What is an AI employee?
An AI employee is a managed role for one recurring business job.
It clears a queue, prepares drafts or checks, flags decisions, and keeps sensitive work behind human approval.
Rebotify sets it up, runs it, and keeps it working inside your current tools.
Tell us the job you wish you could hire for tomorrow.
Mia turns it into the first AI employee scope.
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.
Mia is Rebotify’s intake AI employee. 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.
Background reading: Why one employee beats a dashboard and Why we won’t sell you a fleet.
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.
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.
First roles
Start with a job your team already names.
A strong first AI employee is not vague. It has a queue, a clear cost when work waits, and a human approval point.
Revenue
CRM follow-up employee
Finds stale deals, drafts next touches, logs activity, and prepares account briefs before pipeline review.
Open the pageSupport
Support reply employee
Reads the queue, checks policy and history, drafts replies, and holds edge cases for approval.
Open the pageLegal
Contract review employee
Compares clauses to the playbook, flags deviations, and prepares a reviewer-ready brief.
Open the pageManaged AI
How Rebotify runs it
We set up the tools, monitor the queue, tune the playbook, and keep the AI employee working.
Open the pageHow 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.
More: Memory is the moat.
How it stays trustworthy: the four layers.
A working AI employee needs more than a good prompt. It needs monitoring, scoped permissions, versioned memory, and approval queues customers should not have to operate themselves.
Rebotify manages those layers. When a connector changes, a workflow drifts, or a draft misses the mark, we tune the employee instead of handing the problem back to the customer.
More: The demo is not the product and Review-before-send is the new safety harness.
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.
- What is the difference between an AI employee and an AI agent?
- An AI agent is usually called for a task. An AI employee owns a repeat job: read the inputs, prepare the work, ask for approval when risk appears, and learn from what the team accepts.
- How long does it take to deploy an AI employee?
- Forty-eight hours to first useful work. The first version is narrow: one queue, one sign-off step, and real drafts or checks for the team to review. After the workflow is proven, routine items can move to lighter audit while sensitive work keeps a human approval gate.
- How is an AI employee priced?
- Three ways: per completed task, flat monthly, or outcome-based. Tokens are our cost basis, not what the buyer should have to manage.
- What happens if the AI employee gets something wrong?
- Sensitive work pauses for human review. The AI employee prepares the research, draft, check, or summary. A human approves customer-facing, legal, financial, or risky decisions.
- Where does the data sit?
- In the region and storage model agreed for the workflow — often an Australian cloud region or the customer’s own account. Conversation logs, drafts, and memory follow the approved retention plan. Data movement, if required, is configured with the customer’s controls and audit trail.
- Can the AI employee be cancelled?
- Yes, month to month. If a customer offboards, they keep the playbook and structured memory created for their workflow.
- Who is responsible if something goes wrong?
- Rebotify is responsible for the managed system we run: integrations, monitoring, prompt and playbook updates, incident response, and rollback when a skill fails. The customer owns the business decision and approval queue. Rebotify owns keeping the operating layer healthy.
Read the category? Name the job. We’ll ship the employee in 48 hours.
Tell us the job you wish you could hire for tomorrow.
Mia turns it into the first AI employee scope.