-language agent, built on a custom text-to-Cypher engine plus pre-built query templates for anticipated questions, with text-to-Cypher reserved for the ones it can't anticipate
• Why entity resolution hasn't been a problem: the knowledge graph is built deterministically from lcmd's own structured application data (trade selection, task dependencies, start/end dates), not extracted from unstructured text
• lcmd's product thesis: the knowledge graph is the product, not just a means — the long-term vision is letting other people's agents query lcmd's construction knowledge at a fair market price
• lcmd is now an official member of the Neo4j Startup Program and is already putting its Aura credits to use

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