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Assistant over your documents
A chat that answers from your manuals, contracts or catalogues and cites which document each answer came from. Built with RAG, so the model answers from your information instead of what it thinks it remembers.
We do not sell AI transformation. We take one concrete task that eats hours of someone's week today and solve it with a language model connected to your own data.
They all share one thing: a measurable repetitive task and your own data to feed it.
A chat that answers from your manuals, contracts or catalogues and cites which document each answer came from. Built with RAG, so the model answers from your information instead of what it thinks it remembers.
A bot that handles the same repeated questions on your site or WhatsApp, and hands the conversation to a person the moment it goes off script.
Invoices, delivery notes, resumes or forms that someone types in by hand today. The model extracts the fields into your system, with a review screen before anything is confirmed.
Emails, tickets or complaints that arrive mixed together and need sorting. They get tagged by topic and urgency, and each one goes to the right team.
Product descriptions, meeting summaries or spec sheets, generated in your tone and reviewed by a person before they go out.
Searching by meaning instead of exact wording. It matters when you have thousands of products or documents and plain text search finds nothing.
Order matters: most AI projects fail because nobody defined what working well meant.
We start with a two to three week pilot on a narrow case using your real data. It is cheap and it lets you see the result before committing to a large project.
Together we define what accuracy rate makes it worth having. Without that number there is no honest way to say whether the system works.
Anywhere money, contracts or customers are involved, we leave a human review screen. The model proposes, a person confirms.
We choose the model by cost, speed and data sensitivity, and we explain why.
With the enterprise APIs of the major providers, what you send is not used to train their models. Even so, before we start we define what information leaves your infrastructure and what does not, and if the case requires it we can work with models running on your own server.
There are two costs: development, which is one-off, and model usage, which is pay per use. During the pilot we measure the real consumption of your case and give you an estimated monthly figure before scaling.
Yes, and anyone telling you otherwise is selling smoke. That is why we set an acceptable accuracy threshold, measure against real cases and keep human review anywhere it matters.
It does if there is a repetitive task eating hours today. If it is ten minutes a week, it is not worth it and we will say so.
Yes. We connect to WhatsApp, email, spreadsheets, ERPs and in-house databases, using n8n for integrations or custom development when we need more control.
The pilot is two to three weeks. A production system, 6 to 10 weeks depending on how many systems have to be connected.
Almost no project fits one category only. These are the other fronts we cover.
Corporate sites, landing pages and online stores that actually ship.
Android and iOS apps, published on the App Store and Google Play.
We connect your systems and take the manual work out of the middle.
Subscription products, from MVP to the production release.
If your team repeats something every single day, it can probably be automated. Write to us and we will look at it together.