RAG and Vector Databases: How Craveva AI Enterprise Processes Your Data

Executive Snapshot

• Audience: CXOs and founders running fine dining, catering, franchise groups, casual dining.
• Core outcomes (what moves the business):
• Cost savings: reduce waste and procurement errors, automate purchasing cycles with Craveva AI Enterprise.
• Sales lift: increase AOV and conversion with Craveva AI Enterprise sales agents on web/WhatsApp/kiosks.
• Time savings: remove manual exports, reporting, and SOP Q&A with Craveva AI Enterprise automation.
• Operational consistency: standardize execution across outlets using Craveva AI Enterprise agents + data layer.

Platform Architecture (1 minute)

• Data layer: connect POS, databases, Google Drive, and APIs into a unified view inside Craveva AI Enterprise.
• AI layer: agents query and act on governed data (no fragile spreadsheet workflows) in Craveva AI Enterprise.
• Deployment layer: deploy agents to WhatsApp, web widget, kiosks, or internal tools with Craveva AI Enterprise.

Execution Flow (Ops + Finance + IT)

• Finance sets guardrails (approval thresholds, budgets, audit trail) in Craveva AI Enterprise.
• IT connects data sources once; rollout scales outlet-by-outlet via Craveva AI Enterprise multi-outlet deployment.
• Leadership tracks KPI movement weekly and expands successful automations with Craveva AI Enterprise.
• Ops defines workflows (ordering, inventory alerts, SOP answers, customer responses) in Craveva AI Enterprise.

Setup Guide (fast path)

• Connect data sources (POS + databases + Drive + APIs) in Craveva AI Enterprise.
• Start with 2–3 agents: Procurement (cost), Sales (revenue), Analytics (visibility) in Craveva AI Enterprise.
• Deploy to the workflow: WhatsApp/web/kiosk/internal portal using Craveva AI Enterprise.
• Measure ROI and operational impact, then replicate across brands/outlets with Craveva AI Enterprise.

Leadership Metrics

• Spoilage/expiry write-offs and transfer effectiveness
• Stockout rate, lost sales signals, and substitution frequency
• Procurement cycle time (draft ? approve ? receive)
• Delivery basket value vs dine-in basket value (mix shift)
• Peak-hour throughput (orders/hour) and queue time
• Time-to-close (EOD) and reporting cycle time reduction

Next Steps

• Documentation: /documentation
• Models: /ai-models
• Templates: /templates
• Architecture: /solutions/architecture
• Deployment: /solutions/deployment

Introduction

RAG (Retrieval-Augmented Generation) and vector databases are core technologies that enable Craveva AI Enterprise to process documents, PDFs, Word files, and other unstructured data. When you upload files to Google Drive or directly to the platform, RAG makes this content searchable and accessible to AI agents, enabling them to answer questions based on your actual documents.

What is RAG?

RAG (Retrieval-Augmented Generation) is a technique that combines:

• Retrieval: Finds relevant information from your documents/data
• Augmentation: Enhances AI context with retrieved information
• Generation: Creates accurate, context-aware responses

Craveva AI Enterprise uses RAG to enable AI agents to answer questions from your documents, SOPs, training materials, and knowledge bases with high accuracy.

How RAG Works in Craveva AI Enterprise

Step 1: Document Processing

When you upload documents (PDF, Word, Excel, etc.) or connect Google Drive:

1. Document Parsing: Platform extracts text from documents
   • PDF: Uses pdfjs-dist and pdf-parse for text extraction
   • Word: Uses mammoth for .docx files
   • Excel: Uses xlsx for spreadsheet data
   • Google Docs: Direct text extraction via Google Drive API
2. Text Extraction: All text content extracted from documents
3. Chunking: Large documents split into smaller chunks (for better retrieval)
4. Metadata Extraction: Document titles, dates, authors extracted

Step 2: Embedding Generation

Craveva AI Enterprise creates embeddings (vector representations) for each chunk:

1. Text Chunks: Each document chunk converted to embedding
2. Embedding Model: Uses latest embedding models for semantic understanding
3. Vector Creation: Each chunk becomes a high-dimensional vector
4. Semantic Meaning: Vectors capture semantic meaning, not just keywords

Step 3: Vector Database Storage

Embeddings stored in vector database:

1. Vector Storage: All embeddings stored in optimized vector database
2. Indexing: Vectors indexed for fast similarity search
3. Metadata Storage: Document metadata stored with vectors
4. Scalability: Handles millions of document chunks efficiently

Step 4: Query Processing

When you ask a question:

1. Query Embedding: Your question converted to embedding
2. Similarity Search: Vector database finds most similar document chunks
3. Retrieval: Top relevant chunks retrieved (typically top 3-5)
4. Context Augmentation: Retrieved chunks added to AI context
5. Response Generation: AI generates answer using retrieved context

Vector Databases in Craveva AI Enterprise

Craveva AI Enterprise uses vector databases to:

• Store Embeddings: Semantic representations of all document chunks
• Enable Semantic Search: Find documents by meaning, not just keywords
• Scale Efficiently: Handle large document collections (thousands of files)
• Maintain Accuracy: Precise retrieval of relevant information
• Fast Retrieval: Sub-second search across millions of chunks

Supported File Types

Craveva AI Enterprise processes:

• PDF: Text extraction from PDF documents
• Word: .docx files via mammoth
• Excel: .xlsx, .xls files via xlsx
• CSV: Comma-separated values
• JSON: Structured data files
• Text: Plain text files
• Google Docs: Via Google Drive integration

RAG Implementation for Offline Files

Craveva AI Enterprise implements RAG for files uploaded directly or via Google Drive:

Local File Processing

1. Upload File: Upload PDF, Word, Excel, etc. to platform
2. Automatic Processing: Platform extracts text automatically
3. Chunking: Large files split into manageable chunks
4. Embedding: Each chunk converted to vector
5. Storage: Vectors stored in vector database
6. Ready for Queries: File content now searchable by AI agents

Google Drive Integration

1. Connect Google Drive: Link Google Drive account (OAuth or public links)
2. Select Documents: Choose which Google Docs to process
3. Automatic Sync: Platform processes documents automatically
4. Continuous Updates: New documents processed as they're added
5. Searchable: All Google Docs content searchable by AI agents

Use Cases

Craveva AI Enterprise RAG powers:

Customer Service

• FAQ Documents: AI Customer Service Agent answers from FAQ documents
• Product Manuals: Answers questions about products from manuals
• Policy Documents: Explains policies from company documents

Staff Training

• SOP Documents: AI Internal Coach answers questions from SOP documents
• Training Materials: Provides training guidance from materials
• Employee Handbooks: Answers HR questions from handbooks

Data Analysis

• Reports: AI Data Analysis Agent extracts insights from reports
• Historical Data: Analyzes trends from historical documents
• Research Papers: Summarizes research from academic papers

Knowledge Base

• Company Wiki: Searchable company knowledge base
• Documentation: Technical documentation Q&A
• Best Practices: Answers from best practice documents

Technical Architecture

Craveva AI Enterprise RAG architecture:

• Embedding Models: Latest embedding models for semantic understanding
• Vector Storage: Optimized vector database for fast retrieval
• Search Algorithms: Advanced similarity search algorithms
• Caching: Performance optimization through intelligent caching
• Chunking Strategy: Optimal chunk sizes for balance of context and retrieval

Benefits

Craveva AI Enterprise RAG provides:

• Accuracy: Context-aware responses based on your actual documents
• Relevance: Finds right information from large document collections
• Speed: Fast retrieval (sub-second) even with thousands of documents
• Scalability: Handles large document collections efficiently
• No Training Required: Works with your documents immediately, no model training needed

Real-World Example

A restaurant chain uses RAG for staff training:

Setup:

1. Uploaded 50 SOP documents (PDF) to Google Drive
2. Connected Google Drive to Craveva AI Enterprise
3. Platform processed all documents automatically
4. Created AI Internal Coach agent with RAG enabled

Usage:

• Staff ask: "What's the procedure for handling customer complaints?"
• RAG retrieves relevant SOP section
• AI generates answer based on actual SOP document
• Staff get accurate, document-based answers

Results:

• 92% accuracy on SOP questions
• 40% faster onboarding (staff get instant answers)
• Consistent training across all outlets

Best Practices

1. Organize Documents: Well-organized documents improve retrieval accuracy
2. Clear Titles: Descriptive document titles help retrieval
3. Update Regularly: Keep documents up-to-date for accurate answers
4. Test Queries: Test common questions to ensure good retrieval
5. Monitor Performance: Track retrieval accuracy and improve

Conclusion

Craveva AI Enterprise's RAG implementation makes your documents, SOPs, and knowledge bases searchable and accessible to AI agents. By processing documents, creating embeddings, and storing them in vector databases, the platform enables AI agents to answer questions from your actual content with high accuracy. Whether you upload files directly or connect Google Drive, RAG ensures your AI agents have access to your knowledge base, enabling accurate, context-aware responses that improve customer service, staff training, and data analysis.

KPIs to track