Identity and least privilege
Integrate users, roles and service identities. Restrict documents, tools and actions by business need.
Private industrial AI
Keep sensitive operations data inside the boundary you control.
NeoBram designs industrial AI for fully offline, on-premises, private-cloud and edge environments. The right option depends on your data, connectivity, latency, hardware, licensing and support constraints.
- 01Architecture selected from operational constraints
- 02Human approval and auditability by design
- 03Clear separation of client ownership and third-party licences
AI engineering partner
Your experts define process truth. NeoBram engineers the AI. The production team validates the outcome.
01 / Domain02 / Evidence03 / AI
Quick answer
Private AI is not one deployment pattern. It is a set of design decisions covering model location, data flow, network access, identity, logging, updates, licences and operational support. “Offline” only solves the connectivity boundary; the complete system still needs security and maintenance.
Deployment comparison
Choose the boundary before choosing the model.
The matrix below is an architectural starting point. Final suitability depends on the specific system, model licences, data classification and operating environment.
| Deployment mode | Best suited to | Data boundary | Design considerations |
|---|---|---|---|
Fully offline / air-gapped | Restricted plants, sensitive engineering knowledge and disconnected sites | Model, application and data remain within the isolated environment | Local model availability, hardware sizing, patch process, licence rights and offline evaluation |
On-premises private network | Plants with internal infrastructure and controlled enterprise connectivity | Runs in the customer's data centre or plant network | Identity integration, segmentation, backup, observability and support access |
Private cloud / VPC | Distributed teams needing managed infrastructure inside their own cloud tenancy | Runs in customer-controlled accounts, regions, networks and storage | Provider services, egress rules, region, encryption, model endpoint and operating cost |
Edge and hybrid | Vision, low-latency equipment workflows and intermittently connected sites | Inference occurs locally; only governed events or summaries synchronize if permitted | Edge lifecycle, buffering, fleet updates, failover and central monitoring |
Production safeguards
Private is necessary. Trustworthy operation takes more.
We use risk-management practices as design inputs, including NIST’s voluntary Govern, Map, Measure and Manage functions.
Review the NIST AI RMF PlaybookSource reviewed 10 July 2026. Framework use is voluntary; NeoBram is not affiliated with NIST.
Data lineage and boundaries
Know which data enters training, retrieval and inference, where it is stored, and how it is deleted.
Evaluation before autonomy
Test accuracy, limitations, edge cases and harmful failure modes using representative data and expert review.
Human approval and escalation
Define which outputs advise, which can trigger action, and where a named person must approve.
Monitoring and maintenance
Track quality, drift, latency, usage, cost and security events; plan model and knowledge updates.
Logs and operating evidence
Keep the evidence needed to investigate outputs, reproduce decisions and support internal governance.
Client-controlled deliverables
What the client should control
- Client data and access policies
- Client-specific application code and configuration delivered under contract
- Prompts, retrieval configuration and evaluation assets created for the engagement
- Deployment accounts, networks, logs and operational decisions
- Dependency, model and licence inventory with permitted-use notes
- Operating runbook, backup and rollback steps, plus an executable transition or exit plan
Licence clarity
What “ownership” does not automatically include
- Ownership of a third-party foundation model
- Rights beyond an open-source or commercial model licence
- Transfer of third-party software, data or connector intellectual property
- Freedom from future hardware, maintenance or security obligations
Start with the boundary
Tell us what cannot leave your environment.
We will help determine which model, architecture and operating approach can meet that constraint responsibly.