Why Palantir’s Ontology, Not Just Large Models, Drives Its Valuation Surge

The DataFun technical community hosted a 90‑minute live dialogue on April 24, 2026, featuring system‑intelligence architect Lv Hangfei and two senior guests— Hu Shenmin , head of intelligent R&D at Shanghai Bank, and Xi Zongzheng , senior R&D engineer at Alibaba Cloud. They examined why both banking risk‑control and cloud‑operations teams converge on Palantir’s “ontology” as the key to its soaring market value.

Xi opened by describing three “gaps” that arise without an ontology. The first is a data gap: raw logs, metrics and traces are noisy and fragmented, with over 99% of incoming events being irrelevant, forcing engineers to manually stitch alerts across Prometheus, SLS and Trace APM. The second is a model gap: large‑model AI behaves like a black box, often hallucinating or mis‑attributing causality when it lacks prior knowledge of service dependencies. The third is an engineering gap: enterprises ingest petabytes to exabytes of data daily, creating massive storage, processing and security challenges.

To illustrate ontology, Xi introduced the u model. In this model, a Set represents a node and a Link an edge, turning the entire IT landscape into a graph. He gave a concrete server example: an entity ECS.instance with attributes (IP, CPU, memory, OS, status) runs a micro‑service entity APM.service; the relationship type is “runs‑on”. Observability data such as CPU usage or logs are attached to the server via a data relationship, forming a minimal observable unit.

Hu added a business‑oriented view, splitting ontology into a business layer and a technical layer. He described five dimensions of a business ontology: (1) entity model (e.g., customer, credit‑application, credit‑limit), (2) process model (five‑level modeling: domain → value chain → activity → task → step, following IBM’s methodology), (3) behavior model (actions like “submit application”), (4) rule model (extracting policy logic from legacy if‑else code), and (5) data model (mapping to existing data warehouses and APIs). He emphasized that without a clear process model, AI actions become untraceable, which is unacceptable in regulated finance.

The panel then compared large models to the brain and ontology to the skeleton plus memory. Xi explained that the skeleton defines the IT world’s structure, while the memory stores expert runbooks and best‑practice knowledge—domain‑specific, not generic model training data. A “reflection” capability lets the AI discover which tools or data an entity can access at runtime, enabling dynamic decision‑making.

Both guests highlighted Graph‑RAG as a “blood‑meat” supplement: the knowledge graph supplies precise entity relationships, while the large model provides contextual reasoning. To avoid overwhelming the model, they designed a directory‑like progressive loading mechanism—first loading the catalog, then deep‑loading only the needed branches.

When asked for advice to teams starting ontology projects, Xi warned against trying to model everything at once or building ontology for its own sake. He recommended selecting the most painful scenario, constructing a minimal viable model, and iterating. Hu stressed that 70% of the effort must come from business experts, with continuous discussion and validation, and that incentives (a Wikipedia‑style honor system) and LLM‑assisted rule extraction can mitigate expert time scarcity.

In closing, the speakers agreed that ontology will not be eliminated by future AGI; instead, it will become a new operating system for AGI, providing the structured, trustworthy knowledge that large models alone cannot supply.