Diaform conducts customer-discovery conversations through one link, probing past opinions toward past behavior, concrete events, and the respondent's own words.
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Forty-five minutes per call, plus recruiting, plus the no-shows. By conversation fifteen you're tired, by twenty you're cutting corners, and the next thirty calls, the ones that would have shown the real pattern, never happen.
"Would you use a tool that did X?" gets you a polite yes and zero signal. The Mom Test rule is to ask about the past, not the hypothetical, but under time pressure most founders drift into pitching, and the conversation turns into validation theatre.
Recordings, transcripts, sticky notes, a Notion doc that nobody reads. The insight is in there somewhere, but extracting it takes longer than the conversations did, and by the time it's done, the team has moved on.
The AI is briefed on Mom-Test-style collecting feedback from: ask about the past, not the hypothetical; avoid leading questions; never pitch. It stays disciplined across every conversation, including the ones where you wouldn't.
When someone mentions a problem, the AI digs in: when did it last happen, what did you do about it, how much did it cost you in time or money? That's where the real signal is, and what most founder-led calls skip.
Every conversation comes back with the exact words your customer used, the lines you can drop straight into a deck, a landing page, or a pitch. No paraphrasing, no founder gloss.
Every completed conversation includes summaries, sentiment, answer confidence, optional notable quotes, and the transcript. Use search and CSV export for cross-response analysis.
Voice answers are 2-3× longer and carry the frustration, the resignation, the 'oh god yes' moments that text can't. You hear when something genuinely hurts.
Drop the link in a LinkedIn post, a Twitter DM, a cold email, a community thread. People take the conversation at 11pm on a Sunday. No Calendly back-and-forth.
Frame them around the problem, not the solution, when did this last happen, what did you try, what did it cost. Half an hour of writing replaces every future conversation prep.
If you already have a deck, mockups, or a one-pager, drop them in. The AI references them only when relevant and never leads with them. For pure problem-discovery, skip this step.
Cold outreach, warm intros, community posts, a checkout email, wherever your potential customers are. Each respondent launches their own conversation when it suits them.
Open the dashboard to review each response, filter by sentiment or date, search for a topic, and verify optional notable quotes against the message history.
Before a single line of code, talk to 50 people in the segment you think you're serving. Find out if the problem is real and how they describe it.
The classic Lean Startup discovery loop, scaled. Run a batch of 30 conversations in a week instead of a quarter, and stay close to customers as you build.
Before committing the engineering time, ask the people who'd use it whether the problem the feature solves actually shows up in their week.
Talk to recent buyers and lost deals about the moment they decided. Verbatim quotes about why you won, and the objections you didn't see, feed straight into sales.
When someone leaves, send the discovery link instead of a survey. Find out what changed in their world, not just which checkbox they ticked on the way out.
Sharpen who your real customer is by collecting feedback from across segments, or run discovery for a client to ground their next product bet in real conversations.
The practical details behind setting up, running, and scaling this kind of research with Diaform.
A survey collects answers; a discovery conversation chases the story. Diaform probes when someone mentions a problem, when did it last happen, what did you do, how much did it cost. That's the Mom Test technique, and it's what separates discovery from a feedback form. You get verbatim quotes and emotional weight, not a Likert scale.
Explore the other ways Diaform can turn a static request for feedback into a useful conversation.
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