Sovereign AI, running on your own infrastructure.

At the frontier, yes, and we'll say so. For grounded answers over curated documentation the gap is narrow, and reranking plus well-structured knowledge closes much of it. Where it helps, a small model fine-tuned on your domain can match larger general models on your tasks. We benchmark candidates on your own questions during discovery, and because the gateway abstracts the provider, adding a hosted model later is configuration.

Sometimes, yes. It falls short when data can't leave the tenancy boundary the way Copilot needs, when professional secrecy applies, when knowledge lives outside Microsoft, or when your permission model or automation goes beyond what it can express. If none of that applies, we'll tell you.

We disclose it up front. When our platform is a candidate, it's scored on the same criteria as everything else, and we'll accept independent review of the scoring. Clients who want full ownership take the open-source route.

You own the source from the first commit, the evaluation set is yours, and a fixed-price handover package is quotable at any point. Switching costs you documentation transfer, not the project.

Speech recognition, the model and speech synthesis all run on your infrastructure and stream into each other, so callers hear a reply in about a second, and they can interrupt at any time. We measure it on your hardware during the proof of concept. Every call opens with a recording notice and a way to continue without it. You choose whether to keep audio, transcripts or neither, and for how long, and consent is logged with the call. We configure this for the rules in each market you operate in.

Only your own. The models run inside your environment, and your documents, questions and answers stay there. If we fine-tune a model on your data, that happens on your infrastructure and the resulting model belongs to you. Nothing is sent to us or to a model provider unless you choose to add a hosted model for a specific use.

Every answer is grounded in your documents and cites its source, so people can check it. During the proof of concept we measure accuracy against an evaluation set built from your own questions, and that set stays with you to re-test any future change.

Yes. Connectors carry each source's permissions across, and departments and roles come from your existing directory. People only get answers from content they're already allowed to open.

A two-week discovery sprint gives you a costed recommendation. A proof of concept on your own content follows in six to ten weeks, measured against acceptance criteria agreed up front.

It depends on the models, the number of users and whether you run voice. We size it during discovery. Small fine-tuned models need far less than large general ones, which often keeps the footprint modest. If you aren't ready to buy GPUs, you can start with a hosted model through the gateway and move on-prem later without rebuilding.

That risk is designed out. Commitments are staged, so your exposure is bounded at every step. You own all source code from the first commit, and the runbooks let your team or another supplier take over. Losing us would cost you documentation transfer, not the project.

Most internal knowledge assistants are not high-risk, but the classification has to be documented rather than assumed. Some automations, such as CV screening, credit scoring or anything acting as a medical device, carry heavy obligations. We tell you where the line is before you cross it.

Europe, the United States and India, from offices in Berlin, Dover and Hyderabad. We design for the rules in each market: GDPR, the EU AI Act and DORA in Europe; HIPAA, GLBA and state privacy laws in the US; and the DPDP Act, RBI and SEBI requirements in India.