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Nuklai Explained: How Smart Data is Key to the Future of AI Agents
by
Nuklai

The future of artificial intelligence is full of exciting possibilities that modern businesses simply can’t ignore – and high-quality data is the key to unlocking them. 

AI agents promise to improve industries, enhance customer experiences, and make operations smoother. The secret to their success? High-quality data. 

Today, we’ll zoom in on how Nuklai’s smart data ecosystem is the perfect catalyst for helping businesses take charge of the AI Agent technology.

Unlocking the Potential of AI Agents

AI agents are specialized programs designed to perform tasks ranging from simple, rule-based actions to complex, autonomous decision-making processes. They are being successfully used across various sectors, including:

  • Healthcare: Helping with diagnostics, patient monitoring, and personalized treatment plans.
  • Finance: Improving fraud detection, algorithmic trading, and personalized financial advice.
  • Customer Service: Powering chatbots and virtual assistants that handle customer queries, improving response times and satisfaction.
  • Autonomous Vehicles: Enabling self-driving cars to make real-time decisions based on sensory input.

The common factor among these AI agents is their need for structured, high-quality data to make accurate, fact-based decisions.

What makes Nuklai’s data “smart data”?

Think of smart data on Nuklai like using Google Drive or Slack. Your files on your hard drive are isolated. But once you upload them to Google Drive, they become collaborative assets. Your coworkers can give feedback, use them for their own work, or combine them with other files to create new projects.

Nuklai’s smart data works similarly for company data. Using innovative blockchain technology, it opens data up for new insights, monetization, and opportunities across teams and collaborators. Nuklai also stores various sources of data, including metadata that deeply contextualizes any data sources, reference data, and more. This allows any dataset to be refined with more high quality information to unlock its true potential. After our clients' data is collected, organized, and made smart, businesses can start to innovate, train AI models, and create new revenue streams.

We collaborate with a network of Web2 and Web3 partners who extend their services to our clients’ smart data. These include AI training, business intelligence, and data sourcing, among many others. Smart data can increase revenue and efficiency for companies, providing new insights into their market and clients. Even data previously considered useless can become valuable smart data, bringing efficiency, profit, and new insights.

Why High-Quality Data Matters

Now that we have established what smart data is, consider an AI healthcare agent. To provide accurate diagnoses and treatment recommendations, it needs access to comprehensive medical records, pharmaceutical data, and real-time health metrics. 

Similarly, a finance AI agent requires a vast array of data points to effectively detect fraud or offer personalized financial advice. The effectiveness of AI agents depends on the quality and accessibility of the data they process.

Nuklai: Supporting the AI Future

Nuklai is at the center of this data-driven AI future. As a collaborative infrastructure provider for data ecosystems, Nuklai integrates community-driven data analysis with data from successful modern businesses. Our platform connects data enthusiasts and institutional partners, harnessing untapped data to create new revenue streams and drive innovation.

Breaking Down Data Silos with Nuklai

One of the biggest challenges in using AI agents is the scattered nature of data across applications, organizations, and industries. Nuklai addresses this by offering a user-friendly, streamlined approach to sharing, requesting, and evaluating data. This unified data ecosystem helps businesses gain valuable insights, improve processes, and find new business opportunities.

Imagine an AI agent analyzing the performance of a supply chain. Instead of sifting through dashboards or switching between systems, the agent retrieves relevant information or initiates required processes—all thanks to the seamless data integration provided by Nuklai.

How AI Agents Will Transform Businesses

With current advancements in AI, particularly in large language models (LLMs) that understand context and handle tasks, the potential for AI agents is boundless.

Whether it's automating customer service, optimizing supply chains, or enhancing financial services, AI agents can transform how businesses operate. However, this potential can only be realized with the right data infrastructure.

Nuklai helps businesses take control of their data, preparing it for innovative AI technologies and scaling the use of AI agents. By ensuring data is accessible, governed, and optimized, we pave the way for the next generation of AI-driven processes.

What would you like us to cover in upcoming editions of Nuklai Explained? Let us know on our Telegram. 

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About Nuklai

Nuklai is a collaborative infrastructure provider for data ecosystems, integrating community-driven data analysis with data from successful modern businesses. Our platform connects data enthusiasts and institutional partners to harness untapped data and create new revenue streams.

We aim to unify the fragmented data landscape by offering a user-friendly, streamlined approach to sharing, requesting, and evaluating data for valuable insights, enhancing processes, accelerating new business opportunities, and empowering next-generation large language models and AI.

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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]'