Demystifying Palantir (PLTR) for a new investor

Background:

This article is an attempt to breakdown what Palantir does in a simple easy to understand manner. This article is not going to discuss the financial metrics or valuation of the company which is discussed here (Palantir Operating profitability and expense trends). I have also reviewed the numerous contracts that Palantir has won (as of Sept '21) since they became public. This article can be accessed here (A network of Palantir contracts). This piece is more about what it does in a hopefully simple to understand manner.

The word Palantir refers to a fictional magical artifact. J. R. R. Tolkien (of Lord of the Rings fame) imagined this to be an indestructible crystal sphere. Main use for this object was for communication and to see events in other parts of world (past or future). The name of Palantir Technologies is apt in its description of it capabilities. The ability to see what other can't see. 

So lets dig into what some of Palantir Technology does.

In its most basic function, 

Palantir's programs use computers and software to analyze information, identify and learn from the problem and devise solutions.

Palantir does some of this in part by collecting data from multiple sources, cleaning and making it usable, comparable. Finding patterns using visualization techniques. Then using the information using human intelligence or Machine learning (ML)/Artificial intelligence (AI) to solve the problem.

1) Helps with Data integration and  translation.

Palantir helps organizations to build a solid, high quality database from different sources. Palantir will translate the integrated data interactions in easy to read format. This of course comes with high degree of customization.

2) Helps with Data interconnections.

Data is often spread across the enterprise. Models derived from these disconnected data are difficult to comprehend. 'Micro Models' is a new approach developed by Palantir to address this problem. Micro Models addresses parts of a large problems. It then combines those solved parts back for a solution. Thus, it breaks a ‘BIG’ problem into smaller problems and solves all the smaller problems. Palantir software then chains those solutions together, to solve, the ‘BIG’ problem. The standardized solutions chain thus leads to interconnected workflows. 

3) Helps to Model Objectives.

Business objectives are clear targets and goals for a company to achieve. They may also include strategies achieve the said tasks.

Figure: Palantir keeps improving on the processes leading to objectives being met and improved

The objective model (OM) collects goals, training, evaluation data, result and metrics. This is in context of the underlying logic behind the objective. Thus, OM is a hub, receiving, transmitting and analyzing information about goal achievement. As  with micro-models, Palantir binds individual models to a larger design. This design powers several different workflows. The design has a 'write back' property to improve accuracy of future uses.  Thus, Palantir keeps refining the process, to ensure goal accomplishment and goal improvement.

4) Capable of building and deploying AI/ML Infrastructure

Palantir combines the data foundation (#1) with end-to-end AI/ML deployment infrastructure.  Think of these a AI/ML codes 'widgets' (each doing a specific task), placed at various points in a task workflow. Palantir allows scientists to decide where to place them. They also know how the widgets interact and check their function.  

5) Provides Artificial intelligence for Internet of Things and Edge computing.

Many electronic devices a.k.a 'things' use internet to stay connected  (e.g a smart thermostat, TV etc)  . This internet 'of things' (edge) usually communicates with a Cloud computing (center). Decisions go back to the IoT devices. This can be slow.  Palantir's 'Edge AI' powers decision at the edge detaching the cloud. Thus processing data is faster, with minimal lag, for immediate use. This Palantir tech is being used in ground vehicles, watercraft and spacecraft.

Applications:

The applications of such a technology are rather unlimited. Think of Palantir like a Swiss army knife a multipurpose tool for any number of applications.  I have highlighted some of the applications below

1) Solving financial crimes

Financial crime is complex. There are many moving parts, including the bad actors who are responsible for the crime. Also involved are intermediaries (banks/financial institutions) and victims (individuals and companies).   Criminals are also using newer and smarter ways to commit crimes. Hackers have held companies at ransom demanding cryptocurrencies. These innumerable data points make it difficult to track a financial crime. Conventional approach has been a 'fixed set' of rules with one lead leading to next, which leads to the next. This is labor and time intensive. Palantir provides tools to join billions of records to analyze data fast. Palantir’s enables linking of customers, networks, and counter parties. All this is compliant with privacy and security standards per Palantir. This system can allow banks to move away from the legacy systems. Palantir guides analysts through the investigation to focus on the most important risks. With this high level 30,000 foot view analysts can multitask the investigation. The analyst can addend the generated report to be filed with the authorities.

2) In automotive industry and racing

Palantir is working on its data analytics with Ferrari the car company. The vast number of data points are being used to make the cars safer, faster.

3) Use in Data protection

While data is being used for analysis, Palantir ensures that the data is secure. This is per the regulations that govern data privacy. This compliance with regulatory bodies allows continued use of data in correct manner.

4) Use in Defense and military applications

Palantir has worked for a long time with US armed forces. Its use is in principle the same.  Palantir integrates data external sensors/sources through a single point of access. This reveals unseen links across their entire universe of data. Palantir use if in Data Integration, Resource Planning, Finance & Accounting and Military Readiness.

5) Applications also include using Palantir software in energy sector.  

Energy companies deal with fluctuations in energy demand, transition to green energy. Palantir assists with these concerns by digitizing data and using it analytics.

6) Use in financial planning and analysis

Palantir queries, unifies, creates a usable database for financial management

Palantir has been used for analyzing troves of financial data to streamline expenses, budgets etc. It can query multiple disconnected systems for data, unify it into one harmonized database. This then lets it create a foundation database that finance professionals can use to analyze portfolio, audit finances, track budgets, reprogram funds and reallocate capital. This has been successfully applied to study contract renewals in US army.

 

7) Use in health sciences and medicine

 

Figure: How Palantir can transform medical discovery. The platform is flexible enough to be positioned at every sequential step of medical scientific process, enhancing efficiency and speed.

Palantir's foundry is a powerful tool that is being used currently for all aspects of medical progress. From basic sciences, pre-clinical studies including drug design and development, to developing cell clones Foundry can assist with data management. It also accelerates medical discovery by assisting in phase 0, I, II, III and IV clinical trials. Finally, data can be analyzed from post marketing information making Foundry indispensable for medical discovery. 

Conclusion:

Palantir's software (called Foundry & Gotham)  is a versatile tool which can be used in any industry, seeking solutions to problems which had previously been difficult problems to solve. The program does this by its unique approach of visualizing and finding patterns in data allowing either machines or humans to interact with the patterns for finding solutions. The sheer versatility and application is evidenced by its numerous contracts it has with industry in variety of sectors. 

Disclaimer: I own shares of Palantir. Information has been sourced from public websites.

Global-E (GLBE) has an amazing mechanism to succeed

If you want to know what is to follow without reading  further, it is this: Global-E (GLBE) has an amazing mechanism to succeed. I am glad I own shares of this company. 

I was most interested in how the company works and how it is going to keep the flywheel(s) spinning. Using the information present in form F1 that was filed with SEC by the company (Sep-07-2021), I generated a unsupervised semantic network model. The figure is shown below,

Figure 1: An unsupervised network analysis of Global-E (GLBE) business overview based on SEC F-1 filing data

Figure legend:

1. Merchant side concepts are shown as green nodes to the left
2. Shopper side concepts are shown in pink nodes on right.'
3. The edges, (lines) connecting the nodes are relations that were abstracted from the SEC filing.

Comments:
1. GLBE has plenty to offer the merchants (green square shape on left), which makes it attractive for a merchant to sell on their platform. Success of the merchant is in turn a measure of success of GLBE
2. GLBE also makes it extremely easy for shoppers to buy (Pink square shape on right) across boundaries with all the facilities it has to offer (see figure 1, pink nodes in capital letters as well as figure 2 below, this is from their F-1 filing)

Figure 2: From Global-E F1 filing with SEC Sept 2021

MOST IMPORTANTLY, 

Two Key observations are revealed in the network figure (this is why I love this type of modeling, it allows to read between the lines).

• Observation #1: Offloading complexities occupies a central role in the workings of this set up. This is the central tenet of Global-E's workings and this is the lynch pin around which their success rests. And they are doing a great job at it. 
• Observation #2: The interface between merchant and shopper is right at the payments, pricing and duties/taxes nodes. The alignment of this interface (payments, pricing, duties/taxes), is the crucial solution of cross border commerce. This is the under pinning platform for the 'off loading of complexities' central node.

This is a fantastic network model, that the computational network algorithm has generated. This is purely based on textual and conceptual information in the filing. I am amazed at the sheer simplicity and extreme robustness of the the model which reflects the values of the company. I am pleased I am invested in the company and would not mind adding more soon!

Disclaimer: I own shares of GLBE. Data obtained from F1 filing, SEC and public sources.

Mercadolibre (MELI), a digital footprint more than just Latin America (LATAM)

I was wondering about the reach of some of the continent specific e-commerce platforms. For example, Mercado Libre (ticker: MELI) in Latin America, and Sea limited (ticker: SE) in Asia and their effect on cross border commerce. I did a tree map on Global-E few days ago (Included at the end of the article as supplemental material) which show cases it strengths, on how it is making cross border commerce easy. Mercado Libre is huge in South America. It also has national websites catering to specific countries such as mercadolibre.com.pe for Peru and mercadolibre.com.ni for Nicaragua. It has done an excellent job of 'personalizing' the websites to the countries they cater.

One way to look at extent and reach of a website is to look at server traffic and countries of origination to those sites. I obtained data from Cloudflare to see where the visitors to Mercado Libre are coming from. This gives a good indication of the visitors to the site and a proxy of its reach. 

Method: I obtained the domains for Mercado Libre and its country specific website links. Then using this information obtained the website statistics from Cloudflare.  Cloudflare is a web infrastructure and website security company that provides content delivery network and DDoS mitigation services. The data was limited to last 30 days and provided top 10 locations for visitors to the respective sites. I then cross tabulated data for all of the country specific domains to see where are people visiting from on Mercado Libre's website. Given this was a multivariable data, it is represented as network map which shows the connections in entirety. The figure is shown below.

Figure 1: Country of origin of visitors to Mercado Libre's websites

Results

1. Blue nodes are country specific websites for Mercado Libre, the key for the countries are Argentina (.ar), Bolivia (.bo), Brasil (.br), Chile (.ch), Colombia (.co), Costa Rica (.cr), Dominicana (.do), Ecuador (.ec), Guatemala (.gt), Honduras (.hn), México (.mx), Nicaragua (.ni), Panamá (pa), Paraguay (py), Perú (.pe), Salvador(.sv),Uruguay (.uy) and Venezuela (.ve) (arrow #1)
2. The location country of the visitors are nodes colored with gradient green to red and sized. Thus the visitors from the country with highest frequency will be represented by largest nodes that are green while those that are not as frquent will by small and red. For example, in the figure Brazil, Argentina and United States are the largest nodes (most visitors, see dotted shape arrow #2) while Japan and HK (e.g. #3 arrow ) are smallest.
3. The distance of the node to the domains is based on the frequency of visitors, thus if the nodes are closer to the country node, it means that the visitors from that country were most frequent. For example the Chile node is closest to .cl since most visitors to .cl were from Chile (see arrow #4)

Discussion

Following are my personal observations

1. That most visitors to Mercado Libre's websites are from Brazil and Argentina and respective LATAM countries is expected, what was not expected there was also considerable website traffic from United States,  Europe (Netherlands, France, UK). This tells me that the MELI footprint extends far beyond LATAM and that visitors to the site are coming from outside of South America as well.
2. There were some interesting findings as well, there we significantly higher number of visitors from Netherlands and France to Mercado Libres Nicaragua, Honduras and Salvador websites.
3. There were also visitors from Asian countries like Japan, South Korea, Hong Kong albeit is much lower frequency.

This analysis shows that the visitors to Mercado Libre are more than LATAM countries and the digital foot print extends to US, UK, Europe and even Asia. The analysis has some limitation though for example, the look back period is only 30 days, there could be other patterns is the look back period was extended further. Cloudflare's data only allows for a 7 or 30 day look back. The data is limited to only top 10 countries. Visitors from a country may not mean customers, we do not know the intention or the deliberation of the visitors. While it is possible that some one may accidentally end up on their website, the proportion of those would be fairly small.  Additionally, with the advent of usage of VPN services, the origin of visitors could be spoofed but again this may be expected to be a minuscule proportion and no way to get around this. 

Conclusions: Mercado Libre, Inc. is an Argentine company headquartered in Buenos Aires,  that operates online marketplaces dedicated to e-commerce and online auctions, including mercadolibre.com.  The company operates in South America and has has operations in Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Mexico, Spain, Ecuador, Guatemala, Honduras, Peru, Panama, Uruguay, and Venezuela. Thought its operations are seemigly limited to South America, its digital foot print extends well beyond its borders.

Disclaimer: I own shares of Mercado Libre (MELI) and am invested in the company. The data was obtained from public sources.

Addendum:

Figure 2. Global-E, GLBE a cross boundary commerce platform