Palantir-Foundry-AIP-Program

End-to-end Palantir Foundry & AIP program — pipelines, ontology, Workshop UI, RAG application

🏗️ Palantir Foundry & AIP — Entry Program

Completed the Analyticorex Palantir Foundry Foundations Entry Program — a structured, hands-on evaluation program covering data pipelines, ontology modeling, AIP Logic, and end-to-end application development on Palantir Foundry.

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📌 What I Built

🔁 Data Pipeline (Pipeline Builder + Code Repositories)

• Ingested unstructured PDF documents into Foundry
• Built transformation pipeline to extract, clean, and chunk text
• Replicated visual Pipeline Builder logic using Code Repositories for reproducibility
• Added data health checks — null filtering at ingestion to prevent bad data reaching the LLM

🧠 RAG Application (AIP Logic + Vector Embeddings)

• Generated vector embeddings from document chunks for semantic search
• Integrated with LLM via AIP Logic for natural language querying
• Built grounding logic: system responds "I don't know" for out-of-scope queries — preventing hallucination
• Enterprise reliability standard: answers are citation-backed, not fabricated

💻 Workshop UI (Frontend Application)

• Built user-facing application in Palantir Workshop
• Plain-English query interface for non-technical users
• Custom filters to reduce information overload
• Application backed by governed ontology data — not just raw queries

🗂️ Ontology Modeling

• Defined object types and properties from transformed datasets
• Established relationships between objects
• Extended beyond tutorial — created additional ontology objects via custom transformations

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🛠️ Tech Stack

Component	Tool
Data Pipelines	Palantir Pipeline Builder, Code Repositories
AI / RAG	Palantir AIP Logic, LLMs, Vector Embeddings
Frontend	Palantir Workshop
Data Modeling	Palantir Ontology
Data Quality	Foundry Data Health Checks

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📋 Program Steps Completed

Step	Topic	Status
0	Enrollment & Tracking	✅
1	Environment Setup (AIP Developer Portal)	✅
2	Data Exploration in Contour	✅
3	Building First Pipeline	✅
4	Transforming Data with Code Repositories	✅
5	Creating First Ontology	✅
6	End-to-End Application Deep Dive	✅
7	AIP End-to-End Workflow Speedrun	✅
8	Exploration & Extension	✅
9	Final Demo	✅

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💡 Key Learnings

Data Quality First — Adding null filtering directly in the pipeline ensures only clean data reaches the AI model. This prevents errors downstream and reduces LLM API token waste.

LEFT JOIN thinking in Ontology — Just like preserving zero-sales SKUs in SQL with LEFT JOIN, ontology relationships must account for objects with no linked data — otherwise insights disappear silently.

Grounding > Accuracy — A system that says "I don't know" is more trustworthy than one that always gives an answer. Enterprise users need to be able to rely on outputs.

Code Repositories > Pipeline Builder for production — Visual pipelines are great for exploration. Code repos are what you use when the system needs to be reproducible, testable, and maintained.

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👤 Author

Vijay Ahirwar
B.S. Data Science & Applications — IIT Madras
LinkedIn | GitHub