Synthesizing user research interviews

The hard part of user research isn't the calls. It's what happens after — turning thirty hours of conversation into the three insights that actually move a product roadmap, with quotes attached so stakeholders trust the synthesis.

The problem

The standard tool kit is Dovetail / Reduct / Notion + colored highlights. Highlight-based affinity mapping works, but it's slow and the human eye misses recurring themes. AI summarizers are tempting, but generic ones lose the speaker attribution that makes the quote credible.

How SeekFiles AI helps

• Cross-transcript Q&A — "what onboarding friction came up across all interviews?" with a cited list of speakers and their exact quotes.
• Speaker-aware retrieval — quotes stay attached to who said them.
• Refuses to invent themes — if only 2 of 30 mentioned something, the answer says so.
• Multilingual — interviews across languages get treated as one corpus.

A workflow

1. One assistant per study. Upload all transcripts (Otter exports, Reduct exports, manual notes).
2. Ask the questions PMs ask: "top 3 reasons people abandon onboarding," "what do paying users love that free users don't notice?"
3. Drop cited quotes into your readout deck. Skeptics can click through to verify.
4. Quarterly: ask across studies — "how has the trust-and-safety theme evolved?"

What to upload

• Interview transcripts (Otter, Rev, Descript, Reduct exports)
• Manual interview notes
• Diary studies and survey verbatims
• Past study readouts (PDF) — useful for longitudinal questions

The job stays the same: turn signal into a roadmap. The drudge work of getting there shrinks.