What is an AI employee?
A named role inside a team that runs one recurring workflow end-to-end — drafts, decisions, the loop with the customer — instead of a tile in a dashboard. Rebotify ships the first one in 48 hours, manages the four layers behind it, and prices it like a hire, not a software seat.
Mia is our AI employee. Email her — she’ll book your 15-minute call. That’s the demo.
A role, not a tool.
An automation is a script. An agent is a tool. An AI employee is a role. The difference is not the model behind it. It is the shape of the work. An automation runs on a trigger. An agent is summoned. An employee owns a job — has a name, an inbox, a calendar of recurring duties, and a queue of drafts a human can approve.
The same way you would think about hiring a junior, not the same way you would think about buying a SaaS tool. Onboarding is faster — 48 hours instead of three weeks — but the shape is familiar. Read first, draft second, calibrate, then ship.
Background reading: Why one employee beats a dashboard and Why we won’t sell you a fleet.
What an AI employee actually does.
On day one the employee reads — six months of replies, the team’s playbook, the customers’ tone, the tools the workflow touches. By day two it is drafting. Every customer-facing draft pauses for human approval before it goes out. By week two the queue is shrinking, because the team has taught the employee what to draft, what to flag, and what to silently archive.
In production, AI employees we have shipped run workflows like inbox triage, enrolment follow-up at a major Australian university, first-line ticket triage at a Tier-1 telco, and legal document assembly at an energy retailer. Each is one named role, one workflow, one approval rhythm. The work scales by adding workflows to the role, not by adding fleets of agents.
See it in production: case studies.
How it stays useful: the memory layer.
Every AI employee runs against a structured memory — past replies the team approved, edge cases that escalated and how they resolved, customer tone in the customer’s own words, routing rules. The memory grows the longer the employee is on the job. When a new model drops, the engine swaps; the memory stays.
Day-one performance with each new model is higher than day-one with the last one, because the memory carries six months of validated context forward. Models change every six months. Tools change every six weeks. The memory persists, and so does the value of the hire.
More: Memory is the moat.
How it stays trustworthy: the four layers.
A working AI employee runs behind four layers a customer never sees. A watchdog that auto-restarts a crashed gateway before the next message lands. An observability layer that pages the operator when a skill fails or a cron job slips past SLA. A version-controlled memory so a bad prompt edit rolls back the way a bad code commit would. A queue of pending drafts that pauses for human approval before anything leaves the customer’s domain.
The demo is what runs on a curated 20-minute call. The product is what runs at 2:47am on a Sunday, three weeks in, when an OAuth token quietly expires. The four layers are why the product survives that Sunday.
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 a tool that is summoned for a single task. An AI employee is a named role that owns a recurring workflow — it has an inbox, a calendar of duties, a queue of drafts a human approves, and a memory that compounds over time. The difference is not the model behind it. It is the shape of the work.
- How long does it take to deploy an AI employee?
- Forty-eight hours to first useful draft. Two days of reading and setup, then drafts start flowing into a review queue. By the end of week one the team is approving drafts that already sound like them. By month one most teams have stopped checking every draft and are scoping the next workflow.
- How is an AI employee priced?
- Three ways, none of them by token. Pay per completed task for countable outputs. Flat monthly for ongoing roles like inbox triage or executive ops. Pay on results — a base plus a share of revenue closed — for sales and outreach work. Tokens are our cost basis, not the unit we sell.
- What happens if the AI employee gets something wrong?
- Anything customer-facing pauses for human review before it sends. Drafts queue, a team member approves, the send happens. The expensive part of any reply is the research and tone; the cheap part is hitting send. Done right, the employee does the expensive part and leaves the cheap part to the team.
- Where does the data sit?
- In a region the customer has approved — usually Sydney, Melbourne, or the customer’s own Australian cloud account. Conversation logs, drafts and memory live in storage the customer controls, retained on the customer’s schedule. No data leaves the agreed jurisdiction without an audit trail.
- Can the AI employee be cancelled?
- Yes, any month. There is no twelve-month contract. The model behind the employee is replaceable; the memory layer is portable. If a customer offboards, they leave with the playbook and the structured memory the employee has built up.
- Who is responsible if something goes wrong?
- Rebotify. The employee is managed end to end — we wire the integrations, monitor the gateway, roll back bad prompts the way a team would roll back a bad code commit, and respond when a skill fails. The customer manages the queue and the playbook. We manage the plumbing.
Read the category? Name the workflow. We’ll ship the employee in 48 hours.
Mia is our AI employee. Email her — she’ll book your 15-minute call. That’s the demo.