About
We fix how your team uses AI. Then we build what's worth building.
The long story: what applied AI means, and how we workTutto trains teams to use AI properly, then builds what's worth building. No slide decks, no frameworks: we work on your actual jobs until they stop repeating.
Most firms we meet have the same problem. Staff already use ChatGPT or Claude, and it saves them nothing: ask, fix, ask again, burning tokens on the way. Nobody set the rules, defined what good looks like, or decided what to hand over.
So we do it in that order. The rules: what goes in, and what never leaves the building. A KPI for each job, so output is judged against a standard. What to hand over. Then the build: tools, skills, automation and agents, from an MCP bridge into SharePoint to a document platform that reviews 150,000 privileged files without a single byte leaving the building.
What we believe
Most AI problems aren't AI problems. They're management problems. Nobody would hand an intern a job without a brief, a standard and a sign-off. And that's exactly how most teams hand work to AI.
The firms that come out ahead won't be the ones who bought AI first. They'll be the ones who set the rules, wrote down what good looks like, and knew what to hand over. That work is unglamorous. It's also where the time comes back.
How we work
We're small by design: you work with the people doing the work, not a junior team briefed secondhand. Training is a fixed programme with a guarantee; build work is scoped in one call. Everything runs on your own work, and ends with something that runs.
It starts with a free 15-minute call. Bring the job you keep repeating. We'll tell you honestly whether we can help.
Who you work with
Roxanne Northover
Operations, process, practical AI
Rox works with us on every engagement. Her background is project management, business analysis, operations and process improvement, built on a Master's in Law that gives her a real grounding in research, governance and risk.
What she sees better than anyone is the gap between how a business is supposed to run and how the work actually happens: leadership describing one process while the people doing it every day get by on spreadsheets, inboxes and workarounds nobody ever wrote down. Law taught her the same lesson early: what a system is meant to do and what it does in practice are two different subjects.
She has worked across AI governance, CRM implementation, document management, reporting automation and operational process design. She starts in the same place every time: what is really happening here, and where is the friction coming from?
She does not assume technology is the answer. Sometimes it's automation, sometimes a better system, sometimes simply a clearer process. That judgement is what makes the AI work land.
Daniel Forsthofer
Applied AI, architecture, delivery
Daniel founded Tutto. His background is philosophy, not as an academic pursuit but as the practical grounding for working with technology: what does it actually mean to use these systems, what is the return, and what is in our control?
That last question does most of the work. AI is not a choice. It is the product of economic and scientific conditions that no single decision will stop. What is a choice is whether you understand it and whether you use it well. He works with people who want to know what is actually happening: what these systems are, what they are genuinely good at, where they fail, and what a working setup looks like in their context.
And he builds. Most engagements end with something running in production: a bridge into SharePoint or Salesforce, a document platform that never leaves the building, a process that stopped needing someone to shepherd it. Ship the working thing rather than present the plan for one.
Fear is the starting condition here, not the enemy. He still feels it. The answer isn't bravado; it's a first step, then another. His role is not the expert above you but the experienced first-stepper beside you.
Ready to talk?
Fifteen minutes, free. Bring the job you keep repeating, and we'll tell you honestly what we think.