Automate the 3‑week Excel research grind with an AI scraping + synthesis agent

The 3‑week Excel research grind—collecting web data, cleaning it, and synthesizing insights into spreadsheets—is a recurring pain for analysts, product managers, investors and consultants, typically consuming 20–40 hours of focused work per project and delaying decision cycles. Across 50 million knowledge workers worldwide this adds up to fragmented but sizable demand, with organizations spending roughly $1,200 per user per year on tools and outsourced research that could be automated. You could build an autonomous LLM agent that scrapes prioritized sources, extracts and deduplicates structured data, normalizes fields into ready-to-use Excel/CSV templates, and generates a 1–2 page synthesized memo with provenance links; no-code connectors (Chrome extension, Google Sheets add-on, Zapier/Make) would cut onboarding to under 30 minutes. A micro‑SaaS pricing model ($20–50/user/month plus premium per-report or API tiers) maps to the stated $60B TAM and the 86/100 revenue potential, while the core engineering work focuses on reliable scraping, extraction rules, rate-limit handling and robust prompt engineering to reduce hallucinations. The market is particularly attractive now because LLM agents make multi-step autonomous research feasible without heavy bespoke engineering, no-code integrations lower switching friction, and founders favor lightweight vertical SaaS—factors reflected in a Market Score of 90/100. To win, prioritize traceable provenance and deterministic extraction for high-value vertical templates (VC diligence, competitive landscaping, supplier research), offer white-glove onboarding for early enterprise users, and be candid about ongoing challenges around data legality, scraper maintenance and trust; defensibility will come from repeatable templates and trusted data lineage rather than trying to be a general-purpose research chatbot.