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Table of Contents

• a. Key Points

• b. Company Intro

  • Palantir (PLTR) 101:

  • AIP 101:

• c. Demand

• d. Profits & Margins

• e. Balance Sheet

• f. Guidance & Valuation

• e. Call & Release

• h. Take

a. Key Points

• Great results and great guidance.

• 6 straight quarters of Y/Y growth accelerating while margins explode higher. Good combo.

• Its artificial intelligence platform (AIP) is thriving & driving real commercial value.

• Nice reacceleration in the USA government business. Europe remains challenged.

• Rule of 40 score was over 80.

b. Company Intro

Palantir (PLTR) 101:

Palantir is a software company that helps customers get the most out of their structured and unstructured data. Like many others, it pulls from years of AI/ML work to automate insight-gleaning. It utilizes complex neural networks to power anomaly detection, trend forecasting and natural language processing. It works openly with many database vendors for interoperable storage and low-latency querying, and offers its own tools there too.

Overall, PLTR frees clients to conjoin disparate data sources while utilizing its software to uncover ideas that manual analytics and legacy competition cannot derive. It gives customers a birds-eye view of their operations, with detailed suggestions to help optimize products and workflows. It also lets companies freely test digital twins (a process called Ontology) in zero-stakes environments to actually understand what works and what doesn’t. It’s a similar idea to split testing, with deeper, more applicable and more outcome-driven learnings.

Revenue is neatly split into two buckets – “government” and “commercial.” Government clients use its Gotham product platform, while commercial clients use its Foundry product platform. With Gotham, Palantir routinely builds custom use cases for individual government clients. Foundry was built to be more malleable, with more pre-built app integrations and developer kits available. That diminishes the need to conduct custom builds for every single private enterprise. Still, it does materially more custom building than a typical B2B software firm will.

It also seamlessly leverages the commercial platform to cater to industry-specific needs. By-industry models are intuitively named “micro-models.” These are smaller and boast sector-specific use cases with specialized regulatory compliance help. A financial services model from Palantir, for example, may assess credit risk or fraud detection.

Palantir Apollo provides continuous integration and continuous delivery (CI/CD) to automate software package building and deployment. It’s a foundational piece of the firm’s ability to collect, utilize and drive value from broad data ingestion. It’s also how Palantir can help operationalize these learnings to introduce valuable products.

AIP 101:

In the realm of GenAI and Agentic AI, Palantir is not playing the game of building the biggest model or buying the most GPUs to have the largest infrastructure footprint. This is not a hyperscaler like AWS. It simply gives clients the tools and integrations needed to build apps for their own, more granular use cases. It also has some products for model customization, but the app layer is where this company really shines.

Its most exciting product here is called Artificial Intelligence Platform (AIP). This powers automation and AI app-building.  The company compares AIP to what public cloud vendors did for compute and workload modernization. AWS, Azure, Google and Oracle provided the environment, tools, storage, security and maintenance needed to grow needed compute capacity without managing it yourself. This made migrations and adoption the rational decision. AIP attempts to do the same thing in terms of pushing enterprises to adopt GenAI. This fully manages expedited product building for client deployment. It allows for open collaboration between software developers, data scientists, models and project managers to ensure effective work. It directly supports Foundry and Gotham by uplifting and augmenting potential use cases to “extract value from GenAI models.” And it does so in a quite compelling way that can craft tools on a by-customer basis.

As leadership will tell you, AIP isn’t just another dime-per-dozen chatbot. It’s an aggregator of data, tools and services needed to actually build valuable apps and to embrace GenAI. Considering the lack of finite and structured end products stemming from AIP, I think it helps to hear about some examples of what clients are doing with it:

• Turning inbound emails into automated inventory decisions;

• Automated healthcare documentation for claims.

• The Department of Defense (DoD) is using it to shrink app creation time from hours to seconds.

• Lowe’s cut overdue task rate by 75%.

• General Mills now saves $14 million a year in expenses with AIP.

• Associated Materials raised on-time delivery rate from 40%-90%.

• Trinity Rail cut $30 million in annual CapEx.

• Mount Sinai found $13 million in new revenue opportunities.

The list goes on and on. AIP is where jumbled data, processes and ideas turn into the operationalized, actionable creation of GenAI products.

c. Demand

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