System Design Document
Palantir Platform Architecture Analysis
Document Type: Technical Reference
Version: 1.0
Date: February 2026
1. System Overview
1.1 Platform Architecture
Palantir's platform consists of four interconnected systems:
┌────────────────────────────────────────────────────────────────────┐
│ PALANTIR PLATFORM STACK │
├────────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────────────────────────────────────────────────────┐ │
│ │ USER APPLICATIONS │ │
│ │ Workshop │ Quiver │ Object Explorer │ Custom Apps │ Mobile │ │
│ └──────────────────────────────────────────────────────────────┘ │
│ ↕ │
│ ┌──────────────────────────────────────────────────────────────┐ │
│ │ AIP (AI PLATFORM) │ │
│ │ AIP Logic │ Agent Studio │ Evals │ LLM Orchestration │ │
│ └──────────────────────────────────────────────────────────────┘ │
│ ↕ │
│ ┌──────────────────────────────────────────────────────────────┐ │
│ │ ONTOLOGY LAYER │ │
│ │ Objects │ Properties │ Links │ Actions │ Functions │ Models │ │
│ └──────────────────────────────────────────────────────────────┘ │
│ ↕ │
│ ┌──────────────────────────────────────────────────────────────┐ │
│ │ FOUNDRY (DATA OPERATIONS) │ │
│ │ Data Integration │ Pipelines │ Transforms │ Storage │ │
│ └──────────────────────────────────────────────────────────────┘ │
│ ↕ │
│ ┌──────────────────────────────────────────────────────────────┐ │
│ │ APOLLO (DEPLOYMENT) │ │
│ │ Cloud │ On-Premise │ Edge │ Classified │ Air-Gapped │ │
│ └──────────────────────────────────────────────────────────────┘ │
│ │
└────────────────────────────────────────────────────────────────────┘
1.2 Platform Descriptions
Gotham — Defense and intelligence operations platform
- Counter-terrorism analytics
- Military targeting systems
- Intelligence community workflows
- Maven program (DoD AI)
Foundry — Commercial data operations platform
- Data integration across silos
- Operational workflows
- Enterprise analytics
- Supply chain optimization
AIP — Artificial Intelligence Platform
- LLM orchestration
- Agent development (AIP Agent Studio)
- AI workflow automation (AIP Logic)
- Model evaluation (AIP Evals)
Apollo — Continuous delivery system
- Deploy anywhere (cloud/on-prem/edge)
- Air-gapped network support
- Classified environment deployment
- Version management at scale
2. Ontology Architecture
2.1 Core Concepts
The Ontology is a semantic layer that maps data to real-world business concepts:
┌─────────────────────────────────────────────────────────────────┐
│ ONTOLOGY STRUCTURE │
├─────────────────────────────────────────────────────────────────┤
│ │
│ OBJECTS PROPERTIES LINKS │
│ ┌─────────┐ ┌───────────┐ ┌──────────────────┐ │
│ │ Aircraft│ ─── │ tail_num │ │ Aircraft ──────┐ │ │
│ │ │ ─── │ model │ │ │ │ │ │
│ │ │ ─── │ status │ │ ↓ ↓ │ │
│ └─────────┘ └───────────┘ │ Flight → Airport │ │
│ └──────────────────┘ │
│ ACTIONS FUNCTIONS INTERFACES │
│ ┌─────────┐ ┌───────────┐ ┌──────────────────┐ │
│ │ Schedule│ │ Forecast │ │ Asset │ │
│ │ Flight │ │ Demand │ │ ├── Aircraft │ │
│ │ │ │ │ │ ├── Vehicle │ │
│ └─────────┘ └───────────┘ │ └── Equipment │ │
│ └──────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘
2.2 Object Types
Objects are the fundamental entities in the Ontology:
// Example: Aircraft Object Type
interface Aircraft {
// Primary Key
aircraft_id: string;
// Properties
tail_number: string;
model: string;
manufacturer: string;
status: "active" | "maintenance" | "retired";
location_airport_id: string;
// Links (relationships)
scheduled_flights: Flight[];
maintenance_records: MaintenanceRecord[];
assigned_crew: CrewMember[];
// Actions (mutations)
schedule_maintenance(): MaintenanceOrder;
assign_to_flight(flight: Flight): Assignment;
update_status(status: string): void;
}
2.3 Ontology Metadata Service (OMS)
The OMS manages all ontological definitions:
| Component | Function |
|---|---|
| Object Type Registry | Defines all object types and schemas |
| Link Type Registry | Defines relationships between objects |
| Action Type Registry | Defines available mutations |
| Function Registry | Defines computed properties and logic |
| Interface Registry | Defines polymorphic object shapes |
2.4 Object Storage Architecture
Object Storage V2 (current architecture):
┌──────────────────────────────────────────────────────────────┐
│ OBJECT STORAGE V2 │
├──────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────────┐ ┌─────────────────────┐ │
│ │ Object Data Funnel │ │ Object Query │ │
│ │ (Indexing) │ │ Service │ │
│ │ │ │ (Querying) │ │
│ │ - Dataset sync │ │ - Search │ │
│ │ - Action writes │ │ - Filter │ │
│ │ - Incremental │ │ - Aggregate │ │
│ └─────────────────────┘ └─────────────────────┘ │
│ │ │ │
│ ↓ ↓ │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ Object Storage Database │ │
│ │ - Tens of billions of objects per type │ │
│ │ - Multi-datasource object types │ │
│ │ - Property-level permissions │ │
│ └──────────────────────────────────────────────────────┘ │
│ │
└──────────────────────────────────────────────────────────────┘
3. AIP Architecture
3.1 AIP Component Overview
┌────────────────────────────────────────────────────────────────┐
│ AIP PLATFORM │
├────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ AIP Logic │ │ Agent Studio│ │ AIP Evals │ │
│ │ │ │ │ │ │ │
│ │ AI-powered │ │ Autonomous │ │ Evaluation │ │
│ │ functions │ │ agents │ │ frameworks │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
│ │ │ │ │
│ └────────────────┼────────────────┘ │
│ ↓ │
│ ┌──────────────────────────────────────────────────────────┐ │
│ │ LLM ORCHESTRATION LAYER │ │
│ │ │ │
│ │ ┌────────────┐ ┌────────────┐ ┌────────────┐ │ │
│ │ │ Claude │ │ GPT-4 │ │ Custom │ │ │
│ │ │ │ │ │ │ Models │ │ │
│ │ └────────────┘ └────────────┘ └────────────┘ │ │
│ │ │ │
│ │ - Model selection per task │ │
│ │ - Prompt management │ │
│ │ - Response processing │ │
│ │ - Security & governance │ │
│ └──────────────────────────────────────────────────────────┘ │
│ │ │
│ ↓ │
│ ┌──────────────────────────────────────────────────────────┐ │
│ │ ONTOLOGY SDK │ │
│ │ - Access objects, properties, links │ │
│ │ - Execute actions │ │
│ │ - Call functions │ │
│ └──────────────────────────────────────────────────────────┘ │
│ │
└────────────────────────────────────────────────────────────────┘
3.2 AIP Logic
AIP Logic enables building AI-powered functions:
// Example: Demand Forecasting Function
@Function()
async function forecastDemand(
product: Product,
timeHorizon: number
): Promise<DemandForecast> {
// 1. Gather historical data from Ontology
const historicalSales = await product.getSalesHistory(365);
const marketFactors = await getMarketFactors(product.category);
// 2. Call LLM for analysis
const analysis = await AIP.completion({
model: "claude-sonnet",
prompt: buildForecastPrompt(historicalSales, marketFactors),
outputFormat: "structured_json"
});
// 3. Return forecast linked to Ontology
return new DemandForecast({
product: product,
predictions: analysis.predictions,
confidence: analysis.confidence,
factors: analysis.contributingFactors
});
}
3.3 Agent Studio
Agent Studio enables building autonomous agents:
// Example: Supply Chain Disruption Agent
class SupplyChainAgent {
@Agent()
async handleDisruption(trigger: DisruptionEvent): Promise<ResolutionPlan> {
// 1. Assess impact
const impactedOrders = await this.assessImpact(trigger);
// 2. Generate alternatives
const alternatives = await this.generateAlternatives(impactedOrders);
// 3. Select optimal resolution
const resolution = await AIP.reason({
context: { trigger, impactedOrders, alternatives },
objective: "Minimize delivery delay and cost",
constraints: this.businessConstraints
});
// 4. Execute resolution (with human checkpoint if needed)
if (resolution.requiresApproval) {
await this.requestHumanApproval(resolution);
}
return resolution.execute();
}
}
3.4 AIP Security Model
┌─────────────────────────────────────────────────────────────────┐
│ AIP SECURITY │
├─────────────────────────────────────────────────────────────────┤
│ │
│ DATA GOVERNANCE │
│ ┌───────────────────────────────────────────────────────────┐ │
│ │ - LLMs never trained on customer data │ │
│ │ - All prompts processed in customer's environment │ │
│ │ - No data leaves security boundary │ │
│ └───────────────────────────────────────────────────────────┘ │
│ │
│ ACCESS CONTROL │
│ ┌───────────────────────────────────────────────────────────┐ │
│ │ - Ontology permissions apply to AI queries │ │
│ │ - AI sees only what user has access to │ │
│ │ - Property-level security inheritance │ │
│ └───────────────────────────────────────────────────────────┘ │
│ │
│ AUDIT & LINEAGE │
│ ┌───────────────────────────────────────────────────────────┐ │
│ │ - Complete audit trail for all AI operations │ │
│ │ - Explainability for AI decisions │ │
│ │ - Historical lineage for all changes │ │
│ └───────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘
4. Apollo Architecture
4.1 Deployment Environments
Apollo enables deployment across all environments:
| Environment | Characteristics | Example Use Case |
|---|---|---|
| Cloud | SaaS, multi-tenant | Commercial analytics |
| On-Premise | Customer datacenter | Banking, insurance |
| Edge | Low-latency, local | Manufacturing, IoT |
| Classified | Air-gapped, secure | Defense, intelligence |
| Sovereign | Nation-specific | Government AI |
4.2 Apollo Architecture
┌────────────────────────────────────────────────────────────────┐
│ APOLLO DEPLOYMENT │
├────────────────────────────────────────────────────────────────┤
│ │
│ ┌───────────────────────────────────────────────────────────┐│
│ │ APOLLO CONTROL PLANE ││
│ │ - Version management ││
│ │ - Configuration management ││
│ │ - Health monitoring ││
│ │ - Rollback capabilities ││
│ └───────────────────────────────────────────────────────────┘│
│ │ │
│ ┌─────────────────┼─────────────────┐ │
│ ↓ ↓ ↓ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ AWS │ │ On-Prem │ │ Edge │ │
│ │ Region A │ │ Datacenter │ │ Device │ │
│ │ │ │ │ │ │ │
│ │ Foundry │ │ Foundry │ │ Foundry │ │
│ │ AIP │ │ AIP │ │ (Lite) │ │
│ │ Ontology │ │ Ontology │ │ │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
│ │
└────────────────────────────────────────────────────────────────┘
4.3 Continuous Delivery
Apollo provides GitOps-style continuous delivery:
# Example: Apollo Deployment Configuration
deployment:
name: "production-foundry"
environment: "aws-us-east-1"
components:
- name: "foundry-core"
version: "2025.12.3"
replicas: 5
- name: "aip-runtime"
version: "2026.01.2"
replicas: 3
- name: "ontology-service"
version: "2025.11.8"
replicas: 5
rollout:
strategy: "canary"
canary_percentage: 10
evaluation_period: "1h"
auto_promote: true
5. Data Integration Patterns
5.1 Data Sources
Foundry integrates with diverse data sources:
| Category | Sources |
|---|---|
| Databases | PostgreSQL, Oracle, SQL Server, MySQL |
| Data Lakes | S3, ADLS, GCS, HDFS |
| Streaming | Kafka, Kinesis, Event Hubs |
| APIs | REST, GraphQL, gRPC |
| Files | CSV, JSON, Parquet, Excel |
| SaaS | Salesforce, SAP, Workday |
5.2 Data Pipeline Architecture
┌────────────────────────────────────────────────────────────────┐
│ DATA PIPELINE │
├────────────────────────────────────────────────────────────────┤
│ │
│ SOURCE → CONNECT → TRANSFORM → STORE → SERVE │
│ │
│ ┌──────┐ ┌────────────┐ ┌───────────┐ ┌──────────┐ │
│ │ ERP │→│ Connector │→│ Transform │→│ Dataset │ │
│ │ CRM │→│ │→│ (Code) │→│ │ │
│ │ IoT │→│ │→│ │→│ │ │
│ └──────┘ └────────────┘ └───────────┘ └──────────┘ │
│ ↓ │
│ ┌───────────────┐ │
│ │ Ontology │ │
│ │ Mapping │ │
│ └───────────────┘ │
│ ↓ │
│ ┌───────────────┐ │
│ │ Objects │ │
│ └───────────────┘ │
│ │
└────────────────────────────────────────────────────────────────┘
6. Application Layer
6.1 Workshop
Workshop is the low-code application builder:
| Feature | Description |
|---|---|
| Widgets | Drag-and-drop UI components |
| Variables | State management |
| Events | User interaction handling |
| Actions | Ontology mutations |
| Functions | Custom logic execution |
6.2 Quiver
Quiver is the spreadsheet-like analytics tool:
| Feature | Description |
|---|---|
| Object Sets | Query Ontology objects |
| Aggregations | Group, pivot, summarize |
| Visualizations | Charts, graphs, maps |
| Exports | Excel, CSV, reports |
6.3 Object Explorer
Object Explorer provides entity-centric browsing:
| Feature | Description |
|---|---|
| Object Views | Reusable entity displays |
| Link Navigation | Traverse relationships |
| Property Editing | Inline updates |
| Action Execution | Run mutations |
7. Security Architecture
7.1 Multi-Layer Security
┌────────────────────────────────────────────────────────────────┐
│ SECURITY LAYERS │
├────────────────────────────────────────────────────────────────┤
│ │
│ LAYER 1: INFRASTRUCTURE │
│ ┌───────────────────────────────────────────────────────────┐│
│ │ - Encryption at rest (AES-256) ││
│ │ - Encryption in transit (TLS 1.3) ││
│ │ - Network isolation ││
│ │ - VPC peering / Private Link ││
│ └───────────────────────────────────────────────────────────┘│
│ │
│ LAYER 2: PLATFORM │
│ ┌───────────────────────────────────────────────────────────┐│
│ │ - Identity federation (SAML, OIDC) ││
│ │ - Role-based access control ││
│ │ - Multi-factor authentication ││
│ │ - Session management ││
│ └───────────────────────────────────────────────────────────┘│
│ │
│ LAYER 3: DATA │
│ ┌───────────────────────────────────────────────────────────┐│
│ │ - Dataset-level permissions ││
│ │ - Row-level security ││
│ │ - Column-level masking ││
│ │ - Object-level Ontology permissions ││
│ └───────────────────────────────────────────────────────────┘│
│ │
│ LAYER 4: AUDIT │
│ ┌───────────────────────────────────────────────────────────┐│
│ │ - Complete audit trail ││
│ │ - Data lineage ││
│ │ - Access logging ││
│ │ - Change tracking ││
│ └───────────────────────────────────────────────────────────┘│
│ │
└────────────────────────────────────────────────────────────────┘
7.2 Compliance Certifications
| Certification | Status |
|---|---|
| SOC 2 Type II | ✓ |
| FedRAMP High | ✓ |
| HIPAA | ✓ |
| ISO 27001 | ✓ |
| IL4/IL5/IL6 | ✓ |
8. Performance Characteristics
8.1 Scale Benchmarks
| Metric | Capability |
|---|---|
| Objects per type | Tens of billions |
| Concurrent users | Thousands |
| Query latency | Sub-second for most queries |
| Pipeline throughput | Petabytes processed daily |
| API requests | Millions per second |
8.2 Availability
| SLA | Target |
|---|---|
| Platform uptime | 99.9%+ |
| Data pipeline reliability | 99.99%+ |
| API availability | 99.95%+ |
System Design Document v1.0 — February 2026