Build private knowledge assistants for procedures, equipment, quality and project records with permissions, citations, evaluation and human escalation.
Capability first. Model and vendor selection follow the data boundary, evaluation and operating responsibility.
Direct answer
An industrial RAG system retrieves approved evidence from the user's permitted corpus and uses that evidence to support an answer or draft. It should preserve document identity, revision, status, source links and permissions; show uncertainty; and abstain or escalate when evidence is insufficient. RAG improves reviewability but cannot guarantee correctness, so retrieval and answer support must be evaluated separately.
Industrial decision library
Capability architecture
Model, product and platform names may change. NeoBram selects capabilities from the intended use, representative evaluation, latency, privacy, licences, operating cost and customer support model.
Parse content while preserving source, revision, status, ownership and permissions.
Selection question: Can the pipeline prove which record version produced each retrieved passage?
Find candidate evidence within the user's permitted corpus and operating context.
Selection question: Does retrieval work by question class, document type, language and permission boundary?
Order, de-duplicate and assemble the evidence that the answer may use.
Selection question: Does ranking improve source precision without hiding contradictory evidence?
Synthesize only within the stated intended use and available evidence.
Selection question: Can the model cite, abstain, follow output structure and operate inside the required boundary?
Track retrieval, answer support, permissions, latency, use, feedback and changes.
Selection question: Who owns test cases, thresholds, content updates, incidents and model replacement?
Any vendor or open-source name elsewhere on the site describes an integration context. It does not imply partnership, certification or guaranteed compatibility.
Production acceptance
Limitations
Governance sources
Sources do not certify a NeoBram implementation. They help teams ask better governance, risk and architecture questions.
U.S. National Institute of Standards and Technology
Primary framework for governing, mapping, measuring and managing AI risk across the lifecycle.
U.S. National Institute of Standards and Technology
Primary guidance on OT architectures, threats, vulnerabilities and security controls for industrial environments.
European Commission
Primary source for the human-centric, sustainable and resilient industrial framing.
Direct answers
No. RAG can ground output in retrieved evidence and make review easier, but retrieval can be wrong and generation can still overstate or omit. Test source retrieval, answer support, abstention and escalation.
No. The system can be designed for offline, on-premises, edge or private-cloud operation when the selected models and licences support it. The customer approves the data and support boundary.
Start with the smallest controlled corpus that can answer a bounded user question. More documents can reduce quality when status, ownership, permissions and evaluation are weak.
No. NeoBram may describe a source system as an integration context. A production interface is confirmed with the customer and system owner; no partnership or certification is implied without explicit evidence.
Use one real workflow
NeoBram can lead the AI engineering while your experts retain domain, quality, safety and operating authority.
