The shortlist is the product.
If we send you twelve candidates and you interview ten, we shipped a bad shortlist. The goal is calibration so tight that every name on the list is a real maybe.
The best teams don't have a hiring problem. They have a time problem.
Hiring is the highest-leverage activity inside any company under fifty people. It is also, almost universally, the worst-instrumented one. Founders write a job description, post it to four boards, and then spend nine weeks reading résumés in tabs they meant to close two days ago.
AndHire was built on a simple premise: the inputs to a great hire are knowable, and most of the work between knowing and signing is mechanical. We built a candidate graph deeper than any single recruiter could carry in their head, a calibration model that learns from every search we run, and a workspace that turns shortlists into signed offers in under two weeks.
We work with venture-backed startups in their first hundred hires, and with the e-commerce brands that are growing past the founder-as-recruiter stage. We charge a flat fee per hire, and we do not collect a cent until you sign an offer. We do not believe a fifteen-percent placement rake is the right way to align our incentives with yours. We never have.
If we send you twelve candidates and you interview ten, we shipped a bad shortlist. The goal is calibration so tight that every name on the list is a real maybe.
Misaligned incentives produce mediocre hires. We get paid when our work survives the offer stage, and not a moment before.
Every person in our network has a real human relationship with someone on our team. We do not spam. We do not sell data. We do not blast.
AI ranks; humans recommend. Every match is reviewed by a recruiter who has placed in that role before, and who will pick up the phone when something is off.
A small group of recruiters, engineers, and operators who got tired of how recruiting was done and decided to fix it.
Previously led talent at Ramp and Brex. Believes the median offer letter goes out three weeks too late.
Ex-Stripe ML. Spent four years building search ranking, now building people ranking.
Closed 220+ roles for Series A–C startups. Has a strong opinion about take-home tests.
Distributed systems. Built the matching graph from a Notion doc and a flight to Lisbon.