Stage 1: Data Acquisition & Filtering
This initial stage is the foundation of the entire system. The goal is to gather a large, relevant list of available domain names that meet our basic investment criteria. Without high-quality source data, the AI's analysis would be meaningless. We achieve this by connecting to domain marketplaces and applying strict initial filters.
Source Your Data
You'll need to access lists of newly available, expiring, or dropped domains. The most effective way to get this data is by using APIs from domain registrars and marketplaces.
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Primary Sources
GoDaddy, Namecheap, Dynadot
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Specialized Services
ExpiredDomains.net, DomCop
Apply Initial Filters
Your data-gathering script should immediately filter the incoming data to create a manageable list of candidates for the AI to analyze.
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Price: <= $7
Ensures low initial investment.
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Availability
Confirms domain is ready for purchase.
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Target TLDs
Focus on reputable extensions like .com, .io, .ai, .co.
Stage 2: Feature Engineering
This is the most critical stage where we define "value" for the AI. We translate abstract concepts like "catchy" or "marketable" into concrete, numerical features. This allows the machine learning model to understand and quantify a domain's potential, moving beyond simple keywords to analyze its linguistic and commercial qualities.
Stage 3: Predictive Modeling
With our features defined, we now build a model to forecast a domain's profitability. This involves training a regression model on historical sales data to predict a potential sale price. The final step is to calculate the potential Return on Investment (ROI) to identify the most promising opportunities that meet our 300% revenue target.
Forecasting Profitability: ROI Calculation
The chart below visualizes the core goal: turning a low-cost purchase into a high-value asset. The model predicts a sale price, and we filter for domains where the projected ROI is 300% or more.
Stage 4: System Architecture & Deployment
This final stage assembles all components into an automated, functioning system. The architecture is designed as a pipeline that continuously fetches, analyzes, and predicts the value of domains. Hover over each component in the diagram below to understand its role in the process. The output is a simple, actionable list of high-potential domains delivered to a dashboard or via alerts.
Hover over a component to see its description.