Founder Summary
- Audience: CXOs and founders running bakeries, QSR, fine dining, catering.
- Core outcomes (what moves the business):
- 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.
- Time savings: remove manual exports, reporting, and SOP Q&A with Craveva AI Enterprise automation.
Platform flow (high level)
- Deployment layer: deploy agents to WhatsApp, web widget, kiosks, or internal tools with Craveva AI Enterprise.
- AI layer: agents query and act on governed data (no fragile spreadsheet workflows) in Craveva AI Enterprise.
- Data layer: connect POS, databases, Google Drive, and APIs into a unified view inside 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 (30–60 minutes to first value)
- 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.
- 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.
Leadership Metrics
- Invoice mismatch rate (price/quantity) and resolution time
- Delivery basket value vs dine-in basket value (mix shift)
- Onboarding time to proficiency (by role)
- Recipe compliance variance and portion drift
- Purchase-to-receive variance by category
Explore the Platform
- Deployment: /solutions/deployment
- Documentation: /documentation
- Models: /ai-models
- Templates: /templates
- Architecture: /solutions/architecture
Introduction
Enterprise systems integration enables Craveva AI Enterprise to access data from ERP, CRM, accounting systems, and databases, creating a unified data warehouse that powers AI agents. The platform supports multiple integration methods including REST API, GraphQL API, and direct database connections, making it easy to connect any enterprise system.
Why Integrate Enterprise Systems
Craveva AI Enterprise integration provides:
- Unified Data Warehouse: All enterprise data in one place, accessible to all AI agents
- Automated Workflows: AI agents can trigger actions in external systems
- Real-Time Sync: Live data updates from all connected systems
- Cross-System Analytics: AI Data Analysis Agent can query data across all systems
- Intelligent Automation: Agents use data from multiple systems for comprehensive automation
Integration Methods
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REST API Integration
Craveva AI Enterprise supports REST API connectors:
- Add API Data Source: Navigate to Data Sources → Add Data Source → REST API
- Enter Endpoint: Provide base URL (e.g.,
https://api.example.com/v1) - Configure Authentication:
- API Key: Header-based authentication
- Bearer Token: OAuth 2.0 token authentication
- Basic Auth: Username/password authentication
- Test Connection: Platform validates endpoint and authentication
- Automatic Data Mapping: Platform detects API structure and maps data automatically
Supported Features:
- GET, POST, PUT, DELETE methods
- Query parameters
- Request headers
- Response parsing
- Error handling
GraphQL Integration
Craveva AI Enterprise supports GraphQL API connectors:
- Add GraphQL Data Source: Navigate to Data Sources → Add Data Source → GraphQL API
- Enter Endpoint: Provide GraphQL endpoint URL
- Schema Introspection: Platform automatically introspects GraphQL schema
- Configure Queries: Define queries to fetch data
- Test Connection: Validate endpoint and schema
Supported Features:
- Schema introspection
- Query execution
- Mutation support
- Subscription support (if available)
- Variable support
Database Integration
Craveva AI Enterprise connects directly to databases:
Supported Databases:
- PostgreSQL: Full support with connection pooling
- MySQL: Full support with connection pooling
- MongoDB: Native MongoDB support
- SQL Server: Full support
- Oracle: Full support
- BigQuery: Google BigQuery support
- Snowflake: Data warehouse support
- Redshift: AWS Redshift support
- Athena: AWS Athena support
- ClickHouse: Analytics database support
- Trino: Distributed query engine support
- DuckDB: Embedded analytics support
Connection Process:
- Add Database Data Source: Navigate to Data Sources → Add Data Source → Database
- Select Database Type: Choose from supported databases
- Enter Connection Details:
- Host, port, database name
- Username, password
- SSL/TLS configuration
- Test Connection: Platform validates connection
- Automatic Schema Detection: Platform detects tables, columns, relationships
Common Enterprise System Integrations
ERP Systems
Craveva AI Enterprise integrates with ERP systems via API or database:
- Odoo: REST API or PostgreSQL database connection
- SAP: REST API or database connection (if accessible)
- Microsoft Dynamics: REST API integration
- Custom ERP: REST API, GraphQL, or database connection
Use Cases:
- AI Procurement Assistant uses ERP inventory data
- AI Data Analysis Agent queries ERP financial data
- Automated workflows sync data between systems
CRM Systems
Craveva AI Enterprise connects to CRM systems:
- HubSpot: REST API integration
- Salesforce: REST API integration
- Custom CRM: REST API, GraphQL, or database connection
- Customer Databases: Direct database connection
Use Cases:
- AI Customer Service Agent accesses customer history
- AI Sales Agent uses customer data for personalization
- AI Auto Customer Acquisition analyzes customer segments
Accounting Systems
Craveva AI Enterprise integrates with accounting systems:
- Xero: REST API integration
- QuickBooks: REST API integration
- Custom Accounting: REST API or database connection
- Financial Databases: Direct database connection
Use Cases:
- AI Data Analysis Agent generates financial reports
- Automated financial data sync
- Cost analysis and optimization
Step-by-Step Integration Process
Step 1: Identify Integration Points
Determine what to integrate:
- Data Sources: Which systems contain data needed by AI agents?
- Workflow Triggers: What events should trigger agent actions?
- Output Destinations: Where should agents send results?
- Sync Requirements: How often should data sync?
Step 2: Add Data Source in Craveva AI Enterprise
- Log into Dashboard: Navigate to Data Sources section
- Click "Add Data Source": Choose integration type (REST API, GraphQL, Database)
- Enter Connection Details: Provide endpoint, credentials, connection string
- Test Connection: Platform validates connection automatically
- Save Configuration: Connection saved for use by all agents
Time: 5-15 minutes per system
Step 3: Automatic Data Mapping
Craveva AI Enterprise's AI-Assisted Agent Builder automatically:
- Detects Schema: Identifies tables, columns, relationships
- Maps Entities: Maps data entities to standard formats
- Creates MDL: Generates Modeling Definition Language for data access
- Validates Mapping: Ensures data mapping is correct
Step 4: Configure AI Agents
Once data sources are connected:
- Create Agent: Use AI-Assisted Agent Builder
- Select Data Sources: Choose which data sources agent can access
- Configure Capabilities: Define what agent can do with data
- Test Agent: Use Live Preview to test agent with real data
- Deploy Agent: Deploy to messaging platforms, websites, or custom systems
Step 5: Monitor and Optimize
Craveva AI Enterprise provides:
- Connection Status: Monitor data source health
- Sync Status: Track data synchronization
- Usage Analytics: See which agents use which data sources
- Error Logs: View and resolve integration errors
- Performance Metrics: Monitor query performance and response times
Best Practices
1. Start with One System
Begin with your most critical system:
- Test integration thoroughly
- Validate data accuracy
- Ensure agent works correctly
- Expand to other systems gradually
2. Use API Keys Securely
- Store API keys securely in platform
- Use read-only keys when possible
- Rotate keys regularly
- Monitor key usage
3. Monitor Performance
- Track query response times
- Monitor data sync frequency
- Watch for errors or timeouts
- Optimize slow queries
4. Document Integrations
- Document which systems are connected
- Note data mapping details
- Record any custom configurations
- Keep integration docs updated
Real-World Example
A restaurant chain integrates multiple systems with Craveva AI Enterprise:
Connected Systems:
- 15 Qashier POS systems (via API)
- PostgreSQL database (inventory, suppliers)
- Google Drive (SOP documents, reports)
- Custom ERP system (via REST API)
AI Agents Using Data:
- AI Procurement Assistant: Uses POS sales data + PostgreSQL inventory + ERP supplier data
- AI Customer Service Agent: Uses POS order history + CRM customer data
- AI Data Analysis Agent: Queries all systems for comprehensive reports
- AI Internal Coach: Uses Google Drive SOP documents
Results:
- Unified view of all enterprise data
- AI agents access data from all systems
- Automated workflows across systems
- Comprehensive analytics across all data sources
Troubleshooting
Connection Issues
- Check Credentials: Verify API keys, usernames, passwords
- Check Network: Ensure systems are accessible from internet
- Check Permissions: Verify API keys have required permissions
- Check Logs: Review error logs for specific issues
Data Sync Issues
- Check Sync Status: View sync status in dashboard
- Verify Permissions: Ensure read permissions for data sources
- Check Logs: Review sync logs for errors
- Reconnect: Try disconnecting and reconnecting data source
Conclusion
Craveva AI Enterprise makes enterprise system integration simple and automatic. By supporting REST API, GraphQL, and database connectors, the platform can connect to any enterprise system. The AI-Assisted Agent Builder automatically maps data, and once connected, all AI agents can access data from all systems. With automatic schema detection, data mapping, and error handling, integration takes just minutes per system and unlocks powerful cross-system AI automation capabilities.
KPIs to track
| Metric | Area |
|---|---|
| Delivery basket value vs dine-in basket value (mix shift) | Sales |
| Recipe compliance variance and portion drift | Operations |
| Purchase-to-receive variance by category | Procurement |
| Invoice mismatch rate (price/quantity) and resolution time | Procurement |
| Incident escalation rate and time-to-resolution | Other |
| Shift coverage gaps and last-minute changes | Other |