Leverage the Datura 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.

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Key Use Cases

Here are the key use cases for the Intelligent Agent Task Automation that can be derived from Datura’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.

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Implementation Example

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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.

Extract AI-driven insights for competitor trends and market analysis.

    curl --location 'https://apis.datura.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
    }'

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Step 2: Fetching X (Twitter) Discussions for Sentiment Analysis

Analyze social media conversations related to a product or brand.

    curl --location 'https://apis.datura.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"]
    }'

Gather insights from competitors’ public web pages and articles.

    curl --location 'https://apis.datura.ai/desearch/web/search' \
    --header 'Authorization: dt_<your_api_key>' \
    --header 'Content-Type: application/json' \
    --data '{
        "query": "Latest smartwatch technology features",
        "results_limit": 5
    }'

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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.

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Python Scenario Implementation

Here’s a Python implementation of the AI-powered automation system using the Datura API. This script automates market research, sentiment analysis, and competitor tracking using the API endpoints you provided.

    import requests

    # Set your Datura 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://apis.datura.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://apis.datura.ai/desearch/X/search"
        payload = {
            "query": query,
            "date_filter": "PAST_7_DAYS",
            "tools": ["Twitter Search"]
        }
        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://apis.datura.ai/desearch/web/search"
        payload = {
            "query": query,
            "results_limit": 5
        }
        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)

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How It Works

1

AI-Powered Insights

Fetch AI-powered insights on competitor pricing using Datura AI Search.

2

X Analysis

Get X sentiment analysis for customer feedback on smartwatches.

3

Web Insights

Extract competitor web insights from online sources.

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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"}
        ]
    }
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