Turn interviews into a scoped MVP: an AI-driven playbook for solo founders

Intro — why focused interviews + AI beat feature guessing

Solo founders waste time building features customers don’t need because signals are noisy and manual synthesis is slow. By combining disciplined, Mom Test–style interviews with a speech-to-text + LLM map/reduce pipeline you can convert raw conversations into prioritized user stories, acceptance criteria, and instrumentation — fast enough to scope an MVP in a week. Case studies show this at scale (Outset) and how domain tuning improves signal quality (ValueFlow) — but always add human verification to catch transcription errors and hallucinations ^[1][6][5].

Core workflow: record → transcribe → synthesize → spec

Keep the pipeline minimal: (1) a tight interview script rooted in The Mom Test, (2) a speech-to-text stage, and (3) an LLM-driven map/reduce synth that outputs evidence rows and scoped requirements. For long audio, chunking transcripts, summarizing chunks (map), then consolidating (reduce) is production-proven and keeps prompts reliable and cheap to run ^[3][2]. Use a repeatable template for the LLM output so you get machine-readable evidence rows you can turn into user stories and telemetry plans.

Tools and trade-offs

• AI-moderated platforms (fast, pricey): products like Outset let you run and synthesize many structured interviews quickly; great for breadth but can surface superficial themes unless prompts and parameters are tuned by researchers ^[1][6].
• Raw stack (cheap, controllable): use a speech-to-text API (OpenAI Whisper or AssemblyAI) to transcribe, then feed transcripts to an LLM with explicit extraction prompts. This gives privacy and prompt control at the cost of more wiring and review ^[4][3].
• Human-in-loop (mandatory): always include a short validation pass to confirm critical quotes and willingness-to-pay signals — AI outputs are drafts, not final specs, and transcription hallucinations are a known risk ^[5][9].

One-week plan — turn 10 interviews into a scoped MVP

1. Design: Draft 10 invites and a 30–40 minute script following Mom Test rules (no pitching; ask about recent actions). Use an AI prompt to refine and test questions for clarity ^[2][7].
2. Run interviews (Days 1–3): Record locally, confirm consent, and note handling rules before sending audio to any third-party API ^[3][4].
3. Transcribe: Batch-transcribe with your chosen STT API (AssemblyAI or Whisper). Prefer speaker diarization for multi-person calls to preserve attribution ^[3][4].
4. Map: Chunk transcripts into ~10–15 minute segments and run a chunk-level extraction prompt that returns exact quotes, tasks, numeric metrics, and workarounds as structured JSON ^[3].
5. Reduce: Consolidate chunk rows into an evidence table (quote, inferred problem, frequency, explicit willingness-to-pay). Use a conservative LLM prompt that highlights assumptions and gaps for reviewer review ^[8][9].
6. Spec: From the evidence table, have the LLM propose the top 3 problems, 3 user stories for the top problem with acceptance criteria, and 5 telemetry events to measure PMF signals. Human-review and correct before passing to engineering ^[8][9].
7. Ship: Scope the top user story as a one-sprint MVP (1–2 weeks), implement instrumentation, and validate with usage + follow-up interviews before expanding scope.

Practical prompt patterns

Interview-synthesis (map): "Extract: 1) exact quotes that show pain, 2) the task causing pain, 3) any numbers (hours, $), 4) proposed workarounds. Output as JSON array with keys: quote, task, metric, workaround." ^[3]

Requirements (reduce): "From these evidence rows, produce: 1) top 3 problem statements (one sentence each), 2) for the top problem, create 3 user stories with acceptance criteria, 3) list 5 telemetry events to measure product/PMF signals. Be conservative about assumptions." ^[8][9]

Real-world examples

Outset launched an AI-moderated interview product that reportedly conducted and synthesized 100+ interviews for a WeightWatchers project in under 24 hours; teams used the results to propose segmentation changes — a clear example of speed and scale when synthesis quality is high and researchers tune prompts ^[1].

ValueFlow’s case study shows that adding domain-specific analysis (emotional-shift detection, correction-tracking) produces richer signals, but those gains depended on an analyst narrowing prompts and validating outputs — the common pattern is: start broad, then invest time to make atomic analysis items repeatable ^[6].

Risks, checks, and non-negotiables

Transcription systems can introduce errors and hallucinations; mitigation techniques exist but critical quotes should be verified against raw audio before product decisions are made ^[5]. Confirm consent and data handling before sending audio to third-party services; if compliance requires, prefer vendors with enterprise DPAs or on-prem options ^[3][4]. Finally, design interviews to avoid confirmation bias — the Mom Test rules and careful prompt design help surface contradictions rather than smoothing them away ^[2][7].

Conclusion

With a small, repeatable pipeline (script → recorded audio → reliable transcription → LLM-driven map/reduce → human verification), a solo founder can convert 10–30 interviews into a scoped MVP and an instrumentation plan in a week. Choose an AI-moderated platform when you need speed and breadth, or build a raw stack for control and privacy — but always validate AI outputs against audio and a human reviewer before shipping engineering work ^[1][6][3][5].