What is an AI employee?

A role, not a tool.

A tool waits for a prompt. An AI employee has a job.

It owns one repeat queue: read the input, prepare the work, ask for approval when risk appears, and remember what the team accepted last time. Managed AI is the operating model (who runs it); AI employee is the role (what it owns).

Mia, your AI employee, is Rebotify’s intake role. The AI employee we build for a customer can have any name and role: support reply owner, pipeline hygiene officer, contract review specialist, or another job the team already understands.

The name matters because it concentrates accountability. A settings page does not own a missed customer reply. A named role does: one queue, one rhythm, one place the team can inspect when work slips.

Buyers often arrive from searches for AI receptionist or virtual receptionist. Those terms describe the front-desk job; AI receptionist and managed AI describe how Rebotify delivers it. For law firms, the concrete example is a legal intake employee or virtual receptionist for law firms: approved scripts, after-hours calls, web enquiries, consultation booking, transcripts, and matter handoff before a lawyer gives advice.

What an AI employee actually does.

It starts where the team already feels pressure: support replies, CRM follow-up, document checks, intake, reports, or handoffs.

Day one: it reads the tools, examples, rules, and approved tone. Day two: drafts or checks start landing in a review queue. Week two: the queue gets faster because misses become rules.

In production, AI employees we have shipped run workflows like inbox triage, enrolment follow-up, first-line ticket triage, and legal document assembly. Each is one named role, one workflow, one approval rhythm. The work scales by adding trusted workflows to the role, not by launching fleets of unsupervised agents.

If volume later justifies a second role, it gets its own scope, name, memory, and approval rules. That is different from buying a fleet of agents before the first employee has earned trust on one job.

The buyer should not manage prompts, connectors, monitoring, or model changes. That is why Rebotify sells a managed AI employee, not a platform license.

See it in production: case studies.

How it stays useful: the memory layer.

Every AI employee runs against a structured memory: accepted drafts, rejected drafts, edge cases, routing rules, customer tone, and the reasons a human changed the answer.

Models change. Tools change. The memory is what keeps the role useful because it carries the team’s approved way of working forward.

When the model changes, the employee can swap engines. The vault stays: past approvals, tone examples, edge cases, VIP rules, and escalation history. That is why week-six performance should start from customer context, not from a fresh prompt.

How it is priced.

Three ways, none of them by token. Pay per completed task for outputs you can count — contract reviewed, claim assessed, ticket triaged. Flat monthly for ongoing roles, the way you would pay a salaried hire. Pay on results — a small base plus a share of revenue closed — for sales and outreach work.

Tokens are our cost basis. They are not the unit we sell. The unit we sell is the work.

That keeps the buyer out of inference math. A customer should be able to count completions, contracts, replies, briefs, or booked consultations, not wonder whether a useful extra check made the token bill worse.

Month-to-month for general workflow plans. Vertical intake plans may include setup when scripts, routing, and handoffs need to be built up front.