Agentic automation for SMEs
Your staff is already using AI on personal accounts.
Give them proper tools instead. Scoper hands you a starter solution: plain instructions, useful examples, and a first task. Skip the trial and error. Productivity up, overheads down. No developer required.
Why a solution, not a course
Start where the work happens. Not at chapter one.
Start with structure
A pre-organised folder, prompts that have held up across real workflows, and a first task. No blank-page paralysis. No prompt-engineering rabbit holes.
Built across real workflows
The same scaffolding we use ourselves across real product work. Production-tested, not theoretical. We know which prompts hold up and which fall apart on the third edge case.
Your team runs it themselves
The AI writes the code. Your team reviews the outcome. Cowork with AI rather than outsource the solution. Turn your problem into a capability. Think Amazon Cloud and Gmail.
Three surfaces, one solution
Ask in chat. Run in code. See the result.
A Scoper solution shows up in three places, depending on who’s running it. Owners use chat. Developers use code. Everyone sees the dashboard.
I’ll group them now.
Ask in plain language
Owners and operators use Claude Cowork (chat-based, no setup). Type the task; the solution runs the workflow.
Or run it in code
Developers and power users run the same solution from Claude Code (scriptable, automatable, the same prompts under the hood).
See the result
Whichever surface ran it, the output lands in your dashboard. Counts, queues, the things that need a human eye.
The problem
Most teams pay for hours, not for results.
Every business has work that eats the day without producing much: the inbox, the data entry, the report, the chase. AI can do most of it now. The catch is that setting it up is a project on its own. Most teams either do not try, or try and give up when the prompts that worked on day one fall apart by the third edge case.
The companies that figure this out first will run cheaper and be more productive than their peers. Within a few years the gap will be too wide to close. Workers who know how to operate AI will be standard staff. The companies whose teams do not will lose ground in a zero-sum market.
Not every founder has the time or in-house capability to figure out AI implementation from scratch. That is the gap Scoper closes. Our solutions give you a starting point so you spend your three months building, not learning the hard way.
Skip the fumbling
Skip three months of trial and error.
You know AI belongs in your work. The information is overwhelming, the trial and error endless, the outcomes uncertain. Scoper hands your team the scaffolding to start at professional standards, not from zero or spaghetti code.
Open the solution in Claude Code or ChatGPT Codex (both pure-GUI desktop apps). Your team runs the first task by the end of the week.
While the office is asleep
Junior work, done overnight.
Schedule a Cowork task to run between 1am and 2am. The solution does the prep: research, drafts, queue-clearing. By the time you arrive at your desk, the morning brief is ready: a summary of what was done, decisions awaiting your approval, and email drafts queued for sign-off.
Last night’s run
Tuesday 7 May · Cowork scheduled task
↑ task window
Morning brief
Tuesday 7 May · 7:14am
Briefing
Triaged 12 inbound emails, reconciled 8 Stripe payouts, drafted 4 replies, prepared BAS reconciliation draft, tagged 18 expense receipts in Dext.
Decisions awaiting you
- Approve quote for Acme Co — A$4,200, due tomorrow
- Discount request from key client — 15% over standard, your call
- ATO query on FBT — draft response prepared, looks correct
Drafted, ready to send
- Re: Quote for May campaign — Acme Co
- Follow-up: invoice 1247 — 14 days overdue
Why Scoper
We have built these workflows ourselves.
Most AI projects fail because nobody scoped them properly. The pitch was broad, the brief was vague, and the build started before anyone had agreed what “done” looked like. Scoper is built on the opposite habit: figuring out what the workflow needs before you wire anything up, what to leave out, and where the constraints will surface only later if you do not name them now.
We have shipped AI workflows ourselves across operations, content, and client-facing communication. We know which prompts hold up and which fall apart on the third edge case. We know which workflows are worth automating and which are not.
The solution is the same scaffolding we use ourselves, productised. You drop the solution into desktop AI and your team is working with it by the end of the week. As Claude and ChatGPT evolve, the solution gets updated so you stay current without having to track it yourself.