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Use Case: Real-Time Data Feed for Real World Assets (RWAs)
by
Nuklai

The tokenization of Real World Assets (RWAs) presents significant opportunities for the decentralized finance (DeFi) industry. But it also poses complex challenges, especially in data management and regulatory compliance. 

These pitfalls also affect the maintenance of transparency and trust in the DeFi sector. This case study explores how Nuklai addresses these challenges. It also explains how we provide an efficient and fair asset tokenization process.

First, Nuklai integrates and standardizes data from diverse sources. Then, it ensures an accurate and transparent representation of assets. Nuklai simplifies these processes for issuers by lowering their technical complexities and costs.

After that, we provide investors with a reliable framework for well-informed decision-making. Additionally, our data privacy compliance approach respects transparency and privacy concerns. Simply put, we enhance the integrity of the tokenized assets.

Nuklai also improves the tokenization process by increasing transparency in the valuation process. We enable the tokenization and transparent valuation of a wide range of assets. Then, we foster a better investment environment within the DeFi industry.

This case study highlights our platform's role in asset tokenization. It shows our ability to create an accurate and transparent future for the digital finance ecosystem.

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The Challenge: Navigating the Complexities of Tokenizing RWAs, Data Integration, and Investor Trust

The tokenization of real-world assets (RWAs) is a promising use case within DeFi industry. Yet, despite its evident benefits, it faces a multitude of challenges. These obstacles hamper RWAs' widespread implementation and adoption.

For starters, the industry needs to overcome regulatory, legal, and custodial challenges. Then, it needs to address the issues affecting these tokenized assets' trustworthiness and transparency, which are major concerns for asset issuers and investors alike.

To illustrate these challenges, let us first delve into the asset tokenization process. Let's say you are embarking on appraising a tokenized asset. First, you should comprehend its underlying representation. 

To do that, you must have confidence in the data sources used or required within the valuation process. Your data sources must be accurate at the asset’s first issue and through regular updates. 

These updates must incorporate relevant information that could impact the asset's value over time. Consistency ensures a constant, reliable valuation.

Let us, for instance, consider the tokenization process of a real estate development project. First, the building's digital twin gathers extensive data from its early stages. 

Architects and engineers also contribute a wealth of information to the digital twin in Building Information Modeling (BIM).

This comprehensive data is invaluable to investors. It offers detailed insights into the building's components. 

This data also enables the prediction of maintenance needs. It supports a proactive approach to renovations and repairs to preserve the property's value. Moreover, it ensures that the building’s value does not diminish over time.

This data also helps inform decisions about the development's rental income potential. Alongside that, it helps to optimize the building's spatial efficiency. The strategic configuring or reconfiguring of the space maximizes its utility and value.

Thus, the BIM model has a comprehensive overview of the property. That's why it is a powerful tool that can give investors an on-target assessment of the property’s resale value.

Moreover, developers can integrate its rich, detailed data into the framework of a tokenized asset. Doing so will enhance the asset’s credibility and value proposition.

Second, a property's value is also influenced by external factors. These factors include the demographic profile of the surrounding neighborhood. Local crime statistics and ongoing developments in the area also affect real estate value. 

Market dynamics, like the number of comparable real estate projects nearby, are also critical valuation data. Other data points are the duration from listing to sale and average transaction prices. The occupancy rates and trends in rental prices are also crucial. 

Integrating a data feed with all this information can help determine the real value of a tokenized property. 

The challenge, however, in aggregating all this data lies in its storage across various data sources. Often, this data is in different formats, making the consolidation process technically demanding. 

Incomplete data access can obscure the full picture for investors in the tokenized asset, hurting the anticipated return on investment.

So, any RWA project should have comprehensive and seamless data integration when developing a project. This is vital in maintaining the accuracy and reliability of the investment's valuation.

Nevertheless, these data challenges above are not confined to real estate. They extend to other assets like cars that need a detailed maintenance history. Vehicle valuation also relies on manufacturing data for potential recalls. 

Insurance documentation for damage reports is also vital for the car industry RWA project. Similarly, carbon credits demand up-to-date offsetting data to mitigate the risk of greenwashing. 

All this data should be easy to share and standardized for these diverse assets. Data scientists should also combine it for in-depth analysis before its integration into a project.

That said, data obscurity is only one of the challenges projects face when tokenizing assets. Projects, for instance, cannot disclose certain data aspects to the public. For example, a prospective car buyer might want to know a vehicle's ownership and maintenance history.

But, the previous owners' identities are irrelevant in this situation. Platforms should consider, in detail, what data to include in a tokenized asset. This ensures compliance with data privacy regulations.

So, tokenizing an asset is much more than the tokenization process itself. There are many factors entwined in an asset's value that need careful consideration. 

Access to detailed and accurate data is key to making well-informed investor decisions. Looking ahead, data management and compliance with privacy regulations will continue to play a crucial role in shaping the future of asset tokenization. These aspects will influence the approaches to valuing and investing in these assets.

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Objective: Supporting Asset Tokenization with a Unified Data Platform: Enhancing Transparency and Trust in DeFi

When tokenizing real-world assets, Nuklai recognizes the critical need for accurate, in-depth data. We have set a goal of being the go-to platform for such data feeds.

We will supply your RWA project with in-depth information about the underlying assets. In doing so, we empower issuers of tokenized assets to provide a full and continuous picture of the asset's condition at issuance and resale. 

For instance, in real estate, data originates from varied sources. Municipal authorities might provide demographic information about a neighborhood.

Then, national or private entities record real estate transaction data. Building owners can contribute extensive details from the building's digital model thanks to BIM. 

A unified data exchange and integration platform is vital as this data springs from diverse organizations. Due to constant updates, these databases also mutate fast. 

Our advanced data connector technology seamlessly links on-premises, cloud, or simple file data. It is vital in linking data sources across different organizations and lowers the barriers that hinder collaboration in asset tokenization. 

Our data standardization engine also harmonizes the data into a unified and predictable format. It tackles the challenge of varied data sources and formats, facilitating easy access and use.

Besides, Nuklai understands the complexities involved in adhering to data regulatory compliance. As a result, we ensure that data providers have complete control over the data they share. 

Our platform empowers them to exclude sensitive or private parts within the data as they see fit to uphold data transparency. 

Altogether, our approach guarantees compliance with data protection and privacy regulations like GDPR. It ensures that RWA data handling processes are responsible and respectful, protecting privacy.

Our platform's flexible approach also democratizes access to shared data intelligence. It helps smaller companies or enterprises to reap benefits from these solutions. 

Gathering data for a single real estate project demands significant technical expertise. Under normal circumstances, it would cost a lot of effort and expense.

But our platform simplifies this process. With us, smaller companies can achieve similar results as large enterprises with less effort and cost demands. 

So, Nuklai levels the playing field, creating a fair business landscape. Here, businesses can unlock new value through the strategic use of data. 

It is vital to transform data management for RWAs by supplying essential, up-to-date information. This process facilitates validation and trust. Moreover, it enables precise tracking of an asset's value across a wide spectrum of assets, whether art, real estate, or vehicles. 

Furthermore, if issuers of tokenized assets improve their underlying data, investors can compare and comprehend them at a deeper level. By doing so, investors will base their decisions on the most accurate and relevant data available. 

Such improvements mark a significant stride forward in asset tokenization. They promise a more informed and reliable future for investors and asset issuers in this evolving landscape. 

Javascript
Python
PHP
const ApiUrl = "https://api.nukl.ai/api/public/v1/datasets/:datasetId/queries";
const ApiKey = "[API_KEY]";
const DatasetId = "[DATASET_ID]";

const headers = {
  "Content-Type": "application/json",
  'authentication': ApiKey
}

ApiUrl = "https://api.nukl.ai/api/public/v1/datasets/:datasetId/queries"
ApiKey = "[API_KEY]"
DatasetId = "[DATASET_ID]"

headers = {
  "Content-Type": "application/json",
  "authentication": ApiKey
}

$ApiUrl = "https://api.nukl.ai/api/public/v1/datasets/:datasetId/queries";
$ApiKey = "[API_KEY]";
$DatasetId = "[DATASET_ID]";

$headers = [
  "Content-Type: application/json",
  "authentication: $ApiKey"
];
Javascript
Python
PHP
// @dataset represents your dataset rows as a table
const body = {
  sqlQuery: "select * from @dataset limit 5",
}

@dataset represents your dataset rows as a table
body = {
  "sqlQuery": "select * from @dataset limit 5"
}

// @dataset represents your dataset rows as a table
$body = [
  "sqlQuery" => "select * from @dataset limit 5"
];
Javascript
Python
PHP
cURL
const ApiUrl = "https://api.nukl.ai/api/public/v1/datasets/:datasetId/queries";
const ApiKey = "[API_KEY]";
const DatasetId = "[DATASET_ID]";

const headers = {
  "Content-Type": "application/json",
  'authentication': ApiKey
}

// @dataset represents your dataset rows as a table
const body = {
  sqlQuery: "select * from @dataset limit 5",
}

// make request
fetch(ApiUrl.replace(':datasetId', DatasetId), {
  method: "POST",
  headers: headers,
  body: JSON.stringify(body), // convert to json object
})
  .then((response) => response.json())
  .then((data) => {
    console.log(data);
  })
  .catch((error) => {
    console.error(error);
  });

import requests
import json

ApiUrl = "https://api.nukl.ai/api/public/v1/datasets/:datasetId/queries"
ApiKey = "[API_KEY]"
DatasetId = "[DATASET_ID]"

headers = {
  "Content-Type": "application/json",
  "authentication": ApiKey
}

# @dataset represents your dataset rows as a table
body = {
  "sqlQuery": "select * from @dataset limit 5"
}

# make request
url = ApiUrl.replace(':datasetId', DatasetId)
try:
  response = requests.post(url, headers=headers, data=json.dumps(body))
  data = response.json()
  print(data)
except requests.RequestException as error:
  print(f"Error: {error}")

$ApiUrl = "https://api.nukl.ai/api/public/v1/datasets/:datasetId/queries";
$ApiKey = "[API_KEY]";
$DatasetId = "[DATASET_ID]";

$headers = [
  "Content-Type: application/json",
  "authentication: $ApiKey"
];

// @dataset represents your dataset rows as a table
$body = [
  "sqlQuery" => "select * from @dataset limit 5"
];

// make request
$ch = curl_init(str_replace(':datasetId', $DatasetId, $ApiUrl));

curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($body)); // convert to json object
curl_setopt($ch, CURLOPT_HTTPHEADER, $headers);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, TRUE);

$result = curl_exec($ch);
curl_close($ch);

echo $result;

curl -X POST 'https://api.nukl.ai/api/public/v1/datasets/[DATASET_ID]/queries' \
  -H 'Content-Type: application/json' \
  -H 'authentication: [API_KEY]' \
  -d '{"sqlQuery":"select * from @dataset limit 5"}'
Javascript
Python
PHP
cURL
const ApiUrl = "https://api.nukl.ai/api/public/v1/datasets/:datasetId/queries/:jobId";
const ApiKey = "[API_KEY]";
const DatasetId = "[DATASET_ID]";
const JobId = "[JOB_ID]"; // retrieved from /queries request

const headers = {
  "Content-Type": "application/json",
  'authentication': ApiKey
}

// make request
fetch(ApiUrl.replace(':datasetId', DatasetId).replace(':jobId', JobId), {
  method: "GET",
  headers: headers
})
  .then((response) => response.json())
  .then((data) => {
    console.log(data);
  })
  .catch((error) => {
    console.error(error);
  });

import requests

ApiUrl = "https://api.nukl.ai/api/public/v1/datasets/:datasetId/queries/:jobId"
ApiKey = "[API_KEY]"
DatasetId = "[DATASET_ID]"
JobId = "[JOB_ID]"  # retrieved from /queries request

headers = {
  "Content-Type": "application/json",
  "authentication": ApiKey
}

# make request
url = ApiUrl.replace(':datasetId', DatasetId).replace(':jobId', JobId)
try:
  response = requests.get(url, headers=headers)
  data = response.json()
  print(data)
except requests.RequestException as error:
  print(f"Error: {error}")

$ApiUrl = "https://api.nukl.ai/api/public/v1/datasets/:datasetId/queries/:jobId";
$ApiKey = "[API_KEY]";
$DatasetId = "[DATASET_ID]";
$JobId = "[JOB_ID]"; // retrieved from /queries request

$headers = [
  "Content-Type: application/json",
  "authentication: $ApiKey"
];

// @dataset represents your dataset rows as a table
$body = [
  "sqlQuery" => "select * from @dataset limit 5"
];

// make request
$ch = curl_init(str_replace(array(':datasetId', ':jobId'), array($DatasetId, $JobId), $ApiUrl));

curl_setopt($ch, CURLOPT_HTTPHEADER, $headers);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, TRUE);

$result = curl_exec($ch);
curl_close($ch);

echo $result;

curl 'https://api.nukl.ai/api/public/v1/datasets/[DATASET_ID]/queries/[JOB_ID]' \
  -H 'Content-Type: application/json' \
  -H 'authentication: [API_KEY]'