AI That Runs InsideYour Own Network

Generally not, at the top end. Open models have improved substantially and are more than adequate for retrieval, summarisation, extraction and classification, but the largest commercial models still lead on complex reasoning. We benchmark on your actual tasks so you can judge whether the gap matters for your use case.

It depends on model size and how many people use it at once. A departmental knowledge base is a very different specification from a system serving thousands of concurrent users. We size it from your measured workload and show the working.

Yes. Once deployed, the system needs no outbound connectivity to operate. Internet access is only required for updates, which can be applied on a controlled schedule.

There are three parts: hardware or private cloud, our implementation, and ongoing support. It is a higher upfront cost than an API-based solution and often a lower long-run cost at high volume. We model both against your expected usage.

Sometimes. Most general-purpose business servers lack the GPU capacity for a language model at usable speed. We assess what you have before recommending a purchase.

Either your team or ours. We train your administrators as part of delivery and offer a support agreement covering patching, monitoring and model upgrades if you would rather not carry it internally.

Yes, and it is often the sensible path when your data permits it. We build the application layer so the model can be swapped, which avoids rewriting everything when you migrate.