Questions, answered plainly

You can, and we'll leave your team able to. We make the path shorter because we've done this before and we bring the governance infrastructure. The same team stays with you from the first session to production.

A platform asks you to trust the machine. We start from your own experts and cite where every answer comes from, with an accountable person behind every decision that matters.

A consultancy bills weeks of analysis and leaves you a deck. You get something usable in the first working session, and that session is credited in full if you go on to work with us.

No. We work with solo founders as well as large teams.

It depends on where you are today. If you have the tools but the work hasn't changed, even if people use them daily, start with AI-absent → AI-enabled. If your first critical workflow already runs on agents but most work still waits to be asked, start with AI-enabled → AI-native. If you need AI leadership on your team first, look at fractional leadership. If you're not sure, the working session will tell you which fits.

A full-time AI executive is hard to find and slow to hire, and most companies don't yet know what the permanent role should be. A fractional seat puts someone accountable for AI on your leadership team now, delivers the first outcomes, and can write the mandate for the permanent role if you want one.

One focused day with us. We dig into how your work runs and pick one real workflow together. You leave with a working prototype of that workflow and a view of where you are on the path. For each part of the workflow you also get a build, buy or partner call and who is accountable for each decision, plus our advice on where to go next. If your team is new to AI, we start with the basics on your own work: briefing AI well, writing down the context it needs and organising work so it builds up.

One focused day. We'll tell you beforehand who should be in the room.

Talk to us. We scope the work with you during the working session, which is credited in full if you go on to work with us.

You get something usable from the first session. Once we've seen your workflow there, we tell you how long it will take to reach production.

Either can. We agree who engages us, and on what terms, with you before anything is booked.

If the agreed workflow needs it to work, it goes into the plan. We don't turn every discovery into a change order. Genuinely new work gets scoped with you, in the open.

A person on your side. There's an accountable person behind every decision that matters, and agents can handle some approvals that person has permissioned. Before anything runs, we agree who is accountable and what each agent may approve.

Your data stays in the systems you already use. We work inside them, with only the access you give us, under NDA.

We don't offer a guarantee. You see a working prototype on one of your own workflows in the first session, before you commit to more, and we tell you early if a workflow isn't working.

It shouldn't. Any vendor can raise your usage. We design for value that grows faster than your AI bill.

You can. We build it so you can own it, and we operate it for as long as that's useful. By default we run the system and your brain for you as a managed service, and you can take it over whenever you like. Self-hosted is available, so you can run it yourself with your own agents plugged in.

Yes. No vendor lock-in: bring your own agents or use ours, and swap them whenever you want.

The ability to run it yourself. Your team knows how to manage what we installed and can keep improving it without us.

It's the value you don't get while AI isn't running a workflow it could take on. Put an average monthly value on each workflow, multiply by how many there are, then by the months you wait. It's counted before you spend anything on implementation. Work out yours.

Multiply the number of AI opportunities by the average monthly value of each one. That's what you lose each month you wait. Multiply it by the months you wait to get the total. We also show half of the total, in case value builds up slowly. That half is an assumption, not a measured figure. Run the numbers.

Start with one workflow. Count the hours AI would save on it each month and multiply by what those hours cost you. Add any revenue it would bring in sooner or errors it would stop, then take off what AI would cost to run. With several workflows, use the average, and don't count the same hours twice. If you can't defend a number, use the low end.

Waiting has a cost too, but it's harder to see. Every month you wait, the value of that work goes unclaimed and your team gets no practice with AI. The tools will keep improving either way. Start on one real workflow now and build it so you can swap tools later without starting over.

Show them what waiting costs, in dollars, on work they recognize. Pick one to three workflows, put a monthly value on each and state what a 6-month wait would lose. Then name who is accountable for each one. The calculator writes a line you can paste into the deck.

Add up what the rollout costs in year one: tools, training, platform and security, and the output lost while people learn. Then work out the value of the time AI saves: people × cost per person × the share of work time AI saves, counted only for the months after people have learned. Net benefit is that value minus the cost, and ROI is net benefit divided by the cost. Run the numbers.

Only if you use it. Freed hours become money when they go into work you'd otherwise hire for, ship later or not do at all. If nobody decides where the time goes, it disappears into the week. Name the work it goes to before you count it.

It depends on the work, so measure one workflow before you commit. Our calculator starts at 12.5% of each person's work time, the default in DORA's AI ROI calculator. Treat that as a starting point, not a promise, and use your own number once you have it.

People need time to learn new tools and change how they work, so output dips first. That dip is a real cost, and it belongs in the business case. Keep it short by starting with one workflow and the people who own it.

Don't put everyone through it at once. Start with one workflow and the few people who own it, build a working prototype of it in one focused day, and measure what it really saves. Then roll out to the next team, faster each time. The rest of the team gets its gains later, but you find out what AI saves before you commit the full budget.

Count the months until the time AI saves has covered the year-one cost. Nothing is saved while people are still learning, and from year two the tools and platform costs come back, so take those off. With our calculator's starting values, a 50-person team pays back in about 13 months. Your own numbers will move that a lot, which is why we test them on a real workflow.

Look at whether the work has changed, not whether people have tools. Pick one regular workflow and ask how far AI goes in it: does it help people, do some steps by itself, or run the workflow with people making the key decisions? Then check the foundations: someone in charge, AI that knows your business, a way to check its work, and a way to measure the difference it makes. Take the check-up.

AI-enabled means at least one critical workflow is started and run by AI agents, with your people making the key decisions. AI-native means that's how the company runs: agents run the workflows that run your business.

Because a claim needs something to hold it up. If AI runs a workflow but nobody is in charge of it and nothing is measured, the workflow is hard to trust and hard to improve. Each level needs its own basics in place, and the check-up places each area at the level its foundations support, not the one you claim.

The foundation that holds you back most, which is rarely another tool. If nobody is in charge of AI, start there. If AI's work isn't checked or measured, people won't trust it to run a workflow. Then pick one workflow and change how it runs.