Sourcing tools find profiles and stop. Screening tools score candidates and stop. Mira takes a role brief and returns interviews on your calendar, and the work in between never lands back on your desk.
Mira is the AI recruiter inside Metix AI: five specialized sub-agents that run sourcing, outreach, screening, and scheduling as one loop. The measure of end-to-end is not how many stages are automated. It is that no stage is handed back to you. You write the brief, approve every outreach message, set the bar, and take the final interviews; Mira does everything between those points, and a delivery lead reviews quality before any candidate reaches you. Across our early enterprise customers, 95 percent of roles had a first interview scheduled within 24 hours.
Every hiring tool automates something. The market is full of software that finds profiles faster or drafts messages faster. None of it changes who owns the work, because each tool stops at a stage boundary and hands everything past that boundary back to you. A sourcing tool returns a list, and the conversations are yours. A screening tool returns scores, and the judgment is yours. Speed went up everywhere. The desk the work lands on stayed the same.
So when we call Mira an end-to-end AI recruiter, we mean something narrower and harder than a long feature list. Between your role brief and an interview on your calendar, no stage is handed back to your team; that holds for sourcing, outreach, screening, and scheduling alike. The loop runs until a qualified, interested person is booked, and if nobody clears your bar, Mira says so instead of padding the shortlist.
Closing the loop does not mean losing your grip on it. You keep exactly the controls that need you. You write the brief. You approve every outreach message before it goes out. You set the bar candidates must clear, and you run the final interviews and make the hire. Everything between those points is Mira's job, with a human delivery lead reviewing quality at the exit.
The architecture behind this is public: Mira runs as a pipeline of five specialized sub-agents, described in full in our system report on Mira. This page walks the same loop from your side of it, stage by stage: what Mira takes over, and what stays yours.
Six stages, one owner. The middle column is Mira's work. The right column is yours, and it holds judgment calls, never busywork.
| What Mira takes over | What stays yours | |
|---|---|---|
| Brief | A short structured intake; follow-ups only when an answer would change the search | The role, the constraints, the bar |
| Sourcing | Adaptive retrieval across 860M+ public profiles | Must-haves and disqualifiers |
| Outreach | Personalized first touch and multi-turn follow-up | Approval of every message before it sends |
| Screening | Evidence-graded fit, interest checks, resume collection | The definition of qualified |
| Scheduling | Availability collected, interviews booked on your calendar | The calendar and the interview itself |
| Delivery review | A delivery lead checks every candidate against your bar | The decision, the offer, the hire |
The employer's view of Mira's pipeline. The stages below follow this table in order.
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. A pool that comes back too small gets broadened in a controlled way. A pool that comes back too large or too weak gets tightened. 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 hard gates. 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 honest 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 quietly loosen a bar you set. 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.
This is the stage most funnels quietly leak. An interested candidate is not yet an interview; between the reply and the call sit time zones and calendar tennis, and every day of it costs momentum. Mira collects availability during screening and books interviews directly onto your calendar, so what arrives on your side is a confirmed meeting.
The speed shows up here. Across our early enterprise customers, 95 percent of roles had a first interview scheduled within 24 hours, and roles filled in about a week against an industry average of 45 days. How the booking works, and why we treat scheduling as part of the product rather than an export, is on the interview scheduling page.
These are early results, drawn from the roles Metix AI has run so far, and they move with the role and the market.
What stays yours: the room. Mira gets the right people to the table. The conversation, and the judgment, are yours.
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, hire-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.
Map that loop against the rest of the market and the difference is where each product ends. A sourcing tool automates one stage, then returns a list: the outreach, the replies, the screening, and the scheduling are still yours. An AI screener automates the middle and leaves the decisions. Each of these is genuinely useful, and each ends the same way, with a handoff, and the work on the far side of a handoff does not shrink because the near side got faster.
Agencies are the honest exception. They do finish the search, at 20 to 30 percent of first-year salary per placement, one hand-built shortlist at a time. The full argument, with the market laid out stage by stage, is in AI sourcing tool vs AI recruiter.
One test tells you most of it. Ask what happens after the tool's output appears. If the answer begins with “then you”, you are looking at a stage, not a loop.
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 sub-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.
Mira runs the whole loop: sourcing across 860M+ public profiles, personalized outreach, screening, and interview scheduling, with a delivery lead reviewing every candidate before they reach you. You write the brief, approve every outreach message before it goes out, set the bar candidates must clear, and take the final interviews and the hiring decision.
Both, by design. Mira is built as five specialized sub-agents that run the pipeline, and two humans stay in the loop: you approve all outreach before it is sent, and a Metix AI delivery lead reviews every candidate before they reach your calendar.
Across early enterprise customers, 95 percent of roles had a first interview scheduled within 24 hours, and roles filled in about a week against an industry average of 45 days. These are early results, and they move with the role and the market.
Nothing is charged. A credit is spent only on an interested, hire-ready candidate. Plans run from $49 to $299 a month with 10 percent off annual billing, and the free 14-day trial includes 3 New Roles and 12 Credits, no card.