Start small. Prove it. Then grow.
We build AI into a business the way we build our own companies: one use case at a time, on real data, with rules that keep the AI in bounds and people placed where they matter most.
Three phases.
Fit check
We look at your systems and where the time goes. If either side says no, there is no contract and no invoice.
Build
We connect to your systems and build the first use case on real data, with your team testing it along the way.
Run and improve
We keep it running, watch the conversations and numbers, and add the next use case once the first one pays off.
How we think about AI in a company.
Build the company around AI
Not another tool bolted onto old processes, but an infrastructure designed for intelligent automation, with people placed where their touch creates the most value.
Rules decide, AI words
Language models are good at understanding and writing. Prices, limits, approvals and anything with money attached are decided by rules you control.
Your systems stay
We build on top of the ERP, CRM, accounting and tools you already use. No replacement project before you see value.
Correct beats plausible
Answers are checked against what was actually invoiced and paid, and the AI hands over instead of guessing.
Human touch, on purpose
We decide together where a person adds the most: difficult clients, high-value moments, culture. Everything else can run on its own.
Client work stays client work
We never build competing products from what we learn in an engagement.
What it takes from your side.
- One sponsor on your side who can make decisions
- Someone who can grant access to your systems and their APIs
- Real examples: conversations, orders, reports, the work that takes time today
- A team willing to test early versions and tell us what is wrong
We take on four to six Labs engagements a year, so each client works directly with the people who build the system.
What Labs builds →Working with us: questions, answered.
How long does an engagement take?
It depends on your systems and the first use case, so we pin it down during the fit check rather than quoting a number up front.
What does the fit check involve?
A look at your systems, data and where time goes, and an honest answer on whether AI will pay off for you. If either side says no, there is no contract and no invoice.
Where is our data hosted?
With enterprise cloud providers, on infrastructure where your data is not used to train public models. Specific hosting requirements are agreed during the fit check.
What happens after the first use case?
We keep it running and improving, and add the next use case once the first one has proven itself.
Have a business that should run like this?
Thirty minutes, no pitch. We’ll tell you honestly if it fits.