Your AI CEO walks into every interview already knowing the candidate.
You'd think this needs 90 min per candidate of resume reading — Astra hands you one-page briefs while you finish coffee.
Astra screens candidates by reading everything publicly available about them and synthesizing a one-page brief tailored to your role. For each candidate she pulls the resume, LinkedIn profile, GitHub (for engineers), public writing or talks, and any past company context, then outputs: top 3 reasons to hire, top 2 risks or red flags, 5 tailored interview questions targeting the candidate's specific gaps and strengths, and one "deep cut" question grounded in something they shipped or wrote. She cross-references their background against your role spec (pulled from your job post or recent chat) and flags any mismatch (e.g., "role says principal-level architecture, last 3 jobs were IC implementation"). Output is one Lark message per candidate, ready to read on your phone before the call. 5 minutes of prep replaces 90.
Resume PDF, LinkedIn profile, GitHub (repos + contributions), Twitter/X if relevant, blog posts, conference talks, prior companies' Crunchbase entries.
Read the job post, prior chat about what you're hiring for, and your hiring rubric if you have one in Notion.
One page per candidate: 3 hire signals, 2 risks, role-fit score 1-10, 5 tailored questions, 1 deep-cut question grounded in something they shipped.
Title inflation, gaps in employment, repeated short tenures, stale tech stack vs your needs, or anything that warrants follow-up in interview.
One message per candidate, formatted for phone reading. Linked sources at the bottom. Sent at least 30 min before each interview.
One Lark message per candidate (5-7 bullets, phone-readable) with hire signals, risks, role-fit score, 5 tailored questions, and a deep-cut question. Source links at the bottom.
On-demand per interview batch.
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 reports what she found honestly — "low public footprint, brief based on resume only" — and tilts her interview questions toward open-ended exploration rather than pre-loaded specifics. She never invents details to fill gaps.
She gives a role-fit score 1-10 per candidate but explicitly avoids ranking — hire decisions involve fit, team, and gut signals she can't see. Her job is to make sure you walk in informed; the call stays yours.
Resumes are processed in-session and discarded. They're not added to any training data or persistent memory. The brief itself stays in your Lark thread, scoped to your workspace, and you can delete the thread anytime.
Yes — she scales linearly, ~8-12 minutes per candidate to gather sources and write the brief. For a batch of 10 you'd have all briefs in ~2 hours. She'll prioritize by your interview schedule so the next-up candidates land first.