Five Steps to Building a Data Library Business
📌 The core business model involves collecting, packaging, and selling valuable, niche-specific data tables, with examples selling from \9.95 to \49.
🛠️ The process is broken down into five repeatable steps: Idea, Brainstorm/Collect Data, Filter, Enrich, and Package/Sell.
⏱️ The entire process, utilizing AI and automation scripts provided, can allegedly be completed in under 10 minutes for initial data generation.

Step 1 & 2: Idea Generation and Data Collection (Brainstorming)
💡 The speaker uses an updated version of the open-source library Simpler LLM featuring a recursive brainstorm function to exponentially generate data records using AI prompts.
📄 For the first example (expired domains), the script generated 20 domain records, including ID, domain, and quality score, saved into a CSV file.
🧠 Recursive brainstorming generates data exponentially, starting with a small set of ideas and then generating more ideas based on each initial result.

Step 3 & 4: Filtering and Data Enrichment
🔍 Step 3 involves filtering data; for domains, this meant using external services like GoDaddy/Whois via an automated script to filter out available domains, keeping only those already registered (like expiring ones).
🔗 Data enrichment (Step 4) transforms raw data into a product by adding valuable metrics, often sourced from Data APIs found on platforms like RapidAPI.
🤖 AI code generation tools were used to create a Python script that integrates with the Keywords Everywhere API to pull metrics like monthly traffic and ranking keywords for domain enrichment.

Step 5: Packaging and Selling the Data
1️⃣ API Access: List the curated data as an API on platforms like RapidAPI for recurring access revenue.
2️⃣ Digital Download (Easiest): Sell the resulting file (Excel, Notion) as a downloadable product, for example, via Gumroad.
3️⃣ Web Application: Build a simple UI around the data using AI-assisted builders like Lovable, creating a unique tool based on the dataset.

Key Points & Insights
➡️ The secret to profitable data libraries is the Data Enrichment step, which adds metrics that people will pay for, differentiating it from raw, unsorted data.
➡️ Automation tools like Simpler LLM and AI coding assistants significantly speed up the data collection, filtering, and API integration parts of the process.
➡️ Explore RapidAPI as a primary marketplace for both finding APIs for enrichment and selling your final data product as an API service.

📸 Video summarized with SummaryTube.com on Nov 19, 2025, 19:40 UTC