We provide the required hardware and rent it as an operated system. The platform runs either at your site or in our data center. Your data sources are connected in a controlled way; sensitive data does not have to go to public AI services.
Rented hardware, clear operations
Typical environments
First we clarify data sources, workflows, and risks. Then we size the rented hardware, configure models and integrations, and operate the environment with monitoring, updates, and documentation.
One defined use case is tested with real data and clear success criteria.
Rented hardware, access, integrations, and runbooks are prepared for day-to-day use.
If the value is clear, we expand users, data sources, models, and capacity.
We choose, provide, and rent hardware based on model size, data volume, response times, and budget.
The platform runs on rented 42BIT hardware at your site or in our data center.
We start small and expand only when usage, quality, and effort make sense.
We document data flows, access, logs, and safeguards in a traceable way.
We do not build demos without an operating model. Each setup gets data sources, access rules, monitoring, and clear responsibilities.
We set up the rented AI environment: hardware, runtime, models, updates, monitoring, and backup.
We make internal content searchable without copying it into public AI services.
AI is connected where work happens: tickets, documents, approvals, internal tools, or business systems.
We define who may use what, which data is processed, and how usage remains traceable.
We handle availability, updates, troubleshooting, performance, and extensions.
We show users and admins what the setup is for, where its limits are, and how to use it cleanly.
Local AI is most useful where internal data, permissions, and traceable sources matter.
Search policies, manuals, tickets, and documentation. Answers point back to internal sources.
Summarize, compare, classify, and extract information from internal documents without exposing them externally.
Summarize tickets, find matching runbooks, and draft replies for the helpdesk.
Turn repetitive analysis and triage tasks into guided workflows with review and approval steps.
Define the use case, data sources, risks, and success criteria together.
Define rented hardware, location, architecture, permissions, model choice, and operations.
Set up the platform, connect data sources, and test with real users.
Monitor, improve, update, and support the service as part of your IT landscape.
Together we clarify which data, systems, rented hardware, and operations tasks are needed for your first local AI use case.
Discuss an AI PilotYour data stays local. The platform runs either at your site or in the 42BIT data center, so sensitive data never has to go to public AI services. We design for GDPR compliance and keep a traceable record of data flows, access, logs, and the safeguards behind them, with TOMs, processing agreements, and the relevant roles and permissions all documented. You get that evidence without us making unsupported certification promises.
42BIT provides, rents, and operates the AI hardware as a managed system, so buying it yourself is off the table. We size and choose the GPU/CPU, storage, network, and backup around your model size, data volume, response times, and budget, then coordinate the rental, installation, and lifecycle. The result is an operated rental rather than a hardware purchase on your side.
Both setups work. The platform runs on rented 42BIT hardware either at your site or in the 42BIT data center, whichever fits your situation. For the location you pick, we handle installation, handover, and runbooks, and in neither case does sensitive data go to public AI services. Which one suits your data sources, workflows, and risks is something we sort out together during planning.
We pick the model to match the task and the hardware on hand, so you are not locked into one fixed model. That choice gets made during planning, right alongside how we size the rented hardware and settle the location, architecture, and permissions, and it can shift as we expand. Because the platform runs locally, the models work entirely on your rented environment instead of a public AI service.
A pilot begins with us clarifying the use case, data sources, risks, and success criteria together. From there we test one defined use case with real data against clear success criteria, and we only expand the users, data sources, models, and capacity once the value is obvious. Reach out to talk through a concrete AI pilot, and we will map out which data, systems, rented hardware, and operations tasks it actually needs.
Existing permissions stay the basis for every answer, so the system only ever uses content a user is already allowed to see. We connect your data sources for RAG with access tied to those existing permissions, and each answer points back to a traceable internal source. For critical workflows, human approvals stay in the loop rather than being automated away.