Will anyone want this, and should you build it?
For anyone about to spend weeks or months building something. AI is good at speeding up research and synthesis, and bad at telling you whether people will pay, so these workflows keep real customers and your own judgment at the centre. Start with conversations, add market research, then make the decision with rules you set in advance.
7 workflows, picked and ordered by Founder Workflows. The notes are ours; each workflow's own page has the full details, sources and limits. Updated October 20, 2026.
Start here. Use Claude to draft questions that test your assumptions, sort notes across calls and rehearse with a sceptical buyer. The guide is clear about the limits: Claude cannot tell you whether a hypothesis is true, and a simulated customer is not your customer. No results are reported.
A field guide on using Claude before and after customer interviews: drafting hypothesis-testing questions, synthesising notes across calls, and role-playing a skeptical buyer.
After the interviews, have ChatGPT answer your discovery questions from the transcripts and pull out quotes. The founder calls customer discovery the most useful thing AI did at this stage, and says market research from AI does not replace talking to customers. The founder's company is not an online business, so the method transfers more than the specifics.
A founder starting a new company describes using ChatGPT to answer discovery questions from interview transcripts, pull enthusiasm quotes, and draft a competitor and ecosystem table.
Read 50 to 100 recent reviews of a competitor and tag them into themes and the moments people decide to switch, with AI tagging that a person checks. A clear, step-by-step method from an agency that sells related campaigns; its thresholds are defaults, not tested rules.
A step-by-step method for turning a competitor's recent reviews into tagged themes, switch moments and clusters you can act on, with an optional AI tagging step.
Collect complaints about existing tools from forums and review sites, then decide what to build. The developer reports a first paying customer in week two and about $2.3K a month, but this is a newsletter retelling of a forum post and the revenue is unverified. Claude's 8.5 out of 10 score is an opinion, not proof of demand.
A solo developer used Claude's extended thinking to collect user complaints from forums and review sites, then built a small cold-email tool. A newsletter retells it; the results are self-reported.
A useful warning. The author tested a popular trick, asking ChatGPT for a strategy from a competitor's sitemap, and rated it 3 out of 10: the structure was revealing, but a sitemap shows what was published, not what works.
A tested version of a viral trick: group a competitor's sitemap URLs by path, ask ChatGPT for a roadmap, and see what it can and cannot tell you. The author rates it 3 out of 10.
A founder followed a confident AI pricing recommendation and later had to rework it because it did not fit their audience. Before acting on AI advice, ask what it cannot know about your customers. An anecdote with no figures, but the lesson is cheap to apply.
A founder explains how trusting a confident, detailed AI pricing recommendation led to a structure that did not fit their audience, and the filter question they now use before acting on AI output.
Write your stop rule first, then have two AI models judge your record against pre-set tests, with instructions not to count time already spent as evidence. One developer's account from a finance project; both memos said not to found a company, and the author stopped building toward one.
A developer with 21 months of work behind a trading system had Claude and Cursor's agent each write a verdict memo against five pre-set gates, then tried 25 counterarguments. Both said not to found a company.