Finding a manufacturer is harder than building the product. Here's exactly how we used AI to research, qualify, and contact bag manufacturers for Stashed — and where AI hit its limits.
Building a physical product is 30% design, 30% manufacturing, 40% getting the manufacturer to actually make the thing you designed. AI helps with the first 30%, partially with the second 30%, and almost nothing with the third 30%. Here's how we used it for Stashed, and where it broke.
Prompt Claude: "Find me 20 bag manufacturers in Tamil Nadu, Karnataka, and Maharashtra. For each, give me: (1) primary product category, (2) past clients if known, (3) MOQ, (4) lead time, (5) location, (6) any quality certifications. Format as a CSV." 4 minutes later: 20 entries, ranked. Would have taken me 3 weeks by hand.
Then: cross-reference with IndiaMart, TradeIndia, and LinkedIn for the top 10. Cross-check 5 of them against the AI's claims. 4 out of 5 accurate. The 1 wrong: a manufacturer that closed in 2024. AI's data was 6 months stale, but I asked it to date-stamp its sources, so I knew to recheck.
For the 12 most promising, Claude drafted a personalized first email. Not a template — a real email that mentioned the manufacturer's specialty, my specific product, and a clear ask (send samples, quote MOQ). I edited 8 of the 12, sent 10, got 8 responses. 4 said "send us your spec sheet", 4 said "we don't do custom under 500 units". Down to 8 candidates.
Without AI: I'd have sent 4 templated emails, got 1 response, started the project with a poor manufacturer. With AI: I had 8 candidates by Friday.
Asked 4 of the 8 for samples. Got samples from 4 within 2-3 weeks. Evaluated each on: material quality, stitch quality, finish, weight, zipper smoothness, water resistance, value. The AI helped me write a 12-point sample evaluation checklist and weighted the criteria. But the actual hands-on evaluation? Human. I had to feel the bags, run the zippers, pour water on them.
Sample 1: 90% right, Velcro failed in humidity (caught by the stress test). Sample 2: 95% right but 30% more expensive. Sample 3: 70% right, looked like a duffel. Sample 4: 98% right, this is SN Bags (ours). Decision: SN Bags. The AI's checklist helped me evaluate but the choice was gut.
AI is bad at negotiation. It gives you scripts, talking points, what to ask for. But Indian manufacturing negotiations are relational, not transactional. The 12% price drop came when Mr. Singh (Nav's dad) joined the call. Family reputation matters. AI can't replicate that.
AI is a 10x multiplier on research and outreach. A 2x multiplier on sample evaluation. A 1x multiplier (i.e., human-only) on negotiation. Use it for the first 60%, do the last 40% yourself. The full Stashed case study is at /stashed-case-study.
For anyone building a physical product with AI: /services/ugc-for-ai-tools is the service. We do this for a living now.
2BFT is a 2-person studio in Vaniyambadi, Tamil Nadu. We test every major model on day one, ship real agentic systems, and teach builders across India. Free Academy, free newsletter, 200+ curated resources, 100+ copy-ready systems.
