A workspace where your team and AI agents do the work together.
The work you do here teaches the agents. Each project makes them better at solving the next one.

A place to think. Bring in what matters, work it through with your team and AI, and keep sharpening until it's done. Each piece of work feeds the next.
You work on a canvas, and everything you put on it is a card. Cards hold the things that matter to the work, like a question, some data, an answer from an agent, or a decision you made.
A look at the canvas
From brief to board pack.
Four moments from one piece of work: an example about electrifying a fleet.
Frame the problem. The brief, the people involved, and the constraints, all laid out before any work begins.
How it works
Lay the problem out
Put the question on the canvas, along with the data, the constraints, and the people involved. Move the pieces around until you can see how they fit together. The whole problem sits in one place, not spread across different files and threads.
Bring in what's already known
Past work, earlier decisions, things other teams have done. The workspace pulls in what's relevant so you build on what's already there, not just on what someone happens to remember.
Work it through with agents
Hand pieces of the work to AI agents. They do the heavy lifting, you steer. You can see how they got to every answer.
Carry it forward
What you finish, what you learned, how you solved it. All of it stays in the workspace, ready for the next piece of work.
The most important work is the hardest to repeat. Every problem is a bit different. The people are different. The constraints are different.
Start with the problem, not the answer.
Frame it well, and the right way to solve it becomes much clearer. The workspace is built to start there.
Why this works
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Nothing gets lost.
Everything you and the agents do stays in the workspace, ready to pick up again.
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The agents learn your way of working.
Over time they pick up how your team solves problems, not just generic patterns.
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Each piece of work feeds the next.
What you finish becomes a starting point for whatever comes next.
"A problem well stated is a problem half solved."
Charles Kettering, Head of Research, General Motors
Worked example
Building a software product, week by week
One project, week by week. Every piece stays in one place: the brief, the prototype, the user feedback, the strategy, the backlog. Nothing gets lost between tools.
- Week 01 Brief A short note about what to build and why.
- Prototype A rough version to show and test.
- Week 02 Tested with feedback What users said. What changed.
- Week 03 Vision, strategy, roadmap Where you're going. How you'll get there.
- Week 04 Brought in marketing, finance, and leadership Talked them through it. Got their input.
- Week 05 Picked the approach and the tech What you'll build, and how.
- Backlog The list of work, in order.
- Week 06 Build starts Engineers and agents pick up the backlog and get going.
Everything for this product lives in one place. What worked, what didn't, how the team thought about it. All of it becomes something the next product can build on.
Three parts of the workspace
How your team thinks about problems
Your strongest people know how to frame a problem before they solve it. They know what "good" looks like. The workspace turns the way they think into something everyone can use.

Where the work happens
Bring the problem, the data, and what's already known into one place. AI agents do the heavy lifting. You see every step and stay in control.

What you've already figured out
Past problems and how they got solved, all connected. When a new problem looks like one you've solved before, the answer is already there. Nobody has to ask the same question twice.

Built by people who've done this work for twenty years
We didn't start with a product idea. We started by doing this work for clients, for twenty years. Then we built the tool we wished we had.
AI work for big companies
Satalia has delivered AI for big organisations including WPP, PwC, DFS, and Waitrose. Work that has to be rigorous, trusted, and useful in the real world.
Grounded in research
Founded by Dr Daniel Hulme, an expert in AI and future technologies. Our work is grounded in optimisation science and decision theory, not hype.
We help companies set the rules for AI
We're actively shaping how big companies govern AI agents, including WPP. Trust, transparency, and human oversight matter to us from day one.
We use the workspace ourselves
We use it to do our work. It gets sharper with every problem we solve. Now your team can use it too.
"But our work is too contextual to systematise."
We're not trying to systematise the work. We're capturing how your team frames and solves problems, so the next one starts further ahead.
Questions
Your company already solves hard problems. Make sure each one helps the next.
Every problem you solve teaches you something. Satalia Solve keeps what you learn so the next one is easier.
Book a 20-minute walkthrough. We'll show you how it works using a real problem from your business.



