AI consulting
Your people build it. We make sure it holds.
Somebody on your team is already building with AI tools. We set up the accounts, the repository and the hosting, then teach them to work in it without running up the bill or shipping something you cannot put customer data in.
- Your accounts, your repository
- Several people building at once
- Bring your own model, so the bill stays sane
- We can take it over if you run out of time
Who this is for
You do not need us to write it. You need to know it will hold.
Consulting is the right shape when the people are already there and the knowledge is the missing part. If nobody on your side wants to build, the other page is the honest answer.
- 01
Somebody inside already started
A prototype exists. It looks right, it demos well, and nobody is sure what happens when real customer data goes into it.
- 02
You have developers, not AI habits
Your team can code. They are guessing at which tools to use, what to let the model do unattended, and what the monthly bill will be.
- 03
You want the knowledge to stay
Paying somebody to build it means the understanding leaves when they do. Doing it with your own people means it does not.
What we set up
Day one is the plumbing, in your name.
Before anybody writes a feature, the foundation goes in. All of it in your own accounts, so nothing here is ours to hold.
- 01
The accounts
Hosting, the model providers, the database and the repository, opened in your company's name with your billing on them.
- 02
A repository several people can work in
Two people on two features do not collide. The guardrails merge their work cleanly instead of overwriting each other, which is what lets a small team move at once.
- 03
Hosting you can leave
It runs on ordinary cloud infrastructure and the codebase is portable, so moving it somewhere else later is a move, not a rebuild.
- 04
The model setup
External models plugged in directly rather than the hosting platform's own assistant, which is where that kind of platform makes its margin.
The part nobody warns you about
AI tools are cheapuntil they are not.
The default setup bills you for the convenience, and a team learning on it can spend a frightening amount without noticing. Compute on the projects we are running now could reach twenty to thirty thousand dollars on the default path. It does not, because the models are plugged in directly and the work is structured so the expensive tools are used on the expensive problems.
- Where the money actually goes
- Which tasks justify the strongest model, and which ones never did.
- What to run unattended
- Letting a model work alone is fine in some places and expensive in others. Your team learns which is which.
- A bill you can predict
- The accounts are yours, so you see the spend as it happens rather than in somebody else's invoice.
What we teach them to check
A demo becomes software the day it can survive being wrong.
The tools write working code quickly. What they leave out is the same list every time, and a team that knows the list stops shipping the same four problems.
- 01
Who can see which record
A prototype usually has one user who can do everything. Real software has roles, and somebody has to decide them.
- 02
What happens when it breaks
Backups that have actually been restored from, not backups that exist in a settings page.
- 03
Where the data sits
Encrypted moving and at rest, and stored somewhere your industry will accept when somebody asks.
- 04
Real data at real volume
What works on twenty records has to work on twenty thousand, and on the messy export from your last system.
- 05
Reading what the model wrote
The habit that matters most. Not approving a change because it ran, but because somebody understood it.
How it runs
A weekly working session, and we are reachable in between.
01
A call where you show us the work
What has been built, what it runs on, and who is doing the building. Recorded so your team can go back to it.
02
We stand the foundation up
Accounts, repository, hosting and models, with your team watching it happen rather than receiving it finished.
03
Weekly, with your build as the material
We work on what your team is actually building that week. More often when there is a deadline.
04
You keep going without us
The point is that the sessions stop mattering. If they never do, we were teaching badly.
If it turns out your team does not have the time after all, we can take the project over and finish it. The repository is the same either way, so nothing is lost in the handoff.
Show us what your team already built.
Twenty minutes on the prototype, the stack and the bill. We will tell you what it still needs, whether it is worth finishing, and whether your people can get there with a few sessions or whether you would rather hand it over.