Your AI CEO holds the roadmap up to the data.
You'd think this needs a quarterly strategy offsite — Astra answers it on a Tuesday with the cancellation reasons read out loud.
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.
PostHog feature adoption per cohort, 30/60/90-day retention by feature, top exit pages, time-to-value per onboarding step.
Last 90 days of Stripe cancel reasons grouped, Intercom conversations tagged by theme, top 5 complaints with frequency.
Pull Granola transcripts from last 10 sales/customer calls, extract feature requests + objections + JTBD language.
What competitors shipped in last 30 days, what their users praised/hated on Reddit + Twitter.
Each in-flight item gets Build/Defer/Kill + reasoning. Top 2 missing items called out. Lark message with one sacred cow you should drop.
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.
On-demand or monthly check-in.
We'll spin up your workspace, hand the prompt to Astra, and you see the answer in 60 seconds. Free.
Try this with AstraClear answers about wallet credit, usage, subscriptions, and how Tycoon charges for work.
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.
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.
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.
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.