How We Build Private RAG Search on Google Drive & Workspace
How We Connect AI Systems to Google Drive
Our backend pipeline uses a dedicated Google Cloud Service Account authenticated with Google Drive API v3. The service subscribes to Drive Changes API (push notifications via Google Cloud Pub/Sub). When files are modified, our worker downloads the export stream, extracts clean text, and updates the private vector database.
API Boundaries, Authentication & Latency SLAs
Supported Google Drive Events & Webhooks
How We Protect Your Google Drive Data
- Service account keys stored securely in cloud KMS with automated rotation.
- Read-only drive.readonly scopes prevent accidental document alteration.
- Folder-level exclusion filters ensure HR, payroll, and legal folders remain strictly quarantined.
How We Deploy This Integration in 5 Steps
Key Integration Questions Answered
Can the system read scanned PDFs stored in Google Drive?
Yes. For scanned non-searchable PDFs, our pipeline incorporates an in-VPC OCR step (Tesseract / AWS Textract) to extract clean text before vectorization.
Does Google have access to our search queries?
No. Google Drive is used purely as the file storage source. All vector embedding generation, query retrieval, and model reasoning take place in your private VPC.
RAG & Private Knowledge Search
Learn how we build end-to-end production architectures powered by this integration.
About Aurevion AI
Custom AI systems, private RAG architectures, and sovereign software development for growing engineering teams.
Aurevion AI engineers dependable production artificial intelligence systems, sovereign retrieval-augmented generation pipelines, autonomous agentic workflows, and modern high-concurrency software applications. We partner directly with engineering leaders across North America and Europe, deploying zero-trust private cloud architectures with complete intellectual property ownership, deterministic reliability guarantees, and transparent sprint-based technical team collaboration.