Synthesize these 10 customer interviews
Your AI CEO turns 10 hours of recordings into one decision-ready brief.
You'd think this needs a research analyst and a week — Astra hands you the synthesis the same day.
The short answer
Astra synthesizes customer interviews by reading every transcript, clustering themes, and ranking insights by frequency × severity. She accepts transcripts from Granola, Notion, raw .txt files, or video recordings (auto-transcribed first), reads each in full, extracts every claim or pain point with the verbatim quote, clusters quotes into themes (e.g., "onboarding friction," "missing integration," "price too high"), counts mentions per theme, and ranks by frequency × emotional intensity. Output is a Lark brief with the top 5 themes, 2-3 verbatim quotes per theme, recommended actions, and routed Linear tickets per theme (product issues to product, sales objections to sales, marketing gaps to marketing). The synthesis is reproducible — re-run with new transcripts and Astra updates the same brief, showing what shifted.
How Astra actually does it
- 1Ingest and transcribe
Accept transcripts from Granola, Notion, .txt, or video files (auto-transcribe via Whisper). Normalize speaker labels and timestamps.
GranolaNotion - 2Extract every claim
Per transcript: every pain point, feature request, objection, praise, or competitive mention with the verbatim quote and timestamp.
- 3Cluster themes
Group quotes by semantic similarity into themes. Count mentions per theme. Rate each by emotional intensity (mild/strong/blocking).
- 4Rank and write the brief
Top 5 themes by frequency × intensity. 2-3 verbatim quotes per theme. Recommended action per theme. Honest "what we don't know" section.
- 5Route to teams
Product themes → Linear tickets with quote attached. Sales themes → CRM playbook update. Marketing themes → messaging doc update.
LinearSalesforceNotion
What it looks like in chat
A Lark brief with top 5 themes ranked by frequency × intensity, 2-3 verbatim quotes per theme, recommended actions, and Linear/CRM tickets routed to the right team.
On-demand per interview batch; cumulative re-runs as new interviews land.
Ask Astra this right now
We'll spin up your workspace, hand the prompt to Astra, and you see the answer in 60 seconds. Free.
Try this with AstraFrequently asked questions
What if my transcripts are messy or have multiple speakers?
Astra handles auto-transcribed recordings with imperfect speaker labels. She infers role (interviewer vs customer) from content. For 4+ speaker interviews, mark roles in the file header and she'll attribute quotes correctly. Quality stays high through ~10% transcription error rate.
Can she synthesize across more than 10 interviews?
Yes — scales to 50+ interviews per batch. With more interviews, theme rankings get more reliable (less noise) and she'll surface sub-themes within the top categories. Above 100 she suggests breaking into segments (by ICP, plan tier, etc.) for sharper insights.
What if some interviews contradict each other?
She surfaces the contradiction explicitly ("6 customers want Slack integration; 2 prefer no notifications at all — the split correlates with team size"). She never averages contradictions into mush. The brief shows the disagreement so you can decide which segment to serve.
Will the routed Linear tickets clutter our backlog?
Astra files tickets with the customer quote attached and a recommended priority based on mention count. Your team triages as usual. You can also ask her to file only the top 3 themes as tickets and leave the rest as notes — configurable per run.
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