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Am I building the right thing?

Your AI CEO holds the roadmap up to the data.

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Free to startNo credit card requiredUpdated Apr 2026

You'd think this needs a quarterly strategy offsite — Astra answers it on a Tuesday with the cancellation reasons read out loud.

The short answer

Astra answers "am I building the right thing" by holding your live roadmap up against four signals: what users actually do (PostHog feature adoption + retention curves), what customers say in support and cancellation flows (Intercom + Stripe cancel reasons), what your last 10 customer interviews said (Granola transcripts), and what competitors shipped in the last 30 days. She maps each in-flight roadmap item to evidence — "this feature has 4 mentions in 47 sales calls and 0 churned customers cited it" — and grades each one Build, Defer, or Kill. The output is a one-screen Lark message with a re-prioritized roadmap, the top 2 things you're missing that the data screams for, and the 1-2 sacred cows worth killing. Brutal, sourced, no consultant-speak.

How Astra actually does it

  1. 1
    Pull usage truth

    PostHog feature adoption per cohort, 30/60/90-day retention by feature, top exit pages, time-to-value per onboarding step.

    PostHogMixpanel
  2. 2
    Read cancellation + support themes

    Last 90 days of Stripe cancel reasons grouped, Intercom conversations tagged by theme, top 5 complaints with frequency.

    StripeIntercom
  3. 3
    Synthesize customer interviews

    Pull Granola transcripts from last 10 sales/customer calls, extract feature requests + objections + JTBD language.

    Granola
  4. 4
    Scan competitor changelogs

    What competitors shipped in last 30 days, what their users praised/hated on Reddit + Twitter.

    TwitterRedditAhrefs
  5. 5
    Grade the roadmap + write verdict

    Each in-flight item gets Build/Defer/Kill + reasoning. Top 2 missing items called out. Lark message with one sacred cow you should drop.

    LinearLark

What it looks like in chat

Am I building the right thing? Show me with data.
Astra
Three keep, two kill, one missing. **Keep:** • AI agent workflows — 78% of new signups use it in week 1, top retention driver. • Slack integration — mentioned in 23 of 47 sales calls as a deal-maker. • API rate limit increase — 6 enterprise prospects gated on this. **Kill:** • Native mobile app — 3% of monthly DAU, 11 weeks of eng spent. 0 churned customers cited "no mobile" as the reason. • Custom branding for free tier — 0.4% adoption, support burden of 12 tickets/mo. **Missing (data is screaming):** • Webhook retries — 14 mentions in support tickets last 60 days, top complaint among power users at risk of churn ($23k MRR exposed). Want me to draft the roadmap update for your team and a Linear ticket for webhook retries?
What you get back

A one-screen Lark verdict with each roadmap item graded Build/Defer/Kill + the top 2 things missing + 1 sacred cow recommendation — all backed by quoted evidence from PostHog, Intercom, Granola, and competitor scans.

Cadence

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Frequently asked questions

What if I don't run user interviews?

Astra works with what you have. With just PostHog + Stripe cancel reasons + support tickets, she can still grade roadmap items and surface 2-3 obvious misses. The interview layer adds JTBD language and emotional context — without it the verdict is more quantitative, less qualitative. She'll tell you what confidence level the verdict carries.

How does Astra avoid recency bias?

She weights signals by frequency and severity, not just recency. A feature mentioned 14 times across 6 months outweighs a single loud customer last week. She also separates "loud minority" from "silent majority" by cross-checking complaint volume against affected ARR.

Will she challenge my favorite feature?

Yes — that's the point. If your sacred cow has 0.4% adoption, she'll say so, name it, and propose killing it with the reasoning. You can override ("keep mobile app, strategic bet"), and she'll respect that but track it as a hypothesis to revisit in 90 days.

How long does this take?

First time ~30-40 minutes (she's pulling data from 5+ sources and reading 10 transcripts). Repeat runs 10-15 minutes because she caches the baseline. Output is one Lark message — no slides, no 20-page deck.

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