Practical AI consulting that gets real business value out of AI — without sending your data to a third-party cloud.
We start with your actual workflows — document-heavy processes, customer support, internal knowledge search, reporting — and build assistants, retrieval systems, and automations that solve them. What sets us apart: we can deliver this on a governed, self-hosted AI platform, so your data never leaves your control and you’re not locked into an unpredictable per-token cloud bill.
What’s included
AI opportunity discovery and use-case prioritization tied to business outcomes
Retrieval-augmented generation (RAG) and internal knowledge assistants built on your own documents
Workflow and document-processing automation
Model selection and evaluation — the right model (cloud or self-hosted) for each use case
Data governance and access-control design for AI systems
Optional sovereign, self-hosted deployment: your data stays on infrastructure you control, at predictable cost
Best for privacy- and compliance-conscious teams that want AI’s gains without handing sensitive data to big-cloud vendors. Delivered remotely, anywhere.
Common questions
What does self-hosted or sovereign AI actually mean in practice?
It means the model runs on infrastructure you control — your server, your rack, or a private cloud tenancy in your name — instead of a vendor's multi-tenant platform. Your documents and prompts stay inside that boundary. Practically it changes three things: who can see your data, what your monthly bill looks like, and whether a vendor can change the terms underneath you.
Will our business data be used to train someone else's model?
Not on a self-hosted deployment — the data never leaves your infrastructure, so there is nothing to train on. Where a cloud model is genuinely the better fit for a use case, we tell you which vendor, what their retention terms actually say, and exactly what would be sent, so the call is yours and you make it with the terms in front of you.
Do we need to buy GPU servers before we can start?
No, and you should not. We start with discovery — which of your workflows would actually benefit — then match each one to the smallest thing that solves it. Some use cases run fine on ordinary hardware, some want a GPU, and some are better served by a cloud model. Hardware is a conclusion of that work, not a prerequisite for it.
We know we should be doing something with AI, but not what. Is that a problem?
That is the normal starting point, and it is what discovery is for. We look at where your team actually spends time — document handling, lookups, support responses, reporting — then rank the candidates by value and effort. You end up with a short prioritized list, including the ideas worth skipping, rather than a tool nobody asked for.
