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Multi-Tenant Security and Data Isolation: How Craveva AI Enterprise Protects Enterprise Data

For F&B groups and franchise networks, “enterprise security” is practical: each brand and outlet needs automation and analytics without leaking sales, supplier pricing, or customer data across companies. **Craveva AI Enterprise** enforces multi-tenant isolation (company + outlet) while still allowing centralized intelligence inside each tenant—so agents can operate safely at scale.

Craveva AI Enterprise Team · Mar 8, 2025 · 8 min read
Supported today (auto-updated)
Deployments
  • Web widget (JavaScript embed)
  • WhatsApp Business
  • E-commerce: Shopify, WordPress, WooCommerce, Magento, BigCommerce
Data sources & integrations
  • Offline files + Google Drive
  • Databases: PostgreSQL, MySQL, MongoDB, BigQuery, Snowflake, Redshift, Athena, ClickHouse, Trino, SQL Server, Oracle, DuckDB
  • Online APIs: REST, GraphQL, Webhook
  • POS (Singapore): Qashier, Eats365 (others appear in roadmap/partials)
Note: Some connectors may exist as base classes/framework but are not yet available as production deployments.

Boardroom Summary

  • Audience: CXOs and founders running fine dining, catering, franchise groups, casual dining.
  • Core outcomes (what moves the business):
  • 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.
  • 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.

How the platform works

  • 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.

Business Flow (what changes week 1–4)

  • 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.

Go-live Checklist

  • 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.

What to Measure

  • Recipe compliance variance and portion drift
  • Menu availability accuracy across POS + delivery channels
  • Reorder recommendation accuracy vs actual consumption
  • Repeat rate and retention cohort movement
  • Peak-hour throughput (orders/hour) and queue time
  • Manager task completion rate (SOP + audit checks)

Platform References

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

Multi-Tenant Security for F&B: Isolation by Company and Outlet (Craveva AI Enterprise)

For multi-brand groups and franchise networks, security is not theoretical. It’s the difference between:

  • one franchisee seeing another franchisee’s sales,
  • supplier price lists leaking across brands,
  • outlet-level users accessing company-wide financial dashboards,
  • AI agents pulling the wrong tenant’s data.

Craveva AI Enterprise is designed for these realities. It enforces strict tenant boundaries (company + outlet) while still enabling centralized intelligence inside each tenant so agents and analytics can run safely at enterprise scale.

The Enterprise Security Challenge

Enterprises need to balance two critical requirements:

  • Centralized Intelligence: Access to all data for AI agents and analytics
  • Complete Isolation: Each company's data must be completely separate
  • Multi-Tenant Operations: Support multiple companies on one platform
  • Compliance: Meet GDPR, industry regulations, and security standards
  • Scalability: Support unlimited companies and outlets

Craveva AI Enterprise solves this with multi-tenant isolation enforced end-to-end (auth context → queries → storage), ensuring each company and outlet stays segregated while enabling AI inside the tenant.

How Craveva AI Enterprise Ensures Data Isolation

Craveva AI Enterprise implements multi-tenant security through:

Database-Level Isolation

Company-Level Isolation:

  • All data tagged with company_id in MongoDB
  • All queries automatically filtered by company_id
  • Compound indexes ensure efficient isolation: { company_id: 1, ... }
  • No cross-company data access possible

Outlet-Level Isolation (for multi-outlet companies):

  • Outlet data tagged with outlet_id
  • Queries filtered by both company_id and outlet_id
  • Users can be restricted to specific outlets
  • Each outlet's POS data, inventory, sales tracked separately

JWT Authentication with Context

Craveva AI Enterprise uses JWT tokens that include:

  • Company ID: Ensures user can only access their company's data
  • User Role: Master Admin, Super Admin, Admin, Project Manager, Team Lead, Member
  • Outlet Access: Optional outlet restrictions for users
  • Permissions: Role-based permissions for data access

All API requests automatically filtered by company_id from JWT token.

Role-Based Access Control (RBAC)

Six user roles with different access levels:

  • Master Admin: Platform-wide access (Craveva AI team only)
  • Super Admin: Company-wide access, can manage admins
  • Admin: Full company access, can manage agents and data sources
  • Project Manager: Project-level access, team management
  • Admin: Company-wide access, can manage agents and data sources
  • Member: Limited access, assigned agents only

RBAC ensures users only access data they're authorized to see.

Data Encryption

Craveva AI Enterprise provides:

  • Encryption at Rest: All data encrypted in MongoDB
  • Encryption in Transit: TLS/SSL for all API communications
  • API Key Encryption: Secure storage of API credentials
  • Password Hashing: bcrypt with salt for user passwords

Audit Logging

Complete audit trail:

  • User Actions: All user actions logged with timestamps
  • Data Access: Track who accessed what data
  • Agent Executions: Log all agent executions with prompts and responses
  • Billing Events: Track all billing and usage events
  • Security Events: Log authentication, authorization, and access attempts

Multi-Tenant Architecture Benefits

Craveva AI Enterprise's multi-tenant architecture provides:

Complete Data Isolation

  • Company Separation: Each company's data completely isolated
  • Outlet Separation: Each outlet's data isolated within company
  • User Restrictions: Users can be limited to specific outlets
  • No Data Leakage: Impossible for one company to access another's data

Centralized Intelligence

  • Unified Platform: All companies use same platform infrastructure
  • Shared Resources: Efficient resource usage across all tenants
  • Scalable Architecture: Add unlimited companies without performance issues
  • Cost Efficiency: Shared infrastructure reduces per-company costs

Security and Compliance

  • Enterprise Security: JWT authentication, RBAC, data encryption
  • GDPR Compliance: Data isolation enables GDPR compliance
  • Audit Trails: Complete logging for compliance and security audits
  • Access Control: Granular permissions per user, per outlet

Real-World Example: Multi-Company Platform

Craveva AI Enterprise can host multiple restaurant chains on one platform:

Company A (20 outlets):

  • Connected 20 Qashier POS systems
  • 5 users: 1 Super Admin, 2 Admins, 2 Team Leads
  • All data tagged with company_id: "company_a"
  • Users can only see Company A's data

Company B (15 outlets):

  • Connected 15 Eats365 POS systems
  • 4 users: 1 Super Admin, 1 Admin, 2 Members
  • All data tagged with company_id: "company_b"
  • Users can only see Company B's data

Data Isolation:

  • Company A's sales data completely separate from Company B
  • Company A users cannot see Company B's data (enforced by JWT + queries)
  • Each company's AI agents operate independently
  • Platform provides shared infrastructure but complete data separation

Security Features

Authentication

  • JWT Tokens: Secure token-based authentication
  • Token Expiration: Automatic token expiration and refresh
  • Multi-Factor Authentication: Optional 2FA support
  • Password Policies: Strong password requirements

Authorization

  • RBAC: Six user roles with different permissions
  • Outlet Restrictions: Users can be limited to specific outlets
  • API Key Management: Secure API key storage and rotation
  • Permission Granularity: Fine-grained permissions per feature

Data Protection

  • Encryption: All data encrypted at rest and in transit
  • Backup Security: Encrypted backups
  • Data Retention: Configurable data retention policies
  • Data Deletion: Secure data deletion with audit trails

Compliance Support

Craveva AI Enterprise architecture supports:

  • GDPR: Data isolation enables GDPR compliance
  • Data Privacy: Complete data separation ensures privacy
  • Industry Standards: Follows enterprise security best practices
  • Security Certifications: Architecture designed for security audits
  • Audit Requirements: Complete audit trails for compliance

Best Practices

  1. Use Outlet Restrictions: Limit users to specific outlets when possible
  2. Regular Access Reviews: Review user access permissions regularly
  3. Monitor Audit Logs: Check audit logs for unusual access patterns
  4. Rotate API Keys: Rotate API keys regularly
  5. Use Strong Passwords: Enforce strong password policies

Conclusion

Craveva AI Enterprise's multi-tenant architecture ensures complete data isolation between companies and outlets while enabling centralized intelligence. By using company_id and outlet_id isolation with JWT authentication and RBAC, the platform provides enterprise-grade security that protects your data while enabling powerful AI capabilities. Each company's data remains completely separate, users can be restricted to specific outlets, and the architecture scales to support unlimited companies. This security architecture protects your enterprise data while enabling efficient multi-tenant operations.

KPIs to track

MetricArea
No-show rate (if reservations) and recovery conversionsSales
Returned goods and vendor credit recovery timeOther
Top out-of-stock drivers (forecast vs ordering vs receiving)Other
Invoice mismatch rate (price/quantity) and resolution timeProcurement
SOP compliance rate and audit pass rateOperations
Agent adoption rate (active users) and resolution timeOther

Connect Now: AI Enterprise Consultants

Ready to transform your F&B operations with Craveva AI Enterprise? Book a meeting with our AI Enterprise Consultants to discuss how we can help your business.

Technical Glossary

Artificial Intelligence (AI)

AI/ML

The simulation of human intelligence in machines that are programmed to think and learn like humans. In F&B, AI is used to automate decisions, analyze data, and provide insights.

Machine Learning (ML)

AI/ML

A subset of AI that enables systems to learn and improve from experience without being explicitly programmed. ML algorithms identify patterns in data to make predictions or decisions.

Large Language Model (LLM)

AI/ML

Advanced AI models trained on vast amounts of text data that can understand and generate human-like text. Used in chatbots, content generation, and natural language processing.

RAG (Retrieval-Augmented Generation)

AI/ML

An AI technique that combines information retrieval with text generation. RAG systems retrieve relevant information from a knowledge base and use it to generate accurate, context-aware responses.

AI Agents

AI/ML

Autonomous software programs that use AI to perform tasks, make decisions, and interact with systems. In F&B, agents can automate customer service, procurement, inventory management, and more.

Embeddings

AI/ML

Numerical representations of text, images, or other data that capture semantic meaning. Embeddings enable AI systems to understand relationships and similarities between different pieces of information.

Vector Database

AI/ML

A specialized database designed to store and query high-dimensional vectors (embeddings). Used in RAG systems to quickly find relevant information based on semantic similarity.

Neural Networks

AI/ML

Computing systems inspired by biological neural networks. They consist of interconnected nodes (neurons) that process information and learn patterns from data.

Natural Language Processing (NLP)

AI/ML

A branch of AI that enables computers to understand, interpret, and generate human language. Used in chatbots, sentiment analysis, and text analysis.

Deep Learning

AI/ML

A subset of machine learning that uses neural networks with multiple layers to learn complex patterns in data. Particularly effective for image recognition, speech recognition, and natural language processing.

Data Centralization

Data

The process of consolidating data from multiple sources (POS systems, databases, files, APIs) into a single unified platform. Essential for AI systems to work effectively with all business data.

Data Integration

Data

The process of combining data from different sources into a unified view. Enables businesses to access and analyze all their data in one place.

ETL (Extract, Transform, Load)

Data

A data integration process that extracts data from source systems, transforms it to fit business needs, and loads it into a target database or data warehouse.

Data Warehouse

Data

A centralized repository that stores integrated data from multiple sources. Designed for querying and analysis rather than transaction processing.

API (Application Programming Interface)

Data

A set of protocols and tools that allows different software applications to communicate and share data. APIs enable integration between systems.

Database

Data

An organized collection of data stored and accessed electronically. Common types include relational databases (PostgreSQL, MySQL) and NoSQL databases (MongoDB).

Data Pipeline

Data

A series of data processing steps that move data from source systems to destination systems, often with transformations along the way.

Data Governance

Data

The overall management of data availability, usability, integrity, and security. Ensures data quality and compliance with regulations.

Data Quality

Data

The measure of data's fitness for its intended use. High-quality data is accurate, complete, consistent, and timely.

Business Intelligence (BI)

Data

Technologies and strategies used to analyze business data and provide actionable insights. Includes reporting, analytics, and data visualization.

POS (Point of Sale)

Operations

The system where customers complete transactions. POS systems record sales, manage inventory, process payments, and generate receipts. Examples include Qashier, Eats365, and Dinlr.

Inventory Management

Operations

The process of ordering, storing, and using inventory. Effective inventory management ensures the right products are available at the right time while minimizing waste and costs.

Supply Chain

Operations

The network of organizations, people, activities, and resources involved in moving products from suppliers to customers. Includes procurement, logistics, and distribution.

Procurement

Operations

The process of finding, acquiring, and managing goods and services needed for business operations. Includes supplier selection, negotiation, and purchase order management.

Food Cost

F&B

The cost of ingredients used to prepare menu items. Food cost percentage is calculated as (cost of ingredients / menu price) × 100. A key metric for profitability.

Labor Cost

F&B

The total cost of employee wages, benefits, and related expenses. Labor cost percentage is calculated as (total labor cost / total revenue) × 100.

Menu Engineering

F&B

The analysis of menu items based on profitability and popularity. Helps restaurants optimize menu offerings to maximize revenue and profit.

Average Order Value (AOV)

F&B

The average amount spent per customer transaction. Calculated as total revenue divided by number of orders. Increasing AOV is a key revenue growth strategy.

Customer Lifetime Value (CLV)

F&B

The total revenue a business can expect from a single customer over their entire relationship. Helps prioritize customer retention and acquisition strategies.

Waste Reduction

Operations

Strategies and processes to minimize food waste, inventory spoilage, and operational inefficiencies. Reduces costs and improves sustainability.

Cloud Computing

Technology

The delivery of computing services (servers, storage, databases, software) over the internet. Provides scalability, flexibility, and cost efficiency.

SaaS (Software as a Service)

Technology

A software delivery model where applications are hosted by a vendor and made available to customers over the internet. Users access software through web browsers.

API Integration

Technology

The process of connecting different software systems using APIs. Enables data sharing and workflow automation between applications.

Microservices

Technology

An architectural approach where applications are built as a collection of small, independent services. Each service handles a specific business function.

Automation

Technology

The use of technology to perform tasks with minimal human intervention. In F&B, automation can handle repetitive tasks like order processing, inventory updates, and reporting.

Workflow

Technology

A series of steps or tasks that need to be completed to achieve a business goal. Workflow automation uses technology to streamline and automate these processes.

Real-time Processing

Technology

The processing of data immediately as it is received, without delay. Enables instant insights and responses, critical for operational decision-making.

Scalability

Technology

The ability of a system to handle growing amounts of work or to be easily expanded. Critical for businesses that plan to grow or handle variable workloads.

Dashboard

Technology

A visual display of key business metrics and KPIs. Provides at-a-glance views of performance and helps identify trends and issues quickly.

KPI (Key Performance Indicator)

Technology

Measurable values that demonstrate how effectively a business is achieving key objectives. Common F&B KPIs include food cost percentage, labor cost percentage, and AOV.

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