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.
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.
Accept transcripts from Granola, Notion, .txt, or video files (auto-transcribe via Whisper). Normalize speaker labels and timestamps.
Per transcript: every pain point, feature request, objection, praise, or competitive mention with the verbatim quote and timestamp.
Group quotes by semantic similarity into themes. Count mentions per theme. Rate each by emotional intensity (mild/strong/blocking).
Top 5 themes by frequency × intensity. 2-3 verbatim quotes per theme. Recommended action per theme. Honest "what we don't know" section.
Product themes → Linear tickets with quote attached. Sales themes → CRM playbook update. Marketing themes → messaging doc update.
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.
We'll spin up your workspace, hand the prompt to Astra, and you see the answer in 60 seconds. Free.
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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.
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.
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.
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.