Knowledge Graphs, Retrieval Infrastructure, and the Palantir Ontology: A Technical Tour
Two ecosystems are converging on the same problem from opposite ends. On one side sits the open tooling stack for knowledge representation and retrieval: personal knowledge graphs like Obsidian, property-graph and RDF databases, vector stores, and the hybrid retrieval architectures that stitch them together for LLM applications. On the other side sits Palantir’s Foundry and AIP: a vertically integrated enterprise platform whose Ontology, Pipeline Builder, AIP Logic, and Workshop implement — in a governed, opinionated form — essentially the same architecture that teams otherwise assemble from open components.
Understanding both is valuable because they illuminate each other. The open stack teaches you what is actually happening under the hood: how graphs are stored and traversed, how approximate nearest-neighbor indexes trade recall for latency, why lexical and dense retrieval fail in complementary ways. The Palantir stack teaches you what the assembled system has to look like to survive contact with an enterprise: typed schemas, permission-aware retrieval, human-in-the-loop writeback, lineage, and evaluation. This article works through both stacks in technical depth and then maps one onto the other.
Part I: The Open Knowledge and Retrieval Stack
1. Obsidian: the emergent, file-first knowledge graph
Obsidian is often dismissed as a note-taking app, but architecturally it is a local-first, schema-optional knowledge graph whose storage layer is nothing more than a directory of Markdown files. That design choice has deep consequences.
Read the full article on my substack: https://brianjamescurry.substack.com/
Brian Curry is a Kansas City–based cognitive systems architect and applied AI researcher working at the intersection of AI architecture, knowledge engineering, and intelligent-system design. He is the founder of Vector1 Research, an applied AI lab publishing on agent systems, AI evaluation, cognitive architecture, and the macroeconomics of AI-driven transformation. He has led applied AI and data initiatives across major enterprises including Koch Industries, Tractor Supply, Vail Resorts, Hallmark, Garmin, AT&T, and McClatchy, and founded KC AI Lab, a 1,500-member AI/ML community. His open-source work includes Papilon (causal inference and counterfactual simulation), PyCausalSim (causal discovery via agent-based simulation), MeaningFlow (semantic modeling and ontology), and Memory-Node Encapsulation (episodic memory for agentic systems). Contact: brian at vector1.ai · LinkedIn
