Palantir Hands-On Bootcamp: Property Maintenance Operations
Build an AI-powered property maintenance scheduling system from scratch using Palantir Foundry. In 22 lessons spanning a Foundation Track and an Advanced Track, you will upload data, build pipelines, model an Ontology, create Workshop dashboards, deploy AIP agents, build external apps with OSDK, and tie it all together with predictive maintenance — covering the full Foundry stack with bilingual content (English + 中文).
Premium content
Upgrade to premium to access all lessons, tutorials, and hands-on exercises.
Lesson Overview
Lesson 1: The Palantir Story — From Seeing Stones to Enterprise AI
Meet Palantir Technologies — the company behind Foundry. Learn how a startup named after Tolkien's seeing stones grew from intelligence work into the world's leading data operating system for enterprises.
Lesson 2: What is Palantir Foundry?
Understand what Palantir Foundry is, how it differs from traditional data tools, and meet the rental property management scenario we will build throughout this bootcamp.
Lesson 3: Uploading & Exploring Data
Learn how to create a project folder, upload CSV datasets into Foundry, navigate the file browser, and explore dataset schemas and previews.
Lesson 4: Pipeline Builder & Transformations
Use Foundry Pipeline Builder to visually join, filter, rename, add computed columns (expressions and CASE logic), and aggregate data — with a complete operations reference covering all Pipeline Builder node types.
Lesson 5: What is the Ontology?
Understand the paradigm shift from rows in tables to objects that mirror the real world, and why the Ontology is the foundation of everything in Palantir Foundry.
Lesson 6: Creating Object Types
Create three Ontology object types — Property, Work Order, and Technician — by mapping your CSV datasets to structured objects in Foundry.
Lesson 7: Links, Actions & Object Explorer
Connect your object types with link types, build action types with validation rules, and explore your fully connected Ontology in Object Explorer.
Lesson 8: Introduction to Workshop
Learn what Workshop is and how it enables building powerful applications on top of the Ontology without writing frontend code.
Lesson 9: Building the Maintenance Dashboard
Build a complete interactive maintenance dashboard with property list, filtered work orders, detail view, and action buttons using Workshop.
Lesson 10: Dashboard Charts & Analytics
Add interactive charts and analytics to the maintenance dashboard, including bar charts, line charts, pie charts, and a polished multi-tab layout.
Lesson 11: AIP: AI Meets Your Data
Introduce Palantir AIP and demonstrate how operational AI queries your Ontology through natural language using AIP Agent Studio.
Lesson 12: Building an AIP Agent
Build a Property Manager Assistant agent in Agent Studio that can read Ontology data and trigger Actions through natural language commands.
Lesson 13: AIP Logic, Automate & Full Integration
Create an AIP Logic function for auto-classification, set up Automate triggers, embed the agent in Workshop, and walk through the complete end-to-end operational flow.
Lesson 14: Foundry Developer Experience (FDE)
Learn how to use Code Repositories to replicate Pipeline Builder transforms in TypeScript, build a custom work order routing function, write unit tests, and publish versioned code alongside visual pipelines.
Lesson 15: Data Lineage & Governance
Explore Foundry lineage graphs to trace data from raw CSV to AIP agent, perform impact analysis, configure health checks, use branching for safe changes, and set up granular permissions for compliance.
Lesson 16: OSDK — Build External Apps with the Ontology
Build a tenant-facing React portal using the Ontology SDK (OSDK). Learn to register a third-party app, scaffold a React + OSDK project, query Ontology objects with type-safe TypeScript, apply actions, and deploy an external web app that consumes your Foundry data.
Lesson 17: Capstone Project — Full-Stack Foundry
Apply everything from Lessons 1-16 in a comprehensive capstone: upload 6 realistic datasets, build pipelines, model 5 object types, create a scheduling dashboard, deploy an AIP agent, run predictive maintenance scoring in FDE, build an OSDK tenant portal, and govern the entire system.
Lesson 18: Work Order Scheduling in Workshop
Build a 3-panel Workshop module for scheduling work orders — a detail panel showing work order metadata, a ranked appointment slot list with scoring metrics, and a Mapbox map pinning asset locations. Learn ontology design for scheduling, cross-panel variable wiring, conditional formatting, and map layers.
Lesson 19: VROOM + OSRM Routing Engine
Deploy VROOM and OSRM locally via Docker, map Foundry ontology data to VROOM concepts (vehicles, jobs, skills, time windows, priorities), call the VROOM API to produce optimized technician routes, and write results back to the ontology. Compare automated routing quality against manual dispatch.
Lesson 20: Automated Scheduling Pipeline + Capacity Engine
Build a production batch optimization pipeline (Code Workflow → Lambda → VROOM → writeback), implement constraint validation, create a capacity engine that computes bookable slots from raw capacity minus booked minus emergency reserves, handle mid-day emergency re-optimization, and deploy a quality metrics dashboard with shadow-mode testing.
Lesson 21: OSDK Mobile Apps — Technician & Resident Portals
Build two OSDK-powered mobile applications: a Technician Portal showing today's optimized route with turn-by-turn navigation, real-time status updates, and push notifications; and a Resident Portal for viewing available slots, booking appointments, tracking technician ETA, and submitting maintenance requests. Connect both apps to the Foundry ontology for real-time data flow.
Lesson 22: Capstone 2 — Autonomous Dispatch System
Tie together VROOM routing, the capacity engine, mobile apps, and the scheduling dashboard into a fully autonomous dispatch system. Build a comprehensive testing strategy with scenario libraries, implement graduated rollout (shadow → assisted → supervised → autonomous), and add AI enhancements including demand forecasting, dynamic emergency reserves, and auto-dispatch rules.