Solveion is an independent AI consultancy. We train teams, advise on where AI fits, and build custom systems that stand up to daily use.
What we do
Different starting points, same goal: AI that pays for itself in your organization.
Hands-on programs built around the work your team already does, not generic demos. People leave with skills they can use the next morning.
Learn more →Honest advice on where AI helps your business, and where it does not. We help you pick the few problems worth solving first, then help you solve them.
Learn more →Assistants, automations, and retrieval systems built around your data and your processes. Designed for daily use rather than for a demo.
Learn more →How we work
We start by learning how your business actually runs. Once a problem is well understood, the right use of AI tends to be obvious. Until then it stays invisible.
We look for the smallest version of an idea that can prove itself in real use, ship that first, and let the results decide what comes next. Long roadmaps can wait.
Your team should be able to understand, run, and extend whatever we build together. If you would need us around forever, we did something wrong.
Short essays from our work on AI adoption in practice. No breathless predictions.
Part one of a four-part explainer on post-training. What LoRA actually does, why it changed the economics of adapting a model, and the decision rule for when a business should post-train and when it very much should not.
Three futures are usually proposed for AI watermarking: normalisation, an arms race, or everyone running local models. All three are already happening, to different people. The evidence says the arms race is over, and what the mark now measures is not honesty but sophistication.
Every purchase we have made on an assistant’s recommendation has been mediocre, and the reason is structural. One of these jobs has a verifiable answer and the other depends entirely on trusting sources that are now being manufactured on purpose.
Claude now embeds an invisible watermark in everything it generates, worldwide, with no opt-out. The good case is real. The bad case is that you did not choose it. The ugly case is that the mark proves a machine touched the text, not that a machine wrote it.
Before you ask
No. Most of our work starts exactly where you are: messy data, no strategy document, a few experiments that went nowhere. Understanding that starting point is part of the job.
We are deliberately independent. We work across the major model providers as well as open-weight options, and we recommend whatever fits your constraints on cost, privacy, and capability. We resell nothing, so the advice is the product.
It usually begins with a short conversation about what you want to achieve, followed by a focused first piece of work. That might be a training session, an assessment, or a small working prototype. We keep first engagements small on purpose, so both sides can judge the fit on evidence.
Yes, and we have. Some problems are better solved with a simpler process, better tooling, or no change at all. Saying so early costs you less, and it costs us less than a project that should never have started.
A short conversation is usually enough to tell whether we can help. If we can't, we'll say so plainly.
