Three levels of working with AI
First help AI understand the business, then make it a steady collaborator, and finally let it take part in running the process
Say it clearly
AI can only get it right once you've explained what needs doing
Where most people stop
Let it see what it needs to see
Output is only steady when AI has the right materials and context at hand
What AI Configuration solves
About AI Configuration →Put it in harness
Handing off multi-step work needs a source of truth, memory and a division of labor behind it
This page's service works at this level
What we decide with you
Decide where to move first
The hard part isn't the striking. It's knowing where to strike.
Most things stall not because no one is doing the work, but because no one dares to decide which way to go. The first thing we do isn't to start building. It's to settle that question: which steps are worth handing to AI, which shouldn't be touched yet, and which to start with. Choose the wrong direction, and the faster you go, the more you waste.
Where it applies
Redesign, rather than build on old foundations
We don't build where we can't see clearly. We can only take responsibility when we set the foundation together with you.
We don't add to old systems we can't see into. Why someone else's system was built the way it was, what can be changed, what collapses at a touch: we don't know, and when something goes wrong in a place like that, no one can be accountable. So we redesign that part around your business instead. This also means that if what you need is a feature added to an existing system, we're probably not the right partner.
Where it applies
Leave you the reasoning
We deliver not just the finished thing, but why it was done this way.
Which steps were the key calls, which paths were ruled out, which pitfalls you'll meet when you carry on yourself: we hand all of that over too. The approach you can sum up in one sentence is often the hardest sentence to arrive at. We leave you the process that got there, not just the conclusion.
Where it applies
An agent we built for ourselves first
The most convincing way to show this isn't a demo. It's something we use every day. We were the client for what follows: it ran, and kept running, on our own business before we talked about it. It's the result of going through the three steps above: deciding where to move, redesigning that part, and leaving the reasoning behind as guidelines.
A customer's history is scattered across past orders and earlier conversations, and judging it depended entirely on personal experience. A veteran sees at a glance what matters; a newcomer digs through records for ages and still misses things. Training alone can't fully fix this. Experience can't be copied, but the basis for judgment can.
AI does the homework on each customer
Open a customer's page and AI analyzes their history, then offers risk flags, recommended actions and suggested wording. No one has to judge from scratch; they just confirm or correct AI's draft.
AI drafts after-sales replies
AI first polishes after-sales replies to match our service guidelines, aligning wording, boundaries and approved answers, and a person checks before sending. Speed isn't the point. The point is that even a newcomer won't say the wrong thing.
How the guidelines behind the wording are built is covered in the scenarios on the AI Configuration pageAI reads the business numbers first
Business data scattered across the system is pulled together by AI into a daily reading of what's normal and what needs attention. Managers trigger it when they need it and see at a glance where to look.
Taken apart, nothing here is magic: who makes the call, which criteria they use, at which step AI must stop and hand over to a person, which link to check when something goes wrong. Every step was decided and written down in advance. The hard part was never getting it to run. It was deciding all of this first.
AI drafts, people confirm. The system sets the floor for judgment, and the ceiling stays in human hands.
How we work together
Prototype first, validate fast, keep the cost of trial and error low
Discovery
1–2 days
Requirements analysis
Solution design
2–3 days
Custom plan and scope
Prototype
2–5 days
A working prototype you can try
Iteration
1–3 weeks
The complete agent
Deployment and handover
1–2 days
Deployment, training and documentation
How we scope a project
Set the direction
Not sure yet whether to do it or where to start? Settle that first.
Define the plan
The direction is clear and you need a workable design with clear boundaries.
Decide and build
From design to delivery in one go, with the reasoning handed over too.
We suggest confirming the business process, knowledge foundation, task boundaries and acceptance criteria first, then designing the custom agent in phases.
Take the 4-question self-checkCustom Agents rest on AI already understanding your business. If your business materials, prompt rules and process boundaries aren't clear yet, we suggest starting with AI Configuration. For projects with clear needs and simple scenarios, the two can run together.
About AI ConfigurationFAQ
Hands have to grow out of the brainto know where to push.
Build the brain first: see the Intelligence Hub