The system we run on
Our own practice runs on the same kind of system, and we’d build you the kind we rely on every day.
For-purpose organisations
Not-for-profits, peak bodies and advocacy organisations
Whether you run community services, advocate for a cause or speak for a whole sector, the work matters and the team is usually stretched. We build workflow automation that drafts the routine work from your organisation’s own documents, past work and know-how, train your people to use it and judge its drafts well, and research and evaluate your programs to show what they changed. It’s designed to give a small team the capacity of a much larger one, with your people in charge of every judgement that matters.
Why you come first.
We measure our work by its impact: whether people’s lives, and their communities, are better for it. For-purpose organisations exist for the same reason, and most do that work with fewer people and less funding than it needs. When they can do more, the good flows on to everyone they serve.
That’s why you come first. Your work uses everything we do: workflow automation for the routine, training so people use it and judge well, and research and evaluation for the programs you run and the arguments you make.
For peak bodies and advocacy organisations
How better advocacy reaches policy“Helping peak bodies talk to the government better, so that the government is more likely to spend money where it ought to be spent. Impact. That’s the impact that I want to have.”
We know your week.
Ten problems, as you’d put them, and what we do about each. Most apply to any for-purpose organisation; the three marked are specific to peak bodies and advocacy organisations.
Ten problems small for-purpose organisations face, each paired with what Tailored Ideas does about it, in five groups; three are marked as specific to peak bodies and advocacy organisations. The desk: a big organisation’s job on a small office’s staffing, answered by AI agents that draft the desk work from the organisation’s own documents and past work for a person to approve; submissions that take weeks (marked), answered by drafts built on research into what persuades decision-makers. What you know: knowledge that walks out the door with people, answered by the brain, the organisation’s documents and know-how in plain files, with each day’s work filed in. What’s coming: too much to watch, answered by an AI news desk that reads Parliament, the news and funders every morning; reactions not seen coming (marked), answered by stakeholder intelligence kept in a CRM. What you can prove: research that wouldn’t survive a hard question, answered by surveys and measures built for the question; programs nobody evaluates, answered by design from the evidence and measurement against a comparison group, which also shows what the system and training changed. Your people: boards that know the questions but not the judgement, answered by judgement science; tools that don’t outlast the grant, answered by training on the team’s own work and an adoption plan in every build; communities that need the skills that stay human (marked), answered by community programs built into what people already do. Each answer is tagged as workflow automation, training, or research and evaluation.
The desk
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What this looks like
A small team with the expectations of a much larger organisation. Grant applications, newsletters, board papers and monitoring crowd out the work only people can do.
Workflow automation
A system built around how you work. AI agents, each built for one kind of desk work, draft it from your organisation’s own documents, past work and know-how, and a person on your team approves every draft. The aim: a small team with the desk capacity of a much larger one.
How the agents work -
What this looks like
A couple of weeks on a policy submission that ought to be templated, with a newsletter, a board paper and a grant application queued behind it. And even then it may not speak the language decision-makers need.
Workflow automation · Research and evaluation
Submissions built on research into what makes a submission persuade decision-makers, in your voice and from your positions, with every claim checked against its source before you see it. Your writer starts from a checked draft and spends the time on the judgement.
How a job runs, from brief to reviewed draft
What you know
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What this looks like
What the organisation knows lives in people’s heads, inboxes and scattered folders. And AI can’t use what it can’t see.
Workflow automation
One home for your organisation’s documents, past work and know-how, in plain files. Each day’s meetings, drafts, emails and posts are filed in automatically, so it stays current. We call it the brain, and every draft starts from it.
How the brain works
What’s coming
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What this looks like
Parliament, inquiries, the news, and funders who each run their own portal and newsletter. Nobody has time to read it all.
Workflow automation
An AI news desk that reads Parliament, the news and your funders every morning before anyone starts work, so what matters is waiting when you sit down.
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What this looks like
Good advocacy sometimes upsets a few stakeholders. The damage comes from not anticipating who, and then overcorrecting.
Workflow automation
Member and stakeholder intelligence, kept in a CRM (the system that holds your member and contact records) built around your work, so you see the reaction coming sooner.
How the system tracks members
What you can prove
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What this looks like
Member surveys and community consultation without sound design, analyses that don’t hold up, and no budget for a psychometrician, a data analyst or someone who can appraise the literature. The resource isn’t there.
Research and evaluation
Research done properly, so the evidence behind your submissions holds up: surveys and measures built for the question, which you can run yourselves, and findings stated at the strength the evidence carries.
Research and evaluation -
What this looks like
Evaluations paid for by the service being evaluated, rarely with a comparison. And a program built from what the community said loses that intent by the time it rolls out.
Research and evaluation
Programs designed from what the evidence says works, with the theory of change written down so the intent is kept. Then we measure them against a comparison group wherever one is possible, which most funded evaluations skip, so you can show funders and members what a program actually changed. The same measurement shows what the system and the training changed.
How we evaluate
Your people
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What this looks like
Board development teaches which questions to ask; when and why to ask them is rarely taught. And the more senior people get, the less honest feedback reaches them.
Training
Judgement science for boards and leaders: we train the knowledge good decisions draw on, and build in the experience and feedback that make intuition sound. It’s the same science that shapes how your team reviews the system’s drafts.
Judgement for leaders and boards -
What this looks like
The last new tool faded once the funding ended, or once the person who championed it moved on. Nobody turned it into value people could see, and it quietly stopped.
Training
Training on your team’s own work, spread over weeks and timed to when each piece goes live: where AI helps, how to think with it, and how to review its drafts well. Every build comes with an adoption plan: a champion on your team, results people can see, and use built into the working week. We train for the dabbler and the sceptic, which in a small team is often everyone.
Training -
What this looks like
As AI takes on more of the routine, thinking, reasoning and social and emotional skills matter more, in communities as much as in offices. And generic thinking-skills training rarely carries over to real life.
Training · Research and evaluation
Community capability programs, delivered through your member organisations. We’d build them into the places people already gather and the decisions they already make, and measure them to show what changed. Grants can fund them, with you applying and us delivering through your members.
Community programs
Ten answers, one system.
Your organisation’s knowledge sits at the centre. Automation drafts from it and keeps watch on your sources; training is how your people judge those drafts well; research and evaluation shows what the system and the training changed. Because it’s built as one, it gets better with use: what your people change in a draft is saved, so tomorrow’s drafts know about today.
Your day, twice.
In a small team, the desk fills the day. Here is an illustrative day at a small for-purpose organisation, by hand and with the system working. The routine moves; the judgement stays with you.
- Desk work
- Drafted or watched by the system
- A person reviews
- The work only people can do
The same working day at a small for-purpose organisation, 9 am to 5 pm, drawn as two bars. By hand, desk work fills almost the whole bar: the inbox, the grant application, the report to funders, the board paper and the client update. The one block of the work only people can do is about an hour at the end of the day: a call with a partner. With the system, those five are drafted before you start, in a strip above the bar and outside the day: the inbox triaged, the grant application, the report, the board paper and the update. The bar itself is three meetings, with a funder, a partner organisation and a community member, and then strategy, with a short review before each meeting, prompted on what to check. By hand, about an hour of the day is the work only people can do. With the system, most of it. Illustrative: the organisation and its day are made up.
We build what we recommend.
We’re cognitive scientists who treat every AI question as applied cognitive science. Dr Ryan Jesson (PhD, Cognitive Science, UQ) researches judgement and decision-making; Dr David Maij (PhD, Cognitive Neuroscience, University of Amsterdam) did his doctorate on how people learn.
Meet the team
A mock submission, built for training
For the Queensland Alliance for Mental Health (QAMH), we ran a four-session staff series that included a hands-on mock policy submission: a realistic brief, a practice data set on a website built for the exercise, and the organisation’s own template. Staff practised the whole job, from evidence to finished document, with AI as a thinking partner.
TrainingWhy we do this
We work with the small organisation doing good in the world, on purpose.
“That’s the client that fits my moral compass. It’s the client that actually uses the full breadth of my expertise.”
Dr Ryan Jesson, Principal and Founder
Start with a conversation.
Bring the piece of work that takes most of your team’s week. We’ll talk through what would help first, and what wouldn’t.