A deep dive into Palantir's approach to enterprise semantics, and what dbt-agent might learn from it.
Let's acknowledge it: Palantir has a checkered reputation. Defense contracts, surveillance implications, the general vibe of a company that's comfortable with power asymmetries. Peter Thiel. All of it.
But they also make a crap load of money. And their recent growth -- particularly around AI deployment -- suggests they've built something enterprises desperately want.
The question isn't whether we like them. It's whether they've solved problems we're still fighting.
Palantir's Foundry platform centers on what they call "the Ontology." This isn't a semantic layer in the dbt/MetricFlow sense. It's more ambitious -- and more opinionated.
From their docs: "The Ontology provides a well-defined system into which new information is modeled into a common language for the organization."
Traditional data lakes accumulate datasets and dashboards. Each project reinvents data integration. Context gets scattered across Confluence, Jira, Slack, and tribal knowledge.
Palantir's bet: invest upfront in modeling real-world entities and relationships, and everything else -- applications, AI, governance -- builds on that foundation.
Object Types: Schema definitions representing real-world entities. Not tables -- entities. A "Well" in an energy company. A "Customer" in retail. A "Flight" in logistics. Individual instances are objects; collections are object sets.
Properties: Characteristics that define what information can be stored. Like columns, but attached to business concepts rather than technical tables.
Link Types: Relationships between object types. Not just foreign keys -- semantic connections. A "Customer" places an "Order." An "Employee" manages a "Team." The link carries meaning.
Action Types: This is where it gets interesting. Actions define "a set of changes or edits to objects, property values, and links that a user can take at once." Side effects included -- notifications, webhooks, downstream triggers.
Functions: Code-based logic natively integrated with the ontology. Functions accept objects as inputs, read properties, operate across action types. The computation layer is bound to business concepts.
Interfaces: Abstraction patterns enabling polymorphism. Different object types sharing common structures can be modeled and interacted with consistently.
Most semantic layers describe data. Palantir's ontology describes the business.
The difference is subtle but critical. A semantic layer might define "Customer Lifetime Value" as a metric calculated from revenue tables. Palantir's ontology would define "Customer" as an entity with properties (name, segment, status), links to other entities (Orders, Support Tickets, Account Manager), and actions that can be performed (Flag for Churn Risk, Assign to Campaign, Archive).
The metric exists, but it's embedded in a richer context.
From the docs: the ontology functions as "a shared source of truth for decision-making and decision capture across a large organization."
This isn't just dashboards. Users can:
That last part matters. Traditional BI is read-only. Palantir's approach is read-write. Actions taken in applications update the ontology, creating feedback loops.
The platform abstracts technical concepts (datasets, joins, code) into domain-specific language. "Decision makers are not technical users comfortable with code or IT concepts." They interact using "standard terms they use every day."
This is the "name vs. know" problem Feynman identified. Most data infrastructure speaks in technical names (tables, columns, models). Palantir speaks in business names (Customers, Orders, Campaigns).
Rather than duplicating integration work for each project, "entire applications and use cases" build on shared data assets. Developers focus on business problems rather than "data wrangling."
The reuse compounds. A "Customer" object type defined for one application is available for all applications. The ontology is infrastructure, not project-specific.
Through configurable action types and writebacks, the ontology captures organizational decisions as data. "Organizations can learn from and improve their decision-making" while "insights captured by one user" inform others.
This addresses Jin's critique -- that decision rationale doesn't persist. In Palantir's system, decisions are first-class citizens, not comments in a Jira ticket.
The Andreessen Horowitz piece on "Palantirization" offers a sobering counterpoint.
Palantir achieves "category of one" status through simultaneous mastery of three things most companies can't combine:
The FDE model is key. These engineers spend months embedded in customer organizations, building on the platform while navigating political terrain. It's not SaaS. It's not consulting. It's something in between.
a16z identifies structural constraints:
Problem severity misalignment: Palantir solves billion-dollar problems (counterterrorism, national security). Most vertical SaaS addresses 10-20% efficiency gains -- insufficient ROI for months of embedded engineering.
Customer concentration requirements: The model works with concentrated, high-ACV bases. Fragmented customer bases create unsustainable customization burdens.
Services trap risk: Without strong platform underpinnings, companies devolve into "Accenture for X with a nicer front-end."
So here's the question: can we learn from Palantir's ontology without needing their resources?
The shift from "what tables exist" to "what entities exist" changes how applications interact with data. Instead of JOIN logic scattered across queries, relationships are declared once and reused.
Palantir's action types capture business operations -- the doing, not just the describing. When a user performs an action, it's logged, governed, and feeds back into the system.
Computation tied to business objects, not generic tables. A function that calculates churn risk operates on Customers, not on dim_customer_id columns.
Palantir's system supports version history -- what rules existed when a trace was logged, what the ontology looked like at a point in time.
reconstruct_knowledge_state(timestamp) returns active rules and procedure versions for any point in time.
Keith's half-joke: if we productized dbt-agent, could we position as a non-sinister Palantir?
The serious version: Palantir's moat isn't just the technology. It's the combination of:
What we could offer:
The positioning isn't "Palantir for analytics." It's "what Palantir figured out about enterprise semantics, made accessible."
Move beyond "canonical models" to explicit object type definitions:
object_types:
Customer:
properties:
- customer_id (primary_key)
- segment: string
- lifetime_value: currency_amount
- acquisition_channel: string
- status: enum[active, churned, prospect]
links:
- places: Order (one_to_many)
- has: SupportTicket (one_to_many)
- managed_by: Employee (many_to_one)
actions:
- flag_churn_risk
- assign_to_campaign
This isn't just documentation -- it's machine-readable context for agents.
Current approach: relationships are implicit in JOIN conditions.
Palantir approach: relationships are declared, named, and carry semantic meaning.
link_types:
places:
from: Customer
to: Order
cardinality: one_to_many
meaning: "Customer places Order"
contains:
from: Order
to: LineItem
cardinality: one_to_many
meaning: "Order contains LineItem"
Agents querying the ontology can navigate relationships by semantic meaning rather than foreign key guessing.
What if dbt-agent tracked:
These become training data for improving agent decisions.
| Palantir Capability | dbt-agent Status | Effort to Close |
|---|---|---|
| Object Types | Partial (canonical registry) | Medium |
| Properties | Partial (column standards) | Low |
| Link Types | Weak (implicit in SQL) | Medium |
| Action Types | Partial (decision traces) | Medium |
| Functions | Strong (36 skills) | Low |
| Interfaces | None | High |
| Temporal Versioning | Strong (PKO Phase 5) | Done |
| Object Views | Weak | Medium |
Palantir figured out something important: enterprises need ontologies, not just semantic layers. The difference is scope -- describing the business vs. describing the data.
Their execution requires resources most organizations don't have. But the patterns are extractable. Object types, link types, action capture, temporal context -- these can be implemented without forward-deployed engineers.
dbt-agent is already partway there. The PKO work, the decision traces, the canonical registry -- these are pieces of an ontology. The gap is making them explicit, connected, and agent-queryable.
The positioning opportunity isn't competing with Palantir. It's offering what Palantir offers -- genuine semantic infrastructure -- at a scale and cost that doesn't require defense contracts to justify.