An AI recruiter that runs the whole loop.

The loop starts with a conversation. You describe the role the way you would to a colleague: business context, must-haves, preferences, compensation range, location and work model. From that, Mira builds an Ideal Candidate Profile, the structured contract every later stage optimizes against, so search, screening, and outreach stay pointed at one intent instead of drifting apart agent by agent.

Intake is deliberately short. Every extra question raises drop-off, so Mira asks a follow-up only when the answer would genuinely change the search. The tradeoff is laid out in the system report; what you feel is a setup that stays deliberately short and gets to real candidates quickly.

What stays yours: the intent. The brief is your definition of the role. Mira structures it; she does not substitute her own.

Mira's Search Agent runs retrieval across 860M+ profiles in over 190 countries, built entirely from publicly available sources. Hiring intent is almost always underspecified at the start, so the search runs as a closed loop rather than a single query. Pool size self-corrects in both directions, under logged, controlled revisions. Hard constraints and soft preferences never blur into each other, and every revision is logged, so any result can be explained and reproduced.

Matching runs on meaning rather than keyword overlap. We fine-tuned our own recruiting-native embedding model because general-purpose embeddings kept confusing adjacent roles and miscalibrating seniority. The measured gains are on the sourcing feature page and in the embeddings paper behind it.

What stays yours: the edges of the search. Your must-haves and disqualifiers hold as pass-fail. And when a request is internally contradictory or the market is simply sparse, Mira surfaces the conflict and proposes the smallest revision that restores feasibility, rather than quietly broadening forever.

For each candidate worth contacting, Mira drafts a first touch from three inputs: what matters most in the role, why this specific person fits, and the candidate's own recent work. No generic templates. Engagement then runs as a stateful, multi-turn conversation. Interested candidates move forward fast. Hesitant ones get straight answers at the right level of detail. Silent ones get spaced, varied follow-ups that stop after a defined limit, because your employer brand outlasts any single role.

The control point sits at the top: nothing goes out until you approve the outreach. That approval is not a formality. It is the mechanism that keeps outcome pricing honest, since qualified is defined under messages you signed off on, and we cannot loosen a bar you set without you noticing. The outcome-pricing essay walks through why that matters.

Screening happens on both sides of the reply. Before anyone is contacted, Mira grades fit against the profile as a structured judgment. Hard requirements act as pass-fail gates, so a strong overall profile cannot slide past a missing must-have. Preferences act as weights that shape the ordering. Every grade carries citations to its supporting evidence, and where a profile is incomplete, Mira states the uncertainty instead of inventing detail.

After a candidate replies with interest, she collects the minimum needed to move fast: an updated resume, current location, work authorization, compensation expectations, and interview availability.

What stays yours: the definition of qualified. The bar comes from your brief, and it is the bar everyone who reaches you was checked against.

One more gate sits between the pipeline and your calendar. A delivery lead on our team reviews every candidate before they reach you. That step is what turns automation into something you can rely on: software carries the volume, and a person stands behind the result. A candidate who misses your bar never arrives, and never costs anything, because the pricing runs on the same rule as the loop. A credit is one interested, interview-ready candidate. Plans run $49 to $299 a month, 10 percent off billed annually, and if nobody clears your bar, the credit stays unspent.

What stays yours: everything final. You take the interviews, make the decision, extend the offer, and employ the person. Mira runs the search. The hire is yours.

An autonomous loop you cannot measure is a liability. Agent systems fail quietly: no stack trace, just a plausible answer that happens to be wrong. So Mira's fifth agent does nothing but evaluate the other four. Each agent is scored against task-specific datasets and rubrics, and the whole pipeline runs through simulated hiring flows scored on business metrics: qualified candidate yield, time to shortlist, reply rate, and interview conversion.

The discipline behind that layer is its own note, Agent Evaluation, Done Right. The short version: golden sets grown from real production failures, cheap rule checks before any model-graded scoring, LLM judges whose agreement with humans is actually measured, and evaluation wired into CI, where a metric falling past its threshold blocks the release the way a failing test would. After launch, changes ship through canary against live traffic before they ramp.

We publish this because the loop is the product. A tool that returns a list can afford loose evaluation; a human catches its mistakes at the handoff. A recruiter that runs every stage cannot. The evaluation layer is what lets us hand you a calendar of booked interviews, and stand behind what lands on it.

The fastest way to judge an end-to-end claim is to hand it an end. Bring a role you need filled, ideally the one your current stack keeps circling. Define it, approve the outreach, and watch what reaches your calendar. The trial is free for 14 days, with 3 New Roles and 12 Credits, and no card is required.

Start with one role →