Artificial Intelligence
We apply AI where it pays for itself, and say so plainly when it will not. A model that is right most of the time is only useful if being wrong is cheap and visible.
AI applied where it pays for itself: document and image extraction, support and email triage, classification, and internal assistants wired to your own data.
What we do
Artificial Intelligence
The work usually starts with document and image extraction, support and email triage, classification, or an internal assistant answering from your own data. Each of those has a measurable before and after, which is what makes it worth building.
We set the accuracy bar with you before the build, define what happens on a low-confidence result, and keep a human in the loop wherever a wrong answer costs money. Nothing is shipped as a black box with no fallback.
Your data stays under your control: we agree what may be sent to a model provider, what must stay inside your own infrastructure, and how long anything is retained.
Artificial Intelligence
What you get
A defined use case with the accuracy bar and the cost of being wrong written down
An evaluation set built from your real documents or tickets, not from samples
Low-confidence handling and human review built into the flow from the start
A written data-handling boundary: what leaves your infrastructure and what does not
Monitoring so quality drift is visible after launch, not discovered by a complaint
Our Expertise
Typical stack
How we work
How a project starts, and how it is priced
Tell Us The Problem
Reach us by email or WhatsApp and describe what is broken or what you want built. We reply with our reading of the problem and the options.
Get Scope And Price
You receive a written scope, a timeline and a fixed price, including what is explicitly not included. No surprise line items later.
We Build And Hand Over
We deliver on the agreed timeline, with documentation and a working deployment. You can keep us on for support, or take it in-house.
How pricing works
The price follows the scope. Once we understand the problem, you receive a written quotation that states what is included, what is explicitly not included, the timeline and the price.
If something outside that scope turns up during the work, we write it down and tell you before anything changes. Extra work is quoted separately and only starts once you agree to it.
You can also stop after the quotation. Reading our scope does not commit you to building with us.
FAQ
Questions buyers ask us
How much does an AI project cost?
It depends on the use case, the volume of documents or messages, and how much human review the flow needs. We write down the accuracy bar and the cost of a wrong answer first, then quote a fixed price for that scope. Usage fees charged by a model provider are a separate running cost, shown before you decide.
How long until we see results?
The timeline is written into the quotation. The first milestone is usually an evaluation set built from your real documents or tickets, because it shows whether the accuracy bar is reachable before the full flow is built.
Who owns the code, the prompts and the data?
Ownership of the source code, prompts and configuration is agreed in the quotation before work starts. Your data stays under your control: we agree in writing what may be sent to a model provider, what stays inside your own infrastructure, and how long anything is kept.
What happens after launch?
Quality is monitored so drift is visible, not discovered by a complaint. Under managed service we keep watching it, handle incidents and adjust the flow within the monthly change budget. Or your team takes it over with the evaluation set and the documentation.
What happens when the AI is wrong?
That is designed before the build. Each flow has a defined path for low-confidence results, and a human stays in the loop wherever a wrong answer costs money. Nothing is shipped as a black box with no fallback.
Start with one conversation
Describe the problem and you get our reading of it, a scope and a price. If we are not the right fit, we will say so.