Sovereign AX: Frontier-Class AI With No Data Egress
Run the model where your data already lives
The core promise of VIDRAFT's Solutions and Services line is simple: run frontier-class AI entirely on your own infrastructure, with no data egress. For banks, hospitals, and government, the blocker to adopting AI is rarely capability -- it is that the data cannot leave the building. Sovereign AX (AI transformation) is built around that constraint instead of against it.
What sovereign AX includes
- On-prem foundation models. We deploy our own models inside your environment. Weights, inference, and logs stay on hardware you control. Nothing is sent to an external API.
- A sovereign VLM. A vision-language model for document, image, and multimodal understanding, currently in collaboration discussions -- for example with DeepBrain AI. The aim is frontier-class multimodal capability that never phones home.
- On-device deployment. Beyond the data center, our on-device model work pushes inference to edge hardware -- kiosks, workstations, embedded devices -- where connectivity or policy rules out the cloud entirely.
- A full service catalog. Fine-tuning on your data, evaluation, and integration, delivered as engagements rather than as a black box.
| Concern | Cloud AI API | Sovereign AX |
|---|---|---|
| Where data goes | To a third-party endpoint | Stays on your infrastructure |
| Model location | Vendor cloud | Your data center or device |
| Auditability | Vendor-controlled | You hold the logs |
Why a small team can offer this
Our whole model-building method is capital-efficient by design -- evolutionary merging and on-device optimization aim for frontier-class behavior without frontier-scale compute. That same efficiency is what makes on-prem deployment realistic: a model tuned to run on a constrained footprint is exactly the model an enterprise can host itself, without renting a hyperscaler's fleet.
Where it fits
Sovereign AX makes sense when the data is the whole problem: regulated records, classified material, proprietary corpora that legal will never let touch a public endpoint. If your workload is casual and your data is already public, a hosted API is probably cheaper. We would rather tell you that than sell you an on-prem deployment you do not need.
Honest scope
This is a services-and-deployment offering, not a shrink-wrapped product you download and run in an afternoon. "Frontier-class" describes the target and our benchmark and leaderboard results, not a guarantee that a given model matches the largest closed labs on every task -- capability depends on the workload, the domain, and the fine-tuning data you provide. The sovereign VLM is in collaboration discussions; naming DeepBrain AI describes an active conversation, not a shipped joint product or an endorsement. On-prem deployment moves real integration work to your side of the fence: hardware, security review, and tuning are part of the engagement. What we do commit to is the architecture -- no data egress -- and to being explicit about what is production-ready versus in progress.
More: https://vidraft.net
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