What people ask before every project

With one question somebody has been asked and cannot yet answer: is this AI worth what it costs, are we ready for the rules that apply to us, or where is this allowed to run? We start from that question rather than from a menu. We look at what you already have in place, what is already running, and what the answer would need in order to be credible, then put a recommended first step in writing. You keep that whatever you decide to do next.

Measure one AI system you already use, on one workflow, against how the work ran before. It produces a number your finance team can check and a method you keep. On the deployment side, the smallest thing is one model on one machine inside your network, answering from one team's documents, with the source printed on every answer.

No. Most useful systems start with the documents and records one team already relies on, often a few hundred files rather than an archive. How current and how well organised they are matters far more than how many there are, because a small, well kept set produces answers people trust, while a large neglected one produces confident answers drawn from superseded versions. Working out which of your sources are in good enough shape is part of the first piece of work.

No, and most useful conversations start that way. A half-formed problem described in your own words is plenty to work with, and working out what it is closest to is our job. Tell us what somebody has been asked, or what work you would like to take off a team's plate, and we will say what a sensible first step looks like and whether it is something you could reasonably do yourselves.

The model runs inside your environment, on your hardware or in a cloud account in your own name, and what is sent outside it, if anything at all, is your decision to make rather than ours. The questions people ask and the documents it reads stay where they already are. Where policy allows no outside connection whatsoever, the whole thing runs fully disconnected, updates included, and we set up the process for those before handover.

It depends on the model size and how many people ask at once. Many internal assistants run comfortably on a single modern accelerator, and some run acceptably on ordinary servers. We measure your own work and size it properly before you buy anything. Owning or renting the hardware.

That is the normal case rather than the exception. We deploy inside your own cloud account using your networking, your keys and your identity provider, and we connect to the systems you already run through the interfaces those systems already publish. Nothing we build asks you to replace or reconfigure something that currently works. In most projects the largest part of the effort is fitting properly into what is already there.

You own all of it: the environment, the models, the measurement method, the logs and the documentation, which is written for your team rather than for our next project. No component can only be operated by us, and no licence has to be renewed with us. Ongoing support is available if you want it, a light review every so often is common, and taking the whole thing in house is completely normal.