Can Palantir monopolize the AI ​​industry?
An insider's look at everything Palantir has to offer!

Praise and strong recommendation from top experts in various fields!
· Kim Young-seop, CEO of KT
Jincheol Park, the first CEO of Palantir Korea
· Hyo-Seop Lee, Head of Public Sector at Palantir Korea
Big Data Doctor, 110,000 YouTuber

The golden age of AI has arrived.
Big tech companies are competitively investing astronomical amounts of money in bulk purchases of GPUs and building data centers.
However, no company has yet emerged that is making money by utilizing AI technology.
That is, until Palantir came into the picture.

This book systematically explores the potential and technologies of Palantir, a company that exists in an era where data holds real power.
The author is Korea's leading Palantir expert, possessing both theoretical and practical experience, having implemented Palantir systems in large corporations three times.
It provides detailed and realistic information for general readers investing in Palantir and practitioners in the AI ​​and data industries.
Readers will naturally gain an understanding of Palantir's technology, competitiveness, and unique data-utilization business.

Palantir isn't just a company with a skyrocketing stock price. It's a company that's building a massive data platform leveraging AI technology, and using it to fundamentally reshape every industry and organization on the planet.
It is also an innovative company that has achieved explosive growth without peer by building a clever business model.

Deeply understand Palantir's competitive edge
The only book that can predict the future of business

The author outlines Palantir's core technologies, including AIP, Ontology, Foundry, and Gotham, and discusses Palantir's present and future by deeply analyzing the cases of Korean companies that have adopted Palantir.
Additionally, we utilize the 'Deep Inside' special section to analyze Palantir's representative competitors, such as Databricks and Snowflake, and discuss what differentiates Palantir.

Palantir doesn't build Large Language Models (LLMs), doesn't develop AI technologies like deep learning, and doesn't build AI hardware using GPUs.
It simply uses AI technology to sort, select, and utilize data.
Palantir is not a company that unilaterally provides solutions.
We are a big data company that discovers essential problems in businesses and industries, extracts necessary data, and organizes it.
So how is Palantir responding to the AI ​​revolution? If you're curious about Palantir's competitiveness, product line, customers, and business model—which extends beyond government and institutional sales to the private sector—you should read this book.

What has driven Palantir's rapid growth? It's not simply the technology behind building AI models; it's the experience of working with clients in the field to define problems, connect data, and design and operate ontologies that translate these insights into actionable plans. This is why investors value Palantir so highly.


Palantir continues to demonstrate that AI is not just about creating cool dashboards and chatbots; it's also about "executable AI," playing a key role in solving real-world challenges, increasing efficiency, and enhancing competitiveness for businesses and governments.
---From "The Secret to Palantir Captivating Investors"

At Palantir, our on-site engineers are our most important asset.
However, properly nurturing and growing them is by no means easy.
Previously, Palantir engineers had to personally handle updates and operations for complex solutions, leaving them with little time to engage deeply with customers and their issues in the field.


However, the situation changed with the emergence of the automation and distribution platform ‘Apollo’.
With solution deployment and updates automated, engineers can now spend more time focused on working alongside customers to solve problems and design workable solutions.
Thanks to this, Palantir was able to achieve faster and more powerful execution capabilities.

---From "Staking Our Lives on Solving Customer Problems"

Above all, the most urgent task is to establish a proven Palantir engineer training system, similar to SAP's certification system.
We need to systematize the certification process based on core competencies such as ontology design, action definition, and workshop-based MVP implementation, and establish a structure where personnel who complete this process are recognized as basic engineers in the market.
Furthermore, if this certification is directly linked to individual competency development and reflected in a tangible compensation system (e.g., salary, project unit price), the ecosystem will grow on its own without a separate expansion strategy.


Palantir has been leading the market through its technological prowess, security, and strategic closedness.
However, we must now gradually open up this closed ecosystem and expand the practical adoption base by activating the local engineering ecosystem.
We need to seriously consider whether this is the most realistic and sustainable path to driving Palantir's revenue growth beyond technology dissemination.
---From "Open Platform, a Cradle of Engineer Training"

Palantir's engineers define themselves as "problem definers" rather than "problem solvers."
It is a philosophical definition that transcends language differences and permeates the entire project.
They adhered to the principle that customers should define their own problems and that the definition process itself should be driven by data.
In fact, Palantir's platform, Foundry, was designed to enhance customers' decision-making capabilities.
By aggregating and visualizing diverse data through the platform, we were able to objectively examine whether what we previously perceived as a "problem" was actually a core issue, or whether the data revealed a more fundamental problem.

---From "The Role of Palantir Dispatched Engineers"

Previously, I had focused my use of Palantir Foundry primarily on data analytics, decision support, and the resulting business impact.
In other words, the foundry was perceived as an ‘analysis tool that proves management performance.’
However, we gradually began to focus on the possibility of expanding into an 'operating system' based on Palantir Ontology, which became an opportunity to shift the operational paradigm itself beyond the technical introduction.
In reality, I realized that the bigger obstacle was not the limitations of the foundry platform, but rather my own limitations in setting boundaries, thinking, "This is where the usability ends."
It was not the limitations of technology, but the limitations of thinking that were the real constraints.
---From "Rediscovery of Ontology-Based Operating Systems"

Palantir is not just a data platform with great features; it's a solution whose contract structure itself must be designed as part of a digital transformation strategy.
Approaching Palantir as a point solution for a specific purpose can result in a system that is very expensive and doesn't deliver ROI.
Maximizing value and ROI is achieved by maximizing the structure of the enterprise contract and using it without restrictions across all business areas, not just specific areas.
Company E combined this with its internal expansion strategy to transform the number of users from a "cost burden" to an "opportunity to maximize efficiency," thereby leveraging it as a challenge point to simultaneously achieve both speed and depth in digital transformation.
---From "Why Palantir Signs Enterprise Contracts"

Why do some AIs only write poetry, while others can make decisions that shut down factories? The answer to this question lies in Palantir's AI strategy.
The difference becomes clear when we examine why Palantir chose not to develop its own LLM and how it is transforming AI from a mere "advisor" to an "action agent" working alongside the company to solve problems at its core. This isn't a question of superior LMM performance, but rather a difference in the philosophy behind AI and the accumulated DNA of problem-solving practices.

---From "AI Revolution, Palantir's Response Strategy"

Just as multiple apps run on a single operating system, various processes exchange data with each other and begin to function as a true operating system that solves more complex and high-level problems.

In conclusion, our approach was not about introducing advanced technologies per se.
To solve the 'real-world problems' that companies face, we focused on the 'process' of problem-solving.
And by digitally capturing (encoding) the process, we transformed the data into a living asset for actual operations.
This is the essence of Palantir's solution: moving beyond analysis to operations, beyond reporting to action.

---From the "Ontology-based 'Field Home' Project"