We’re working with a newsletter agency that wants their competitor research fully automated. Right now, their team has to manually: - Subscribe to dozens of newsletters - Read every new issue - Track patterns (hooks, formats, CTAs, ads, tone, sections, writing style) - Reverse-engineer audience + growth strategies We’re trying to take that entire workflow and turn it into a single “run analysis” action. High-level goal: - Efficiently scrape competitor newsletters - Structure them into a compressed format - Run parallel issue-level analyses - Aggregate insights across competitors - Produce analytics-style outputs - Track every request through the whole distributed system How the system works (current design): Step 1 – You trigger an analysis You give the niche. The system finds relevant competitors. Step 2 – Scraper fetches issues Our engine pulls their latest issues, cleans them, and prepares them for analysis. Step 3 – Convert each issue into a “structured compact format” Instead of sending messy HTML to the LLM, we: - extract sections, visuals, links, CTAs, and copy - convert them into a structured, compressed representationThis cuts token usage down heavily. Step 4 – LLM analyzes each issue We ask the model to: - detect tone - extract key insights - identify intent - spot promotional content - summarize sections Step 5 – System aggregates insights Across all issues from all competitors. Step 6 – Results surface in a dashboard / API layer So the team can actually use the insights, not just stare at prompts. Now I’m very curious: what tech would you use to build this, and how would you orchestrate it? P.S. We avoid n8n-style builders here — they’re fun until you need multi-step agents, custom token compression, caching, and real error handling across a distributed workload. At that point, “boring” Python + queues starts looking very attractive again.

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