ON-PREMISES AI KNOWLEDGE BASE
How to Build an On-Premises Enterprise AI Knowledge Base
A practical guide to private RAG for manufacturing: architecture, enterprise data sources, ACL and SSO, model and infrastructure choices, implementation stages, acceptance tests and cost drivers.
Answer first
An on-premises enterprise AI knowledge base runs models, indexes, vector storage, document processing and access control in an environment governed by the organization. It uses RAG to retrieve authorized information from ERP, PDM, MES, PLM, M365, databases and documents, then returns answers with sources. Start with data boundaries, identity, ACLs, representative questions, refusal rules and measurable acceptance criteria—not with the largest model available.
What is an on-premises enterprise AI knowledge base?
It is more than a chat model installed on a company server. A complete system includes connectors, parsing, indexing, identity, source permissions, retrieval, citations, audit, backup and operations. The system identifies the user, filters available enterprise sources, retrieves evidence and only then asks the model to compose an answer.
Without synchronization, version management and access design, answers become stale and may expose information the user could not access in the source system.
When on-premises or hybrid deployment fits
- Drawings, BOMs, formulas, quotations, customer records or SOPs must remain under company control.
- ERP, PDM, MES, PLM and file servers are primarily available on the internal network.
- The project must integrate AD/SSO, groups, roles and source ACLs with auditable access.
- Regulation, customer contracts or security policy require traceable data location and processing boundaries.
- Core data stays on premises while selected cloud models may be used for approved tasks in a hybrid design.
Six layers of the architecture
- Sources: ERP, PDM, MES, PLM, M365, databases, file systems, drawings and documents.
- Synchronization and parsing: preserve document, version, page, part and other metadata.
- Indexing and retrieval: combine keywords, vectors and field filters for technical terms and part numbers.
- Identity and access: filter candidates against AD/SSO and source ACLs before generation.
- Models and applications: select models for language, context, hardware, latency and task requirements.
- Governance and operations: manage citations, refusal, logs, backup, monitoring, versions and permission changes.
Information to prepare
- Systems and documents, formats, versions, volumes, update frequency, APIs and database access.
- Users, groups, roles, ACLs, deprovisioning and cross-team sharing rules.
- Thirty to one hundred representative questions with expected answers, acceptable sources and prohibited areas.
- Known issues such as duplicates, obsolete versions, scans, inconsistent names and missing metadata.
- Infrastructure, network zones, GPU options, backup, security review and operating ownership.
A five-stage rollout
- Scope one team, one valuable use case and a clear data boundary.
- Connect sources and establish parsing, mappings, versions and index updates.
- Test allow, deny, group changes, disabled accounts and sensitive-data boundaries.
- Run a proof of concept against real questions, citations, refusal, latency and stability.
- Complete monitoring, backup, administrator training, incident procedures and expansion rules.
Acceptance requires evidence, not fluent answers
A fluent answer is not necessarily correct. Test a pre-agreed question set across representative roles and record retrieval, answer, citation, access, latency and failure type.
- Retrieve the correct record, document, revision or transaction.
- Return citations to a source, page, version, part or system record.
- Prevent unauthorized users from inferring restricted content through answers or suggestions.
- Reflect source updates, obsolescence and permission changes within the agreed interval.
- Refuse safely when evidence is missing, conflicting, obsolete or insufficient.
- Give administrators visibility into synchronization, errors, capacity, usage, models and indexes.
What drives cost and schedule?
Cost depends on the number and type of sources, integration method, document formats, access complexity, model and GPU choices, data volume, update frequency, security controls, test coverage and operating scope. A scoped proof of concept produces better estimates than sizing the entire environment from user count alone.
- A single team and source reduce initial integration and acceptance risk.
- Documented APIs, fields and access structures make delivery easier to estimate.
- Scans, drawings, complex tables and multiple versions require extra processing and tests.
- On-premises models must be sized for GPU memory, concurrency, context, latency and availability.
Frequently asked questions
Can an on-premises AI knowledge base operate without internet access?
Yes. It can be fully isolated, connected through controlled channels, or deployed as a hybrid. Model updates, licensing, operations and security policy determine the design.
Is on-premises automatically safer?
No. It improves control but still requires authentication, least privilege, patching, logs, backup, key management and administrator governance.
Do we need a large GPU server?
Not always. Size infrastructure for model choice, concurrency, latency, processing volume and availability. A proof of concept can provide measurements before hardware is finalized.
Can we begin with one PDM system or document set?
Yes. A narrow, valuable source is easier to validate for synchronization, access, citations and answer quality.
Can private RAG reveal unauthorized documents?
It can if every document is placed in one unfiltered index. Enterprise designs should enforce identity and source ACLs before retrieval and test permission changes and disabled accounts.
How quickly can a project show results?
It depends on data and access complexity. Start with one team, one source and representative questions, then confirm the production schedule from actual integration and test results.