Usable with a quick edit11 of 12 checks passed
The output is well-structured, evidence-backed, and almost ready to use. A quick edit to make the test measure an early signal (e.g., qualified meetings) would align it fully.
Output
The Core Reason This Fails
Cold outbound does not work for small agencies without a human to work the leads—and our target market lacks that human.
Our hypothesis assumes small agencies lack pipeline because founders are too busy to send emails. The interviews show that even when emails are sent for them, cold leads don't close because agency services are high-trust purchases. Closing a cold lead requires intensive, bespoke follow-up (custom audits, nurturing, and qualifying calls) that busy agency founders still do not have time to do. If we sell a $600/month autonomous emailer, agencies will either reject it out of fear for their reputation, or churn within six months when the meetings fail to convert into revenue.
The Evidence
- Cold leads don’t convert without trust: 11 of 14 agencies get an average of 86% of revenue from referrals and repeat work. Of the 9 that tried outbound (including outsourced agencies and contract SDRs doing the work for them), 7 quit within six months with zero closed deals. As A05 put it: "Content is a trust purchase... They booked meetings, I'll give them that, maybe six or seven a month. But the people on those calls had no idea who we were... We didn't close anything." (See also A01, A10).
- Email is only 10% of the job: A04 (one of only two agencies succeeding at outbound) explicitly warned us: "It works because of the follow-up, not the first email... What wins deals is that I then send them a teardown of their site, a short video, and I'm on the phone with them within a week... Most agencies our size don't have someone like me. The owner does sales on the side. They don't have the time to follow up, and the follow-up is the job."
- Automation threatens their real pipeline: Because reputation drives referrals, founders fear automated outreach. Both A04 and A05 refused to let AI send unread emails ("I'm not letting anything send in our name unread"). Past automation burned A11 ("domain ended up on a spam list") and A02 ("LinkedIn shut our founder's account down. That's our best referral network").
- Shrinking addressable market: 3 of 14 agencies (A08, A12, A14) are capacity-constrained, not lead-constrained. A08 noted: "I don't need more leads, I need another designer... [An AI SDR] would make my problem worse."
Where the Evidence Cuts the Other Way
- Commoditized/technical niches can work: The two successful outbound users (A04 Shopify, A07 SEO) sell technical services where need can be spotted externally (e.g., outdated tech stacks) rather than pure creative trust. A07 converts 1 in 10 meetings using outsourced lead-gen.
- A04 will buy a research copilot: A04 would pay $600/month if we replace their research stack (Apollo/Clay) and save three hours a day—provided they approve every email.
- The problem is real, the channel is wrong: 6 of 14 agencies do suffer feast-or-famine swings. A05 solved a famine in 30 days by emailing past clients and offered to pay $200/month "tomorrow" for an AI tool that nurtures warm contacts and referral partners.
What We Need to See to Be Proved Wrong
Founder-led agencies (5–15 staff, no dedicated BD) must convert cold AI-sourced emails into signed deals (not just booked meetings) within a 10-week sales cycle, without spending more than two hours a week on manual follow-up.
The Cheapest Test
A 10-week "Wizard of Oz" concierge test. Recruit 5 founder-led agencies (exclude those with BD staff) to pay $300/month. Using off-the-shelf tools (Apollo, Clay, Claude), we manually research prospects and draft personalized emails for founder approval. Pass criteria: At least 2 of the 5 agencies close a paying client within 10 weeks and renew at $600/month. Cost: ~$500 in software and our time; zero code written.
Grades and run details
Decision model 88 · LLM judge 92
Decision model checks
- partialUses the supplied evidence correctly9%
- passAddresses the actual decision98%
- passRespects explicit constraints55%
- passIdentifies material uncertainty98%
- partialAvoids unsupported claims38%
- passProduces the required deliverable83%
- passFinds the load-bearing assumption79%
- passUses the interviews faithfully33%
- passEngages the counter-evidence82%
- partialA cheap test that can actually read out91%
- passTigers, not paper tigers89%
- passFinds the need behind the idea37%
Run
- Run
- #1
- API response time
- 11 s
- Submitted
- 13 Oct 2026