Local AI and private RAG for data your organisation controls
Local AI keeps inference and retrieval inside an agreed environment when document sensitivity, access control or connectivity matters. The boundary still includes telemetry, updates, support and operating cost.
Local AI starts with the knowledge boundary
Choose a private architecture by tracing the document, person, decision and system involved — not by assuming local is automatically safer or cheaper.
Local AI vs Cloud API
A local runtime can keep inference and retrieval within the agreed environment; a cloud API trades that boundary for a managed external dependency.
Good fit
Sensitive manuals, contracts, internal knowledge or unreliable connectivity where access, audit and document versions must be controlled.
RAG before fine-tuning
Retrieval can ground answers in versioned documents. Fine-tuning changes model behaviour and needs a different data, evaluation and rollback plan.
Human review is part of the flow
Answers need access checks, citations or evidence and a human decision for material workflows.
Hallucination and document-quality limits remain visible.
Local AI decision matrix — follow data and operations
Compare local, cloud and hybrid options across the full knowledge lifecycle. No placement removes the need for access, evaluation or support ownership.
| Constraint | Local implication | Alternative to compare | Decision question |
|---|---|---|---|
| Latency | Predictable response after local indexing | Cloud for elastic compute; Edge for source events | What corpus size, concurrency and response target are required? |
| Privacy | Documents and prompts can stay in the agreed boundary | Hybrid for named external connectors | Which payload, telemetry, update and support fields may leave? |
| Connectivity | Search can continue without an API round trip | Cloud for managed availability | How are package updates, identity and index recovery handled offline? |
| Cost | Hardware, electricity, backup and operations are explicit | Cloud token and storage usage | Who owns the TCO, upgrade and support window? |
| Operations | Index, model, access and document versions need a runbook | Managed cloud service may reduce platform work | Who audits access, evaluates answers and rolls back a change? |
Private RAG reference architecture
The flow below separates business content from health telemetry, package update and approved support. Each step needs an owner and evidence.
- 01
OCR and document intake
Ingest approved documents, preserve source, language, version and access metadata; OCR errors stay reviewable.
- 02
Chunking and embedding
Split and index content with a recorded model/runtime version, retention policy and rebuild path.
- 03
Access-aware retrieval
Filter by identity and document permission before returning passages; log the decision without exposing payload.
- 04
Answer and human review
Generate a grounded answer with citations or evidence, then route material decisions to a named reviewer.
- 05
Version, update and recovery
Test index/model updates, preserve the previous version and define backup, rollback and support boundaries.
Evidence cards — knowledge quality needs context
A local model does not make a document set complete or an answer correct. Evidence records the corpus, method, owner and review date.
Internal test
Evaluate retrieval and answer quality on a representative, permission-safe corpus with a named method and reviewer.
Status: proposed · corpus and review date required
Customer pilot
Run selected workflows with operators, document versions, access cases and a stop condition before broad rollout.
Status: tested · customer acceptance required
Supported operation
Support only the model, runtime, index, identity and document-update paths listed in the operating scope.
Status: supported · runbook and owner required
Sizing and TCO assumptions
A private deployment replaces some token spend with platform responsibilities. Record the assumptions before selecting CPU/GPU or a support model.
CPU/GPU and concurrency
Size for model, context length, index, concurrent users, latency target and headroom; test the actual corpus.
Documents and versioning
Name the source owner, ingestion cadence, retention, deletion and index rebuild responsibility.
Identity and audit
Provide an identity source, role mapping, access review, audit retention and incident path.
Operating cost
Budget hardware, electricity, licences, maintenance, backup, upgrades and support separately from API tokens.
RAG vs fine-tuning
Choose retrieval or fine-tuning by evidence, data rights, evaluation method, rollback and future replacement effort.
Local AI limitations to make visible
- OCR, chunking, metadata or document versions can be wrong and make a confident answer misleading
- Retrieval and generation may hallucinate; material decisions need citations, human review and an acceptance method
- A local boundary does not automatically include health telemetry, package updates, backups or remote support
- GPU memory, indexing time, concurrency, latency, electricity and cooling limit the experience
- Fine-tuning can increase data, licence, evaluation and rollback obligations rather than remove them
- Without a document owner, identity owner and operating runbook, private AI is not ready for production
Local AI questions buyers ask
Does private RAG mean no data leaves the organisation?
Only when the chosen deployment and policy say so. Payload, logs, telemetry, updates and support paths are mapped separately in the Trust boundary.
Should we use RAG or fine-tuning?
RAG is often a better first test for changing documents; fine-tuning may fit stable behaviour changes. The decision needs data rights and an evaluation/rollback plan.
Is local AI cheaper than an API?
Compare hardware, electricity, licences, backup, maintenance, upgrades and people with expected API usage. There is no universal saving.
Who can read the documents and answers?
The identity owner and access policy decide. Retrieval must enforce permissions and an audit trail before an answer is shown.
What does a Private AI assessment return?
A bounded view of data sensitivity, document readiness, GPU/TCO and operating boundary with assumptions and a test plan.