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Purpose

The organizational change Smaya enables is not “AI helps with tasks.” It is a change in what one person can be accountable for. A recruiter today runs a pipeline. A recruiter on Smaya supervises a team of AI Employees that runs the pipeline — and is accountable for their judgment rather than for their keystrokes. The work does not disappear. The unit of the job moves up a level, from doing to directing.
The metric that matters is the supervision ratio, not headcount. Exterview does not sell headcount subtraction. It sells how much a single accountable person can competently oversee.

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What the ratio looks like

One person can supervise a portfolio of AI Employees. How large depends entirely on how much judgment the work carries and how mature the configuration is. The ratio rises as calibration proves itself — not as a license tier. An organization moves along it by demonstrating that its criteria produce judgments its people agree with, which is measurable and visible in the record.

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How the working relationship is structured

A reporting line: each AI Employee has one named human owner. Upcoming: a consolidated portfolio view for each manager. It is a reporting line, deliberately. Each AI Employee has one named human owner, the way a person has one manager.

You set the mandate

What the role is for, and the outcomes it is accountable for. Written in a sentence you would sign off on for a person.

It works, and reports

It does the work and answers when you ask, with citations. Upcoming: reports brought to you in the Agent Inbox, so you do not chase status updates.

It escalates rather than guesses

Anything outside its mandate comes to you automatically. An AI Employee that is unsure raises it; it does not improvise.

You approve what matters

Creating a role, publishing a job and contacting a candidate wait for your approval. Routine reading and answering proceed.

You review and correct

Your corrections are recorded, and a pattern of corrections is the signal that criteria need recalibrating.

You can suspend instantly

Upcoming. One action pauses or revokes a role, exactly like removing a person’s access, without touching the others.

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A day in the hybrid model

1

Morning: read the Agent Inbox, not your email

You open the Agent Inbox and see the reports to view and the schedules to accept. What is waiting on you is the agenda. The Agent Inbox is Upcoming; today you start from the Decision Center and the Leaderboard.
2

Answer the reports

Each report carries the evidence and a recommendation. You answer Proceed, Drop or Talk. Your reasoning is captured.
3

Accept the schedules

Slots that need your team’s time wait for you to accept or decline. Actions you asked an AI Employee to take wait on an approval card.
4

Spend the rest of the day on judgment

Candidate conversations, hiring-manager alignment, offer strategy — the work that was always the job and never had enough hours in it.
5

Periodically: recalibrate

Where you keep overriding in the same direction, the criteria are wrong. You change them once, under approval, and the whole portfolio improves.

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Running a whole function this way

At maturity, a function is run by a small human team with a large digital one. A talent organization that ran on twelve recruiters might run on four people supervising sixty AI Employees — with the four doing the stakeholder, judgment and relationship work that was previously squeezed between coordination tasks.

What people keep

Judgment about people · relationships with candidates and hiring managers · setting the standard · every consequential decision · accountability.

What AI Employees take on

Volume · consistency · coordination · availability outside working hours · producing evidence · never getting tired at candidate two hundred.

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What does not change

A person is still accountable

Every AI Employee has one named human owner. Accountability is never diffused across a system.

People still decide

Outcomes that materially affect a person are made by a named human, at every ratio. The ratio changes supervision, never authority.

Oversight stays real

A higher ratio is only legitimate when the record proves the judgment holds. Scale that outruns calibration is not efficiency.

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Adopting it

1

Start with one role and a low ratio

One AI Employee, one owner, heavy review. You are calibrating criteria, not saving time yet.
2

Measure agreement, not volume

Track how often your reviewers agree with the recommendation. Agreement is the signal that earns a higher ratio.
3

Raise the ratio deliberately

Add roles to a portfolio when agreement is consistently high and escalations are the genuine exceptions.
4

Reshape the job description

The role changes from executing a process to owning a standard and a portfolio. Say so explicitly — the people doing it need to know their job changed.

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FAQs

That is a decision for the organization, not a claim we make. What the platform changes is how much one accountable person can competently oversee. Most customers redeploy that capacity into hiring more, hiring better, or giving candidates and hiring managers a materially better experience.
It depends on how much judgment the work carries and how well-calibrated the criteria are. Teams typically start between one and five and grow into the tens as agreement between reviewers and recommendations proves out. There is no fixed license-imposed limit.
Override rates and approval patterns are visible in the record. A reviewer approving everything instantly at a high ratio is a measurable pattern, and it is the exact signal to lower the ratio or recalibrate.
No. Configuring and supervising an AI Employee is a business task — a mandate, an approval boundary and a named owner. See Agent Studio.
Yes, in part. AI Employees, approval before writes and the record are Available in Exterview. The Agent Inbox, the consolidated portfolio view, suspending a role in one action, and AI Employees working on a standing goal are Upcoming.