HAN MA · CO-FOUNDER
Han
Co-founded Rebotify. Has been shipping AI and automation work for Australian businesses since 2016, before the hype cycle made it fashionable. Writes the positioning, adoption and pricing notes.
About
Rebotify turns AI into a worker your team can rely on.
We build, connect, monitor and improve a named AI employee for your business.
The service is deliberately simple: one worker, priced to the workflow — flat monthly, per-task, or by outcome — and no expectation that your team becomes an AI operations team.
FIRST FIXGet one task off the teamSee how it works
Tell us the work that keeps coming back.
Mia maps the first AI employee role that can clear it.
THE INSIGHTBusinesses do not want
Businesses do not want
more software to manage.
Most teams already have too many apps, too many tabs and too many half-finished AI experiments. Rebotify turns the first useful AI role into a managed employee: a name, a job, a morning rhythm and someone responsible for keeping the work moving.
2016
Operating AI systems for Australian businesses since the early automation wave.
48hr
Target for first useful employee setup after the kickoff call.
1 role
Named, owned, reviewed weekly — like any new hire.
CO-FOUNDERS
Who runs Rebotify.
Two co-founders. Both have been shipping AI work for Australian businesses since 2016 — and both still spend their day on real customer workflows.
JACK QIAN · CO-FOUNDER
Jack
Co-founded Rebotify. Operates the reliability, privacy and product surface — the layers customers never see that decide whether an AI employee stays useful at week twelve. Writes the craft notes.
PRINCIPLES
How we think about the work.
The best employee feels simple from the outside: ask for the work, review what matters, and wake up to progress already made.
Sell the employee, not the tooling
Your team should not need to understand tokens, model routing, MCPs, cloud computers or retry logic. They should meet a named worker with a clear role.
Start with recurring executive work
Inbox, meetings, follow-ups, loose ends, reporting and context switching are shared across agencies, law, insurance, wholesale and operations-heavy businesses.
Run it after launch
The useful part of an AI employee is not the first demo. It is the monitoring, fixes, new skills and memory work that keep it useful months later.
WHAT WE OWN
Setup, reliability and weekly improvement.
We scope the first job, connect the stack, document the memory, configure the worker, watch for failures and add the next useful skill. Your team asks for work. We keep the employee sharp.
WHAT YOU KEEP
Control, approvals and business context.
Your employee works inside the tools you approve, follows the role you define and escalates when judgement matters. The goal is real work back for your team, not opaque automation running loose.
PROOF
Three roles, shipped and running.
Each case study is scoped to one workflow. Read what it actually proves before assuming it covers a different industry or a bigger job than the one described.
Energy · legal & regulatory drafting
A tier-1 Australian energy retailer.
A named drafting assistant assembles first-draft contracts and regulatory filings from a precedent library for in-house counsel to review and sign — proof that Rebotify can run a scoped, reviewed drafting workflow. It does not prove small-firm legal intake or any client-facing legal output.
Read the case study
Higher education · course advice
A leading Australian provider of online higher education.
A named course advisor handles first-touch enquiries and pathway questions, with formal credit decisions and admission conversations staying with human advisors — proof of first-touch enquiry handling, not enrolment or admissions decisions.
Read the case study
Telecommunications · chat triage
The national telecommunications carrier of a small island nation.
A named chat triage officer classifies and drafts replies to routine support chats, with billing, complaints, and outage communication routed to agents — proof of high-volume first-response triage, not autonomous customer resolution.
Read the case study
QUESTIONS
Before you hand over work.
Who is behind Rebotify?
Two co-founders, Han Ma and Jack Qian, who have been shipping AI and automation work for Australian businesses since 2016. Han works on positioning, adoption, and pricing; Jack works on reliability, privacy, and the product surface that keeps an employee useful after launch.
What is an AI employee?
A named AI role scoped to one workflow — an inbox, a queue, a matter, a chat channel — connected to the tools your team already uses. It drafts, checks, and routes work; it does not get a blanket login to everything, and it is not a chatbot bolted onto your website.
How is Rebotify different from software?
Software asks your team to configure it: prompts, workflows, permissions, and troubleshooting. Rebotify scopes the workflow, builds and connects the employee, watches it daily, and tunes it weekly. Your team reviews the work and approves sensitive decisions; nobody on staff debugs the system.
Can I see proof?
Yes — three case studies below, each scoped to exactly what it demonstrates. None of them is a substitute for a different industry’s workflow; read what each one actually proves before assuming it applies to your business.
Who approves AI work?
A named human on your team, every time the work is customer-facing, financial, legal, or otherwise sensitive. The AI employee drafts and flags; your team signs off before anything ships.
48-HOUR START
Tell us the queue that keeps slipping. Leave with the first AI employee scope.
Get one task off the team
Tell us the work that keeps coming back.
Mia maps the first AI employee role that can clear it.