We’re building Alteam, an AI hiring assistant that helps teams create job briefs and automatically ranks candidates with a match score. I always see in X that people are hiring on X and end up with 100s of DMs, and probably getting exhausted with reading all of them.

We started working on this after noticing that most hiring tools optimize for pipeline management, but not for defining what you actually need. Many job descriptions are vague, inconsistent, or copy-pasted, which leads to poor candidate matching downstream. Our approach: Generate structured job briefs step-by-step with AI Extract structured skill signals from candidates Compute a transparent match score based on requirements alignment Continuously refine the scoring model based on recruiter feedback Right now we’re focusing on: Early-stage startups hiring technical and product roles Structured skill extraction Improving explainability of match scores We’d love feedback on: How you currently define hiring requirements Whether match scoring is useful or misleading What signals you think are underrated in hiring Happy to answer technical questions.