Service
A secure foundation and a technical owner for your first AI build.
What local AI requires.
Many companies expect to buy a desktop AI machine, download an open model in an afternoon, cancel the cloud AI subscription, and run everything locally. Machines that look alike on a spec sheet behave very differently on a real workload: memory size decides which models fit, memory bandwidth decides how fast they answer, and a box built for prototyping is not built to serve a team. Buy the wrong one and the purchase is sunk, along with the weeks spent trying to make it work or replace it.
The models that fit on one machine are generally a step behind the frontier models in the cloud, and the gap shows on long documents, multi-step reasoning, and careful writing. A single machine works well for one person, but responses slow as more people share it.
Beyond the model, someone has to run the inference server, connect it to company identity, log who asked what, keep models and drivers current, back it up, and watch it. If nobody does that work, people go back to the free AI tools on their laptops, where the company never meant its data to go.
HIPAA still applies on premises.
The HIPAA Security Rule applies to electronic PHI wherever it lives. Every safeguard still applies on premises, and the company is responsible for access control, audit logging, encryption, patching, backups, and the risk analysis that covers them.
Anthropic, OpenAI, AWS, and Microsoft all offer BAA-covered options, but coverage varies by product, platform, and feature. The same model can be covered through one route and not through another, and vendor documentation does not always agree, so Inoculis confirms coverage for the exact route in writing before any PHI moves. The deployment choice should follow from the workload, the data, and who will run the system.
When on-premises is the right answer.
On-premises can be the right choice when a contract requires data to stay on site, when steady high-volume inference makes cloud costs compound, or when a workflow cannot tolerate the round trip. The common mistake is buying hardware before anyone has measured the workload, chosen the models, and named who will run the system.
A hybrid setup runs sensitive or high-volume work on the local machine and sends harder tasks to a frontier model under a BAA. A written rule says which data goes where.
Christian sizes the system before any hardware is ordered. He tests candidate models on comparable hardware using synthetic data, then sizes compute, memory, storage, and the inference stack to the measured workload. Once the appliance is built, he installs it at your site and hardens it. He builds and runs on-premises inference infrastructure himself (virtualized NVIDIA GPUs, CUDA, llama.cpp) and publishes the tooling on GitHub.
Ownership and engineering capacity.
The company needs to keep the know-how as the system changes, and an investor’s diligence team will ask who owns it.
Most companies at this stage have no AI engineering team. Either your engineer or IT partner builds, or Christian leads the build with one or two contract engineers.
What Inoculis sets up.
- Deployment decision
- Cloud under a BAA, on-premises, or a mix, chosen after looking at the workload, the data, and who will operate the system. If hardware is already on site, the assessment says whether to configure it, repurpose it, or retire it.
- Hardware sized to the use case
- Christian tests candidate models on comparable hardware before any hardware is ordered, then sizes the appliance to the measured workload. Inoculis rents you the appliance for a monthly fee rather than selling it.
- Hardware setup and operation
- Inoculis installs the appliance at your site and adds it to your inventory, backups, and monitoring. Patching, model updates, and support are part of the monthly fee.
- BAA and feature map
- The map shows which vendor and route to use, which features the BAA covers, and what gets switched off. Inoculis reviews BAA and contract terms with your counsel.
- Security baseline for AI
- Identity and access, logging, retention, encryption, and patching. On-premises hardware goes into the asset inventory and the backup plan like any other system that holds regulated data.
- Development data
- Synthetic or properly de-identified data for development, so contractors never need production PHI.
- Sanctioned AI for staff
- An approved tool and an acceptable use policy that replace the free tools people already use.
- Go-live gate
- The checks every AI use case passes before it touches regulated data, including human review of what the AI produces.
Who builds it.
Coaching and fractional technical ownership both start with a paid discovery to agree on the first use case, the scope, and the delivery budget.
Coaching. Christian guides your leadership through the decisions and reviews the work. Your engineer or IT partner builds.
Fractional technical owner. Christian leads the build with one or two contract engineers. You control the accounts, data, code, and documentation. He plans the handover to your team when the work begins.
Boundary
What this is not.
Inoculis does not author FDA submissions for AI-enabled devices or own device software or device cybersecurity. Those stay with R&D, Quality, and Regulatory.
Inoculis does not host your data on its own servers. On-premises systems are installed at your site.
Inoculis does not certify the company or guarantee that a regulator, customer, or investor will accept the result.
Who this is for.
Medical device, SaMD, IVD, life science, and digital health companies with no in-house AI engineering team. It fits companies that want AI on regulated or proprietary data, whether they already bought hardware that is not delivering or are about to choose between cloud and on-premises.