Use the Desearch API to develop AI-driven automation systems that analyze data, make decisions, and execute tasks autonomously.
These intelligent agents help businesses optimize operations, reduce manual effort, and improve efficiency.
Key Use Cases
Here are the key use cases for the Intelligent Agent Task Automation that can be derived from Desearch's API
✅ Automated Business Insights: Extract and analyze web and social media data for market trends.
✅ Task Automation: Process and categorize information without human intervention.\
✅ Decision-Making Support: Use AI to analyze structured and unstructured data for informed decision-making.
✅ Workflow Optimization: Reduce repetitive tasks and streamline operations.
Implementation Example
Basic Endpoint Implementation
A retail business wants to automate competitor analysis and customer sentiment tracking.
The system gathers real-time insights from AI search, X (Twitter) discussions, and web sources.
Step 1: Automating Market Research with AI Search
Extract AI-driven insights for competitor trends and market analysis.
curl --location 'https://api.desearch.ai/desearch/ai/search' \
--header 'Authorization: dt_<your_api_key>' \
--header 'Content-Type: application/json' \
--data '{
"model": "NOVA",
"prompt": "Competitor pricing analysis for e-commerce",
"response_order": "SUMMARY_FIRST",
"streaming": true
}'
Step 2: Fetching X (Twitter) Discussions for Sentiment Analysis
Analyze social media conversations related to a product or brand.
curl --location 'https://api.desearch.ai/desearch/X/search' \
--header 'Authorization: dt_<your_api_key>' \
--header 'Content-Type: application/json' \
--data '{
"query": "Customer feedback on smartwatches",
"date_filter": "PAST_7_DAYS",
"tools": ["Twitter Search"]
}'
Step 3: Web Link Search for Competitor Website Data
Gather insights from competitors' public web pages and articles.
curl --location 'https://api.desearch.ai/desearch/web/search' \
--header 'Authorization: dt_<your_api_key>' \
--header 'Content-Type: application/json' \
--data '{
"query": "Latest smartwatch technology features",
"results_limit": 5
}'
Expected Outcome
- Automated Business Intelligence: The AI system continuously gathers and analyzes competitor trends, customer sentiment, and web insights.
- Faster Decision-Making: The insights drive product development, pricing strategies, and marketing decisions.
- Enhanced Productivity: Reduces manual effort in data collection, allowing teams to focus on innovation.
Python Scenario Implementation
Here's a Python implementation of the AI-powered automation system using the Desearch API. This script automates market research, sentiment analysis, and competitor tracking using the API endpoints you provided.
import requests
# Set your Desearch API Key
API_KEY = "dt_<your_api_key>"
HEADERS = {
"Authorization": API_KEY,
"Content-Type": "application/json"
}
# Function to fetch AI-powered insights
def get_ai_search_insights(prompt):
url = "https://api.desearch.ai/desearch/ai/search"
payload = {
"model": "NOVA",
"prompt": prompt,
"response_order": "SUMMARY_FIRST",
"streaming": False
}
response = requests.post(url, json=payload, headers=HEADERS)
return response.json()
# Function to fetch X (Twitter) sentiment analysis
def get_X_sentiment(query):
url = "https://api.desearch.ai/desearch/ai/search/links/twitter"
payload = {
"prompt": query,
"model": "NOVA",
}
response = requests.post(url, json=payload, headers=HEADERS)
return response.json()
# Function to fetch competitor web insights
def get_web_search_results(query):
url = "https://api.desearch.ai/desearch/web/search"
payload = {
"prompt": query,
"tools": [
"web",
"hackernews",
"reddit",
"wikipedia",
"youtube",
"arxiv"
],
"model": "NOVA"
}
response = requests.post(url, json=payload, headers=HEADERS)
return response.json()
# Scenario: Automating Market Research for Smartwatches
ai_response = get_ai_search_insights("Competitor pricing analysis for e-commerce")
X_response = get_X_sentiment("Customer feedback on smartwatches")
web_response = get_web_search_results("Latest smartwatch technology features")
# Print results
print("🔍 AI-Powered Competitor Analysis:\n", ai_response)
print("\n🐦 X (Twitter) Sentiment Analysis:\n", X_response)
print("\n🌍 Web Competitor Insights:\n", web_response)
How It Works
- AI-Powered Insights: Fetch AI-powered insights on competitor pricing using Desearch AI Search.
- X Analysis: Get X sentiment analysis for customer feedback on smartwatches.
- Web Insights: Extract competitor web insights from online sources.
Expected Output
🔍 AI-Powered Competitor Analysis:
{
"response": "Major e-commerce retailers have reduced smartwatch prices by 15% in Q1 2025...",
"source": "AI Model: NOVA"
}
🐦 X Sentiment Analysis:
{
"tweets": [
{"user": "techguru", "text": "Loving my new smartwatch! Battery life is amazing!"},
{"user": "fitnesspro", "text": "Smartwatch heart rate accuracy is disappointing..."}
]
}
🌍 Web Competitor Insights:
{
"results": [
{"title": "Top Smartwatch Features in 2025", "url": "https://technews.com/smartwatch"},
{"title": "Best Smartwatches Under $200", "url": "https://wearables.com/budget"}
]
}