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The problem
An MSME manufacturer chasing government and PSU tenders processes 30–80 RFQs a month. Each tender is a 40–200 page PDF, written in language designed to discourage the unprepared. Buried in there are the things that matter: eligibility clauses you might not meet, EMD you have to deposit, submission deadlines that are deceptively close, technical specifications you may have to subcontract.
The cost of screening manually is brutal: a senior engineer spends 45 minutes on a tender just to discover it requires ₹15 lakh of EMD or a turnover threshold the firm clears only barely. Multiply by 50 tenders a month and you’ve burned a senior person’s entire workweek on triage — before any actual quotation work begins.
The solution
The pipeline ingests tender PDFs the moment they arrive (CPP Portal subscriptions, IndianTradeBird emails, manual uploads), and Claude produces a structured tender brief: eligibility check, EMD & tender fee, key dates, technical scope, evaluation criteria, and a fit score against your firm’s profile. Briefs land in a Notion database where you triage with a single column: pursue / skip / refer to specialist.
The recipe deliberately leaves the go/no-go decision to a human. AI summarises and scores; the operator decides which tenders are worth pursuing.
The full setup, the n8n JSON, the Notion templates, and the troubleshooting
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