Grounded in real sources
Answers are retrieved from approved documents, policies, manuals, records, and knowledge bases rather than generated from memory alone.
Enterprise RAG and AI knowledge search
Make enterprise knowledge searchable, answerable, and governed.
RAG lets AI answer questions from your organization's own documents - policies, contracts, manuals, procedures - not generic training data.
What is RAG?
A practical way to ground AI answers in enterprise sources.
No retraining required
New and updated documents can be indexed without retraining the foundation model every time business content changes.
Governed access
Document-level ACLs help ensure users only retrieve answers from sources they are allowed to access.
Use cases
Where enterprise RAG creates operational value.
Finance
Policy and compliance Q&A with citations to approved source documents.
Legal
Contract search, clause extraction, obligation review, and precedent discovery.
HR
Employee knowledge base answers grounded in current policies and procedures.
Operations
Technical manual search for field, maintenance, service, and support teams.
Customer Service
Verified response generation based on approved product, policy, and process content.
Government
Regulatory document discovery across large document sets with controlled access.
Pipeline
The seven stages of a production RAG system.
1. Document ingestion
Connect approved document repositories, file shares, records systems, and knowledge bases.
2. Metadata tagging
Capture owner, department, sensitivity, language, date, version, and access control metadata.
3. Chunking
Split content into retrieval units that preserve context, headings, tables, and multilingual structure.
4. Embedding
Represent text as vectors using models selected for domain, language, and evaluation requirements.
5. Indexing
Store content in search infrastructure with metadata, filters, access rules, and update paths.
6. Retrieval
Use hybrid BM25 plus dense vector retrieval to balance keyword precision with semantic matching.
7. Generation
Generate answers with faithfulness constraints, citations, refusal behavior, and audit logging.
Data readiness
Good answers depend on prepared source material.
Before
- Unstructured documents across disconnected repositories.
- No consistent metadata for ownership, sensitivity, or lifecycle.
- No ACL mapping between document systems and AI retrieval.
- Mixed versions of the same policy, contract, or procedure.
After
- Audited document estate with source ownership clarified.
- Standardized ingestion, chunking, indexing, and refresh pipeline.
- ACLs enforced at retrieval before generation occurs.
- Versioning tagged so answers can cite current approved sources.
Checklist
RAG readiness checklist.
Priority use cases and target users are defined.
Authoritative source repositories are identified.
Document ownership is clear.
Sensitivity and classification rules exist.
Access controls can be mapped to retrieval.
Version control and expiry rules are known.
Arabic and English content requirements are documented.
Evaluation questions and expected answers are available.
Grounding, citation, and refusal behavior are defined.
Hosting and data residency requirements are clear.
Monitoring and audit logging requirements are agreed.
Maintenance ownership is assigned.
Arabic and multilingual RAG
Arabic search quality needs deliberate design.
Arabic embedding models
Select and test embedding models against Arabic, English, and mixed-language enterprise content.
RTL chunking challenges
Handle right-to-left text, tables, headings, OCR artifacts, and mixed scripts during parsing and chunking.
Arabic evaluation sets
Create test questions and expected evidence in Arabic so retrieval quality is measured, not assumed.
Approach
How in-box.ai builds enterprise RAG.
Discovery
Define users, questions, documents, risk boundaries, success criteria, and data sovereignty requirements.
Architecture
Design ingestion, retrieval, model, access control, hosting, logging, and evaluation components.
Build & Evaluate
Implement the pipeline, test retrieval and answer faithfulness, and tune against real enterprise questions.
Govern & Maintain
Set ownership for content refresh, quality review, access changes, monitoring, and model updates.
FAQ
Common RAG questions.
Does RAG eliminate hallucinations?
No. RAG reduces uncertainty by grounding answers in retrieved sources, but evaluation, citations, refusal rules, and monitoring are still required.
Do we need to retrain a model?
Usually not for the first production use case. Most enterprise knowledge search systems begin with retrieval, prompting, access control, and evaluation.
Can RAG enforce document permissions?
Yes, if ACLs are captured and enforced during retrieval. This has to be designed into the pipeline, not added only at the chat interface.
How long does a RAG project take?
A focused pilot can often be built in weeks. Production timelines depend on document quality, ACL complexity, integrations, evaluation scope, and governance needs.
Move from document search to governed AI answers.
We will assess your document estate, access model, Arabic requirements, and production readiness.