3c Enterprise

Latest (26.0901): Making steady progress on section 2.2 PAL demos (speedruns).

This Enterprise section focuses on Palantir Foundry as a concrete, hands-on example of a modern enterprise platform for data integration, ontology, analytics, applications, AI assistance, and governance.

• This page reorg’d 26.0826 (simplified)
• I will be in HK/Jiangsu from 7 September - 10 October and will not be updating this site.

TOC:

• 1 Concepts
• 2 Getting started hands-on
• 3 Demo sources and docs

1 Concepts

• 1.1 Why Palantir
• 1.2 Why all the fuss about AI in the enterprise (and how Foundry can mitigate these problems)
• 1.3 Basic Foundry workflow

1.1 Why Palantir

My focus (for now) is on Palantir Foundry because:

• Palantir is a market leader and offers a generous Foundry free trial.
• Foundry makes the enterprise visible and governable by bringing data from many systems into a common operational model that can be analyzed, monitored, and updated.
• AI is used as a helpful engineering tool, not a replacement for human judgment. Because LLMs are probabilistic, their access and actions must be controlled, verified, approved, and logged.
• Foundry’s built-in AI assistant (FDE) makes hands-on self-study practical, including completing and troubleshooting the demos.
• Foundry represents the logical culmination of ZiptieAI: (2) neural networks → (2b) models → (3) agents → (3b) workflows → (3c) enterprise platforms.

1.2 Why all the fuss about AI in the enterprise (and how Foundry can mitigate these problems)

For the diagram below:

• My original title: The fantasy world – (left) a crystal ball (called a “palantir” (“seeing stone”) in The Lord of the Rings) and (right) AGI (a myth that digital circuits can host intelligence). Palantir Foundry represents the real world – An enterprise system that provides the infrastructure and safeguards so that AI can be a practical “helpful assistant” (not super-human intelligence).
• GPT’s strongly suggested “professional” title: The trusted wizard (left) using a crystal ball (“palantir” or “seeing stone” in The Lord of the Rings) and (right) modern AI running on digital hardware. Both promise enhanced visibility and insight, although one belongs to fantasy and the other (AGI) belongs to engineering.
• My current title: Palantir provides proven engineering (the crystal ball is just a metaphor, marketing genius); those selling AGI are selling a fantasy that can cause big problems in an enterprise.

1.3 Basic Foundry workflow

The following diagram shows the basic PAL workflow.

• 1a Data source. Raw data from enterprise systems. For example: CSV files, Databases, ERP systems, CRM systems, Sensors, External APIs.
• 1b Pipeline. Transforms raw data into clean, structured, and consistent datasets. For example: cleaning, joining, filtering, aggregating, validation.
• 3a Ontology read. Applications and users read enterprise data through the ontology. The ontology provides a common operational model of the enterprise.
• 3b Ontology writeback. Applications, workflows, and users update enterprise systems through the ontology. For example: change status, create ticket, approve request, update record.
• 4 Analysis. Code, queries, dashboards, rules, AI, and other logic analyze the ontology data. Purpose: discover information, detect problems, and support decisions.
• 5 UI / App. User-facing applications. For example: dashboards, workflows, forms, operational applications, AI assistants.
• 6 Security / Governance. Controls who can see data, modify data, run workflows, and use AI. For example: permissions, auditing, approvals, version control, lineage, logging, AI.
• About AI. AI can appear in many layers: pipeline transforms, model integration, ontology functions, analysis, UI assistants, and governance monitoring. AI should be treated as a helpful assistant, not the center of the system and not the final authority. The responsible pattern is: AI suggests, deterministic logic verifies where possible, humans approve important actions, and the platform logs and governs the workflow.

The ontology is the operational heart of Foundry. Most applications, analysis, workflows, and AI interact with enterprise data through the ontology rather than directly through the raw datasets.

The three crystal balls represent three layers of governance. Foundry controls

• Crystal ball #1 — Data governance. Controls how raw enterprise data becomes trusted operational data.
  • Examples: ingestion, transformation, validation, lineage.
  • This is the boundary between: Data Source, Pipeline, Ontology.
• Crystal ball #2 — Operational governance. Controls how applications, users, workflows, and AI interact with the enterprise.
  • Examples: ontology permissions, workflow permissions, write-back controls, approval processes.
  • This is the boundary around: Ontology, Analysis, UI, Writeback.
• Crystal ball #3 — Global governance. Controls the entire platform.
  • Examples: security, auditing, version control, branching, deployment, compliance.
  • This is the layer that sits above everything else.

2 Getting started hands-on

• 2.1 Palantir free tier sign up
• 2.2 PAL demos. These are less complicated, but you start from 0, and getting a working demo at the end requires a lot of skill (including debugging).
• 2.3 PAL (installable) examples. These are complicated, but they have an autoinstaller that allows you to start with a (usually) working version that you can modify.

2.1 Palantir free tier sign up

If you cant get a trial version of PAL running, then there’s no reason to read further. You will not understand PAL by just reading about it.

• I did not record how I signed up, and its not a process I can repeat. But I can tell you the following:
  • You must be in a country that PAL supports (thats only a few countries).
  • You can NOT use a VPN.
  • You must have a passport or some picture ID of good enough quality for facial recognition verification. My passport is almost 10 years old, but it still worked.
  • You need a camera on your PC to take a pic of yourself. PAL will compare your ID and the pic.
  • I think you need a mobile phone number (and email).
• It was a fairly long process, but very professionally done (I was impressed).
  
  
  
  

2.2 PAL demos (speedruns)

Step-by-step start to finish demos. Original PAL demos with modified ZAI workflows and documentation. Organized (WIP) as a cohesive progressive study plan.

Main workflow diagram (simplified) for demo D1 (for details about this workflow see D1 workflow chats).

A diagram of the configuration steps for demo D2 (I spent days on my initial attempts to recreate D2 without this diagram; with this diagram its fairly simple; its even simpler if you do it in 3 separate phases as described in the latest version of the docx and if you simply ignore the shaded out parts of the demo)

2.3 PAL examples

PAL “examples” are complete (usually more complicated) and have an installer that allows you to start with a (usually) working version that you can modify. ZAI adds a few extra steps:

• Install a second (experimental) example
• Modify that example at your own pace
  

PAL example (left, center) and my own build (right) (3c.1b)

Workflow diagrams for examples are usually a bit more complicted than for demos.

3 Demo sources and docs

• 3.1 Palantir Foundry sources
• 3.2 Demo plan (outdated)
• 3.3 Demo docs creation/maintenence strategy

3.1 Palantir Foundry sources

• See the chat with FDE about demo sources.
• palantir.com/docs/foundry. Has a lot of great content. But its probably not where you want to initially start. Note: The most interesting thing on this page for me was the diagram below (I added numbering that matches the workflow diagram in 1.2 above).
  
  
• Several Youtube sites. The sites I viewed were quite good, but not really suited for an absolute beginner. Documents are the best for absolute beginners (and they tend to be more up-to-date and can serve as the template for your own docs).
• build.palantir.com has links to examples/demos organized by type: AI Starter Pack, Document Suite, Reference Implementation, etc.
  
  
• learn.palantir.com has links to examples/demo organized by role: Data Engineer, App Dev, etc.
  
  
• [my-org].palantirfoundry.com/workspace/marketplace is a Foundry internal app for finding examples/demos organized by 9 “stores”.
  
  
  

3.2 Demo plan (outdated)

I spent the first month doing the demos I thought looked interesting from the sources listed in 2.2 above (lab notes (MS.Word docx files) are available for each example/demo on the GDrive). Those demos are organized (by Palantir) by role and project types. But what I really needed was a (straight, start to finish) learning path for a generalist beginner. So I decided I’d just start doing demos and after a while I’d eventually figure out a learning path. I still don’t have a learning path. But I do have a rough draft (OUTDATED) learning plan #699_planning_.docx

3.3 Demo docs creation/maintenence strategy

Demo doc creation/maintenance process (AI makes this process practical and reliable)

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