AI Solutions
AI has no ceiling right now - if you can imagine it, it can most likely be built. Made-to-measure AI products and automations of real day-to-day work, with a lot of weight on one thing: the privacy and security of your data.
The useful question is not "should we use AI" but "which hour of our week should stop being manual". UpCreation builds assistants, LLM-powered features, and RAG systems over your own documents - tools that answer with your knowledge, in your tone, inside your processes.
Privacy decides the architecture. When data is sensitive, models run self-hosted - GLM or Kimi on your infrastructure - so nothing leaves your walls. When speed matters more than secrecy, frontier models like Claude do the heavy lifting. Most real systems mix both, routing each task to the right tier.
Python services, clean APIs, and evaluation harnesses make the results measurable - AI that creates value you can point at, not a demo that impresses once.
What you get
- Bespoke AI products shaped around your goals.
- Automation of repetitive work - hours back for your team.
- Assistants, LLM features, RAG over your own data.
- Privacy-first: self-hosting and strict data control.
- Evaluation and monitoring, so quality is measured - not assumed.
Questions, answered.
The things clients usually ask about AI Solutions - answered straight.
Will our data end up training someone's model?
No. Architectures are designed so your data stays yours: self-hosted models where privacy is critical, strict API data-handling settings elsewhere, and clear boundaries documented for every integration.
Self-hosted or cloud models - which is right for us?
It depends on the sensitivity of your data and the quality bar of the task. Self-hosted GLM or Kimi keeps everything in-house; frontier cloud models offer top quality for less sensitive work. Many projects route between both.
What does an AI project cost?
Small automations start in the low thousands of euros; full products scale with scope. Every engagement starts with a short discovery that produces a concrete estimate before you commit.
How do you keep AI answers reliable?
With retrieval over your verified content (RAG), constrained outputs, and evaluation sets that measure quality release after release. AI is engineered here, not just prompted.
Can AI be added to our existing system?
Usually yes - through APIs, webhooks, or an MCP server that exposes your system's capabilities to AI clients safely.