Core idea: enterprise AI should not bypass the systems that already govern corporate information. This lab demonstrates an architecture where repository permissions, retention rules, metadata and provenance remain authoritative while AI is added as a controlled retrieval and reasoning layer.
Repository Objects2.4Mdocuments + records
Governed Sources12ECM / file / business systems
Permission Filter100%pre-retrieval enforcement
Answer Citations100%source-backed demo
AI Exposure0unauthorized objects
Governed AI Retrieval Pipeline repository-aware RAG
Enterprise Repositorydocuments, records, ACLs, retention, metadata
Secure Retrievalidentity + group + object permission filtering
ParsingPDF, Office, image OCR, email, metadata
Embeddingsapproved text chunks mapped to vector space
Semantic Searchmeaning-based retrieval with source constraints
RAGmodel receives only authorized retrieved evidence
AI Responsegrounded synthesis with confidence boundaries
Citations / Provenancedocument, page, chunk, source, policy trail
Business Problem
Organizations may have decades of governed documents spread across ECM platforms, file shares, archives, SharePoint, email and line-of-business systems. AI creates value only if access control, records obligations and source truth survive the transition.
Architecture Principle
The AI layer is additive. It does not replace repository authority. Retrieval is permission-aware before content reaches the language model, and answers preserve evidence links back to source records.
Consulting Outcome
A client receives an inventory of repositories, access models, retention obligations, AI-ready content flows, RAG architecture, security controls, proof-of-concept implementation plan and modernization roadmap.
Repository model: the source system remains the system of record. AI indexes are derivative acceleration layers, never the final authority for security, retention or legal disposition.
Connected Enterprise Sources illustrative inventory
| Source | Content | Security model | Retention | AI readiness |
|---|---|---|---|---|
| Documentum / ECM | Contracts, case files, records | Repository ACL + groups | Policy-managed | Ready via connector |
| ApplicationXtender | Images, reports, fixed content | Application / document-level | Retention / hold | Ready via connector |
| SharePoint | Office docs, collaboration | M365 identity / site / file | Policy-dependent | Ready via API |
| File Shares | Legacy departmental content | NTFS / directory groups | Mixed | Needs classification |
| Email Archive | Messages + attachments | Mailbox / archive policy | Policy-managed | Ready with controls |
Repository Truth
Derivative AI Index
Document Intelligence Pipeline ingest → normalize → chunk → enrich
AcquireAPI, repository connector, file drop, migration batch
Detecttype, MIME, encryption, duplicate, language
ParsePDF / DOCX / XLSX / PPTX / email / HTML
OCRscanned image and image-only PDF extraction
Normalizeclean text while preserving page / section boundaries
Chunkcontext-aware segments with source coordinates
Embedvector representation for semantic retrieval
Sample Processing Queue
| Object | Type | Pages | Parse | Security sync |
|---|---|---|---|---|
| Vendor_MSA_2011.pdf | 48 | Complete | Verified | |
| Board_Minutes_2008.tif | Image | 17 | OCR complete | Verified |
| Policy_Archive_1999.doc | Legacy Office | 12 | Normalized | Verified |
Content Controls
Malware scanPII detectionClassificationDuplicate detectionLanguage detectionPage coordinatesVersion mappingRetention stateLegal hold state
Non-negotiable control: the language model never decides what a user is authorized to retrieve. Authorization is resolved against enterprise identity and source-system policy before candidate evidence is assembled.
Permission Evaluation
Governance Controls
Policy Decision Trace example
| Candidate document | Repository match | User permission | AI retrieval | Reason |
|---|---|---|---|---|
| Executive_Comp_2026.xlsx | 0.93 | DENY | BLOCKED | Restricted finance group |
| Vendor_MSA_2026.pdf | 0.91 | ALLOW | INCLUDED | Legal + Procurement access |
| Security_Incident_42.pdf | 0.88 | DENY | BLOCKED | Security response team only |
Semantic Search meaning-based retrieval, still permission-aware
ContractsPoliciesLegalRestricted HR excluded
| Score | Document | Matched meaning | Source | Access |
|---|---|---|---|---|
| 0.94 | Vendor_MSA_2026.pdf | renewal notice + termination window | ECM / Contracts | Authorized |
| 0.89 | Procurement_Policy_v9.pdf | renewal approval requirements | Policy Repository | Authorized |
| 0.84 | Legal_Playbook_Contracting.docx | auto-renew risk guidance | SharePoint | Authorized |
Grounded answer workflow: this static lab shows the expected enterprise RAG behavior and evidence flow. Production deployment would connect a real embedding model, vector store and LLM through the client-approved security architecture.
RAG Workspace retrieval + grounded synthesis
GROUNDED / HIGH CONFIDENCE
Before the agreement renews, confirm the notice deadline in the governing contract, verify whether business-owner and procurement approvals are required, and document any decision to renew, renegotiate or terminate. For the sample MSA, the renewal clause requires notice before the renewal date; the procurement policy requires owner review before renewal. The AI response does not rely on uncited model memory for those claims.[1] Vendor_MSA_2026.pdf · §12 Renewal · source object ECM-CTR-18422 · page 31 · repository ACL verified[2] Procurement_Policy_v9.pdf · Renewal Controls · source object POL-0091 · page 14 · policy repository ACL verified
Before the agreement renews, confirm the notice deadline in the governing contract, verify whether business-owner and procurement approvals are required, and document any decision to renew, renegotiate or terminate. For the sample MSA, the renewal clause requires notice before the renewal date; the procurement policy requires owner review before renewal. The AI response does not rely on uncited model memory for those claims.[1] Vendor_MSA_2026.pdf · §12 Renewal · source object ECM-CTR-18422 · page 31 · repository ACL verified[2] Procurement_Policy_v9.pdf · Renewal Controls · source object POL-0091 · page 14 · policy repository ACL verified
Retrieved Evidence
Guardrails
ACL filter before promptNo unsupported claimsCite every material assertionShow uncertaintySource links retainedQuery audit trail
Evidence & Provenance Ledger answer-to-source traceability
| Citation | Source object | Version | Page / chunk | Security proof | Integrity |
|---|---|---|---|---|---|
| [1] Renewal clause | ECM-CTR-18422 | 6.2 | p31 / chunk 204 | ACL verified | SHA-256 recorded |
| [2] Procurement rule | POL-0091 | 9.0 | p14 / chunk 88 | ACL verified | SHA-256 recorded |
| [3] Legal guidance | SP-LGL-8831 | current | §4.3 / chunk 61 | M365 verified | ETag recorded |
Why provenance matters
Every answer should be reviewable by a human. The user can inspect exactly which repository object, version, page and chunk supported the response, together with the access decision that allowed it to be used.
Evidence lifecycle
When a source object changes, the derivative chunk and embedding can be invalidated and regenerated. When a source is deleted or becomes inaccessible, its derivative evidence is removed from the retrieval layer.
Production architecture: the recommended design separates systems of record, ingestion/orchestration, semantic retrieval, model access and audit. This keeps AI replaceable without weakening enterprise content governance.
1. Systems of Record
Keep authoritative content and policy where the enterprise already governs it.
- Documentum / ECM
- ApplicationXtender
- SharePoint / M365
- File shares / archives
- Business systems
2. Content Intelligence
Extract usable evidence while retaining source identity and security context.
- Connector framework
- Parsing / OCR
- Metadata normalization
- Chunking
- PII / classification
3. Retrieval Layer
Support lexical + vector retrieval with policy-aware candidate filtering.
- Search index
- Vector database
- Hybrid ranking
- ACL filter
- Freshness sync
4. AI Experience
Use models as controlled consumers of retrieved evidence, not as repositories of truth.
- RAG orchestration
- Prompt controls
- Citations
- Confidence / refusal
- Audit + telemetry
Deployment Patterns
| Pattern | Best fit | Key control | Typical stack |
|---|---|---|---|
| On-prem / private cloud | Highly regulated content | Data locality | ECM + self-hosted vector + private model gateway |
| Hybrid | Most enterprises | Selective content exposure | On-prem connectors + cloud AI services |
| Cloud-native | Modern M365 / SaaS estates | Tenant / identity governance | Cloud search + vector + managed LLM |
Consulting proposition: answer the enterprise question, “How do we safely put AI on top of 20 years of governed corporate documents?” with architecture, controls, proof, migration planning and an executable roadmap.
Assessment & Strategy
Prototype & Delivery
Representative Deliverables enterprise engagement
Current-state architectureRepository/data mapSecurity modelGovernance matrixAI readiness scorecardRAG reference designPrototypeThreat modelEvaluation planMigration roadmapRunbookExecutive presentation
Consulting Support Samuels Enterprises, LLC
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