Private and offline industrial AI
Use AI where your industrial data is allowed to stay
Use internal information in an AI workflow that fits your organisation’s permitted boundary. NeoBram helps scope the application, data path and operating responsibilities for offline, on-premises or private-cloud requirements. Begin with one job, such as finding applicable equipment evidence, and test both answer quality and what can connect or leave.
Scope the first decision
A practical first step
- Start with
- One information task, its permitted sources and a clear rule on external connections, processing and support access.
- Possible scoped outputs
- A scoped review can produce a data-flow and dependency record, acceptance checks and an agreed plan for deployment and operation.
- Your team brings
- Your information owner and IT/security owner, a system outline, source structure, connectivity restrictions and representative permitted examples.
- Next decision
- Approve a design to test, resolve a missing dependency or stop. Production use follows agreed evidence and customer approval.
Opens the AI strategy-session booking page; choose a duration there. Bring your source outline, connectivity rules and IT/security constraints.
Choose the work and the boundary together
Fully offline means the intended workflow runs without external connections. On-premises describes location and may still involve approved network services. Private cloud and hybrid designs require their own connection and data-flow review.
Define the output a person needs and their authority. Finding a bulletin can support research; approving replacement suitability or an operating instruction remains a specialist decision.
Company Expert AI is the company-specific assistant product route. This service addresses the deployment and operating boundary for an agreed workflow; product fit and technical scope still need assessment.
Review the complete information path
Map source records, prompts, retrieved passages, outputs, logs, backups and support material. Include identity services, telemetry, licence checks, model endpoints and updates. Establish what may leave and which dependencies are required.
Processing location, retention/deletion, encryption and key management, supplier access and incident handling need explicit review for the selected design. These are scoping questions; a private label does not prove implemented controls.
Equipment research inside a proposed local boundary
Illustrative architecture using synthetic maintenance documents. The arrows describe a proposed flow, not implemented controls or proof of disconnected operation.
Proposed local operating boundary
Approved sources
Equipment register, current manuals and reviewed service records.
Source owner decides versions, permitted users and withdrawal rules.
Local application
Retrieval/index → selected model/runtime → answer with source references.
Model, hardware, identity and licences must be assessed for the boundary.
Authorised reviewer
Checks equipment identity, document revision and applicability before using the answer.
An answer cannot authorise an operating or purchasing decision.
Logs, stored outputs and backups
Scope these within the operating boundary alongside the index and model. Agree access, retention and the backup destination; no external destination is assumed.
- Separate approved import path
Reviewed source, model or software updates enter only through an agreed transfer and testing process. Establish package provenance and rollback.
- Separate approved export path
Diagnostic or support material leaves only if separately authorised. Define what may be shared and with whom; do not assume a permanent external connection.
Disconnected-operation check
Offline acceptance: required operation must work without external connections. Test startup, sign-in, retrieval and recovery with connections unavailable, including the selected local dependencies.
Before approval, record actual endpoints, processing/storage, access, encryption, retention/deletion and recovery. This sketch makes no security, compliance or deployment guarantee.
A reported offline research workflow
NeoBram’s company-reported spare-parts research account describes local search and source references. Public query-level timings and independent network-boundary verification are not available in that account.
Test normal work and failures
Test startup, sign-in, retrieval, model execution and recovery with external connections unavailable when offline operation is required. Include ambiguous equipment identity, withdrawn permissions, stale material and an unavailable source.
Agree representative document sizes, user load and failure behaviour before sizing hardware or setting response expectations. Record what must stop and which dependencies must be restored.
Agree the technical review and handover
Start with the intended question, source collections, system outline and connectivity rules. A description is enough for initial discussion; approved samples can follow through an agreed route.
Scoped outputs can include a connection inventory, permission matrix, evaluated workflow, installation record and recovery instructions. Agree the exact set, unresolved questions, timing, customer input and fee before paid work.
Measure the complete research task
Measure question-to-usable-evidence time, including asset matching, version checks and correction. Compare difficult questions as well as straightforward ones. A quick but unsupported answer has not completed the job.
Include source maintenance, hardware, licences, administration, updates and support in the operating cost. The aim is a usable research workflow inside a boundary your owners can verify.
More clarity
Questions and answers
Does offline AI need a large GPU server?
Not always. Hardware depends on the workload and selected model. We assess representative performance before recommending capacity or assuming existing equipment is sufficient.
Can an offline system be updated?
Yes, with an approved transfer and testing process. The plan should cover verified packages, document or model updates, backups and rollback inside the restricted environment.
Is on-premise AI always cheaper or safer?
Neither follows automatically from location. Compare the complete design, access controls and operating costs. Hardware, administration, licences and maintenance matter alongside external usage charges.
Can a private assistant respect different teams' document permissions?
That requirement must be designed and tested across retrieval, generated answers and stored outputs. Source permissions, document changes and user identity need to remain aligned; it is not established simply by hosting the model internally.
What happens when an offline model or document collection becomes outdated?
Assign an update owner and an approved import process. Check revisions against representative questions or tasks before release, retain the previous accepted version where appropriate, and tell users when information coverage has changed.
Start with one business problem