Yes — it’s absolutely possible, and it’s one of the most common ways enterprises adopt RAG (Retrieval-Augmented Generation) quickly without migrating everything into a new system.

For many organizations, Google Drive, SharePoint, and OneDrive already are the company’s knowledge base: policies, SOPs, contracts, technical docs, templates, reports, meeting notes, and more. The real challenge is that documents are massive, scattered, and hard to search, and teams often struggle to find the right version at the right time. RAG solves this by:

1. Connecting to your existing repositories
2. Extracting, processing, and indexing content + metadata
3. Retrieving the most relevant sources at question time, then generating an answer with citations

NKKTech Global is an AI company focused on enterprise GenAI/RAG deployments that work in real operations: permission-aware access, audit logging, citation-based answers, and a clear path from PoC to production.

1) What “Real Integration” Actually Means

Some people assume RAG “reads files live” every time someone asks a question. In practice, reliable enterprise RAG works through synchronization + indexing:

• Scheduled sync: scan Drive/SharePoint/OneDrive on a cadence (e.g., every 15 minutes, hourly, daily)
• Event-driven sync (webhooks): update the index whenever files are created/updated/deleted
• Hybrid approach: webhooks for speed + scheduled scans as a safety net

The key point: RAG doesn’t replace Drive/SharePoint/OneDrive. It adds a smart knowledge layer (semantic search + citations) on top of what you already use.

2) A Practical Architecture for Drive/SharePoint/OneDrive RAG

A minimal but scalable architecture typically includes six components:

(1) Connectors

• Google Drive API
• Microsoft Graph API (for SharePoint + OneDrive)

Connectors authenticate via OAuth or service-to-service methods (depending on your enterprise setup) and read only what’s permitted.

(2) Ingestion Pipeline

• Collect metadata: folder path, owner, created/modified timestamps, file types, permissions, site/library (SharePoint), etc.
• Extract content: docs/pdfs/pptx/xlsx…
• Apply OCR for scanned PDFs (when needed)

(3) Chunking & Enrichment

• Split content based on structure: headings, sections, clauses, tables, lists
• Attach metadata to each chunk (department, project, site, source URL, version/date, tags…)

(4) Indexing (Vector + Hybrid)

• Vector index for semantic retrieval
• Hybrid search (keyword + vector) to capture IDs, template codes, clause numbers, and exact phrases

(5) RAG Runtime (Retrieval + Answering)

• Retrieve top-k relevant chunks
• Optional reranking for higher precision
• Generate an answer with citations (document name + excerpt location + link)

(6) Governance (Security + Operations)

• RBAC/ABAC by department, project, group
• Audit logs: who asked what and which sources were retrieved
• Optional masking/DLP for sensitive content
• “Don’t hallucinate” rules: if evidence isn’t found, say so

3) Can RAG Respect Drive/SharePoint/OneDrive Permissions?

Yes — and it should.

Two common approaches:

Approach A: Permission-Aware Indexing (Recommended)

• Store permission context (group/user/site) at indexing time
• At query time, filter retrieval results before sending them to the LLM
  Pros: fast, scalable, enterprise-friendly.

Approach B: Query-Time Access Checks

• Retrieve candidate sources, then call APIs to verify access in real time
  Pros: strict access validation; Cons: more API calls, higher latency.

In real deployments, NKKTech Global typically uses Approach A plus periodic permission refresh to balance security, performance, and cost.

4) Common Integration Challenges (and How to Handle Them)

1) Excel / PowerPoint content with heavy tables

• Requires table-aware parsing
• Index by row/section rather than one huge chunk

2) Version sprawl and inconsistent naming

• Use metadata signals (modified date, standardized folders, naming rules)
• Add versioning rules per folder or document category

3) Multi-language corpora (VN/EN/JP)

• Choose embedding models that perform well cross-lingually
• Hybrid retrieval often improves results for mixed terminology and codes

4) Freshness and update speed

• Combine webhook sync + scheduled backfill scans
• Incremental indexing (update only changed files)

5) Sensitive data governance

• Mask or restrict access to sensitive fields (pricing, personal data, contracts)
• Maintain audit trails and strict “answer-only-from-approved-sources” policies

5) Where Should You Start?

If you have all three repositories (Drive + SharePoint + OneDrive), the most practical rollout is:

1. Start with one repository + one or two departments for the PoC
   (e.g., HR policies, Sales proposals, PMO processes)
2. Prioritize high-frequency, high-risk content: SOPs, policy docs, templates, contracts
3. Once KPIs look good, expand to the second and third repositories

6) KPIs to Prove the Integration Works

• Time-to-find information (before vs after)
• “Correct answer with citations” rate
• “Not found in current sources” rate (to identify corpus gaps)
• Internal user satisfaction (CSAT)

7) How NKKTech Global Supports This

As an AI company, NKKTech Global typically delivers:

• Connectors for Google Drive and Microsoft 365 (SharePoint/OneDrive)
• A robust ingestion pipeline with structure-aware chunking
• Hybrid retrieval + citation-first answering
• Permission-aware access control + audit logging
• A clear PoC → production roadmap without disrupting your existing storage

If you want a fast start, most organizations can begin with a quick-win use case (HR policy / SOP / proposal library) in 2–3 weeks, then expand systematically.

Contact Information:
🌐 Website: https://nkk.com.vn
📧 Email: contact@nkk.com.vn
💼 LinkedIn: https://www.linkedin.com/company/nkktech