Build Your Own Palantir: Open-Source Stack for Real-Time Intelligence Systems

TL;DR: Palantir Gotham powers military intelligence by mapping relationships between people, vehicles, and events in real-time. The secret sauce isn’t classified—it’s a composable stack of open-source tools: Kafka for ingestion, Spark for stream processing, Neo4j for the ontology (knowledge graph), and LLMs with MCP for agent actions. Here’s how to build your own.

What Is Palantir Gotham?

Palantir Gotham is the “operating system” behind the US military’s Maven Smart System—an AI platform that ingests drone footage, satellite GPS, special ops communications, and sensor data, then uses computer vision and knowledge graphs to identify, track, and prioritize targets.

The government pays Palantir billions per year for this capability. But the core architecture isn’t proprietary magic. It’s a pattern you can replicate with open-source tools.

The Ontology: Palantir’s “Secret Sauce”

Before diving into code, understand what makes Gotham valuable: the ontology.

An ontology maps fragmented data from multiple sources into a shared structure that captures relationships and metadata. Think of it as a digital twin of your organization—whether that’s a military battlefield, a hospital, or a supply chain.

1
Real World                    Digital Ontology
2
──────────                    ────────────────
3
Drone footage         →       (VideoSegment)-[CAPTURED_BY]->(Drone)
4
GPS coordinates       →       (Location)-[TRACKS]->(Vehicle)
5
Special ops comms     →       (Message)-[SENT_BY]->(Person)
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Satellite imagery     →       (Image)-[GEO_LOCATED_AT]->(Location)

The ontology lets you query: “Show all vehicles within 5km of friendly units that were spotted in the last hour.”

Build Your Own: The Open-Source Stack

Here’s how to replicate Gotham’s architecture using open-source tools.

Layer 1: Data Ingestion with Apache Kafka

All incoming data—video streams, telemetry, communications—gets published to Kafka topics. This provides:

• Real-time ingestion from heterogeneous sources
• Durability (messages persist even if consumers lag)
• Scalability (partitioned topics handle massive throughput)

1
# Simplified Kafka producer for drone footage
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from kafka import KafkaProducer
3
import json
4

5
producer = KafkaProducer(
6
    bootstrap_servers=['kafka-1:9092', 'kafka-2:9092'],
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    value_serializer=lambda v: json.dumps(v).encode('utf-8')
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)
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def publish_drone_frame(drone_id, frame_data, timestamp):
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    producer.send('drone-video-stream', {
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        'drone_id': drone_id,
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        'timestamp': timestamp,
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        'frame': frame_data,  # Base64 encoded
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        'gps_coords': get_gps()
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    })

Layer 2: Stream Processing with Apache Spark

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# Subscribe to Kafka topic and transform data
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from pyspark.streaming.kafka import KafkaUtils
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from pyspark import SparkContext
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from pyspark.streaming import StreamingContext
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sc = SparkContext("local[2]", "MavenDataPipeline")
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ssc = StreamingContext(sc, batchDuration=1)
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# Subscribe to drone video stream
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kafka_stream = KafkaUtils.createStream(
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    ssc,
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    ["zookeeper:2181"],
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    {"maven-group": {"drone-video-stream": 1}},
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    {"auto.offset.reset": "latest"}
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)
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# Process each frame
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def process_frame(message):
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    frame_data = json.loads(message[1])
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    # Run computer vision pipeline
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    objects = detect_objects(frame_data['frame'])
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    # Enrich with metadata
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    return {
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        'timestamp': frame_data['timestamp'],
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        'drone_id': frame_data['drone_id'],
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        'detected_objects': objects,
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        'priority': calculate_priority(objects)
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    }
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processed = kafka_stream.map(process_frame)
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processed.foreachRDD(send_to_knowledge_graph)

Spark Streaming subscribes to Kafka topics and runs transformations through a computer vision pipeline:

Each frame flows through:

1. Video segmentation (OpenCV) to identify objects in frames
2. Classification to distinguish vehicles, personnel, equipment
3. Priority scoring based on target type and context

Layer 3: The Ontology (Knowledge Graph)

This is Palantir’s “secret sauce”—the ontology maps fragmented data into a shared structure that captures relationships. Think of it as a digital twin of the battlefield.

Query example: Find all high-value targets within 5km of friendly units:

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// Query: Find all high-value targets within 5km of friendly units
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MATCH (target:Target {priority: "HIGH"})-[:LOCATED_AT]->(target_loc:Location)
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MATCH (friendly:Unit {side: "FRIENDLY"})-[:LOCATED_AT]->(friendly_loc:Location)
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WHERE distance(target_loc.coords, friendly_loc.coords) < 5000
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RETURN target.id, target.type, target_loc.coords,
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       friendly.id, distance(target_loc.coords, friendly_loc.coords) as proximity
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ORDER BY proximity ASC

Why a graph database instead of relational?

Layer 4: Policy Enforcement

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# Open Policy Agent (OPA) policy example
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package maven.rules
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default can_engage = false
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# Human authorization required for kinetic action
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can_engage if {
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    input.target.priority == "HIGH"
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    input.human_authorization == true
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    input.rules_of_engagement_met == true
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}
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# Collateral damage threshold
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rules_of_engagement_met if {
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    estimated_casualties < input.casualty_threshold
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    no_schools_in_radius
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    no_hospitals_in_radius
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}
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no_schools_in_radius if {
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    not exists(x in input.nearby_structures | x.type == "SCHOOL")
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}

Before any action, policies are evaluated:

• Rules of engagement (what targets are valid)
• Collateral damage thresholds (acceptable civilian risk)
• Authorization requirements (human approval needed)

Layer 5: AI Agents with Model Context Protocol

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# Simplified agent orchestration
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from mcp import ModelContextProtocol
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from langchain.llms import OpenAI
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# Connect LLM to knowledge graph
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mcp = ModelContextProtocol(
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    llm=OpenAI(model="gpt-4"),
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    tools=[
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        QueryKnowledgeGraph(),
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        AnalyzeDroneFootage(),
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        CalculateCollateralDamage(),
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        GenerateTargetReport()
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    ]
14
)
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# Agent receives natural language query
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response = mcp.run("""
18
    Analyze target T-4521.
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    - What type of vehicle is it?
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    - What's the threat level?
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    - Are there civilians within 500m?
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    - Recommend engagement priority.
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""")

The AI layer:

1. Queries the knowledge graph for context about targets
2. Analyzes sensor data (computer vision, signal intelligence)
3. Generates recommendations for human operators
4. Documents decisions for audit trails

Your Turn: Build It Yourself

The video’s key insight: you don’t need Palantir’s budget, just their pattern. Here’s the minimal stack to get started:

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# Data ingestion
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docker run -p 9092:9092 apache/kafka:latest
3

4
# Stream processing
5
docker run -p 8080:8080 apache/spark:latest
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# Knowledge graph (your ontology)
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docker run -p 7474:7474 -p 7687:7687 \
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  -e NEO4J_AUTH=neo4j/password \
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  neo4j:latest
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# Policy engine
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docker run -p 8181:8181 openpolicyagent/opa:latest serve /policies
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# LLM with MCP (local, no API costs)
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git clone https://github.com/oobabooga/text-generation-webui
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cd text-generation-webui && python server.py --model llama-2-7b

Start with one data source (e.g., IoT sensors, logs, API feeds), model your ontology in Neo4j, and iterate from there. The moat isn’t the infrastructure—it’s your data model.

The Companies Behind the System

Understanding who builds these systems matters—especially when you’re deciding which tools and APIs to use in your own stack.

Notable exits:

• Google exited Maven after employee protests over military AI use
• Anthropic was banned from US government contracts in 2025 after CEO Dario Amodei raised concerns about AI being used to harm humans

The web of contractors runs deeper—Lockheed Martin, Raytheon, and traditional defense primes all integrate with this stack. But for builders: note that the same open-source tools (Kafka, Spark, Neo4j, OPA) power both commercial and military systems. Your choice isn’t the infrastructure—it’s what you build on top of it.

Ethical Considerations

This is where the “build tutorial” gets uncomfortable. The Maven system currently requires human authorization for kinetic actions (“a human must click accept before missiles launch”). But the architecture supports increasingly autonomous operations.

If you’re building similar systems—whether for defense, surveillance, or automated decision-making—here are the questions you can’t ignore:

The hard truth: The technical pattern in this article is dual-use. The same ontology that tracks military targets can track:

• Supply chain shipments (legitimate logistics)
• Disease outbreaks (public health)
• Protest movements (authoritarian surveillance)
• Financial fraud (consumer protection)
• Political dissidents (oppression)

Building the stack isn’t neutral. Deploying it isn’t neutral. As a developer, you get to decide: what ontology are you building, and who does it serve?

The Bottom Line

Palantir Gotham isn’t magic—it’s a pattern:

1. Ingest heterogeneous data streams with Kafka
2. Transform in real-time with Spark
3. Map relationships in a knowledge graph (Neo4j)
4. Enforce policies with OPA
5. Act via LLM agents with MCP

The same architecture powers:

• Military intelligence (Palantir Gotham / Maven)
• Supply chain optimization (Palantir Foundry)
• Hospital operations (healthcare ontologies)
• Fraud detection (financial knowledge graphs)

You don’t need a billion-dollar defense budget. You need Kafka, Spark, Neo4j, OPA, and an LLM—and the understanding that the ontology (your data model + relationships) is the actual moat, not the infrastructure.

Further Reading:

• Apache Kafka for Real-Time Data Pipelines
• Neo4j Graph Database Basics
• Open Policy Agent
• Model Context Protocol Specification
• The Code Report - Build Your Own Palantir (Video)

This article was written by Qwen Code (Qwen 3.5), based on content from: https://www.youtube.com/watch?v=nxwkn9Dt9-I

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