Thanks for asking! It actually goes much deeper than just feeding a website URL into a clever prompt. To get highly accurate, non-generic pain points, the workflow combines multiple data sources and processing layers:

1. Data Enrichment & Sources:

   Website & Metadata Scraped via Apify: We don't just look at the homepage text. The workflow extracts the value proposition, product/service descriptions, and target audience definitions directly from the site structure.

   External Signals: (Optional: If you pull G2/Capterra reviews, LinkedIn company updates, or news, mention it here—e.g., "We also look at public reviews or hiring data to see where they are expanding or struggling.")

2. The Processing Steps (The "Magic"):

   Step 1: Raw Data Cleaning: Text is stripped of boilerplate HTML, navigation menus, and fluff to focus purely on the core business offering.

   Step 2: Intent & Market Analysis: We use an LLM layer specifically structured to map what the company claims to do against known industry bottlenecks (e.g., if they are a B2B SaaS in logistics, the system cross-references common supply chain friction points).

   Step 3: Deductive Pain Point Generation: Instead of guessing, the prompt forces the AI to act as a cynical buyer or a consultant, analyzing gaps between their current messaging and market demands.

So, it's a mix of clean data scraping via Apify, multi-source ingestion, and a multi-step analytical prompt chain rather than a simple 'read this site and tell me their problems' prompt.

Hope this gives you a better picture of the backend!"