CORE FEATURES
AI Assistant (Veni)
Intelligent chatbot powered by Google Gemini for metadata extraction and conversational item creation.
Overview
Invenicum’s AI Assistant, named Veni, leverages Google Gemini to streamline inventory management through natural language conversations. Veni can extract product metadata from URLs, auto-fill item details, and guide you through creating inventory items using simple chat commands.
Smart Extraction
Extract product details from web URLs automatically
Conversational UI
Create items through natural language chat
Multi-language
Supports locale-based responses (en, es, etc.)
Persistent History
Chat history syncs across devices
How It Works
The AI Assistant integrates with your workflow in two primary ways:
- URL-Based Metadata Extraction
When adding a new item, paste a product URL (Amazon, eBay, manufacturer site) and Veni will automatically extract:
Paste Product URL
In the item creation form, look for the “Extract from URL” option and paste the product link.
Review & Confirm
Veni will populate the form with extracted data. Review for accuracy and make manual adjustments if needed.
Save Item
Complete the creation process with pre-filled metadata, saving significant time.
The AI extraction works best with structured product pages from major retailers and manufacturers. Custom or poorly formatted pages may yield incomplete results.
- Conversational Item Creation
Chat directly with Veni to create items, search your inventory, or get help navigating the system.
Add a new laptop to my Tech Equipment container
I can help you create a laptop. What’s the model name?
Dell XPS 15, 32GB RAM, 1TB SSD
Got it! I’ll create a Dell XPS 15. Should I add those specs as custom fields?
Yes
Created “Dell XPS 15” in Tech Equipment with RAM and storage specs.
Use the SAY_HELLO_INITIAL command when first opening the chat interface to get a personalized greeting based on your locale.
Technical Implementation
Veni is powered by two core services:
ChatService
Manages conversation state and message history:
- Persistent History: Messages stored server-side via
/ai/chat/history - Real-time Updates: Uses Flutter’s
ChangeNotifierto update UI instantly - Locale Support: Automatically sends user language preferences to Gemini
lib/data/services/veni_chatbot_service.dart
AIService
Handles URL metadata extraction:
- Endpoint:
POST /ai/extract - Input: URL and list of fields to extract
- Output: Structured JSON matching your item schema
lib/data/services/ai_service.dart
Use Cases
E-commerce Imports
Quickly catalog online purchases by pasting order confirmation links.
Equipment Onboarding
Add new office equipment by extracting specs from manufacturer sites.
Parts Inventory
Auto-populate technical specifications for electronic components.
Collection Management
Fast-track adding collectibles with details from auction sites.
Best Practices
For URL Extraction
Use Direct Product Pages
Avoid category pages or search results
Check Extracted Data
AI interpretation isn’t perfect — always review
Supplement Custom Fields
Add domain-specific fields manually after extraction
For Chat Interactions
Be Specific
”Add laptop” is vague; “Add Dell XPS 15 to Tech Equipment” works better
One Action at a Time
Break complex workflows into individual requests
Provide Context
Mention container names, quantities, or locations explicitly
The AI Assistant requires an active internet connection and uses your organization’s API quota for Google Gemini. Large-scale extractions may incur additional costs.
Limitations
Rate Limits
Heavy usage may hit Gemini API quotas
Accuracy
Extraction quality depends on source page structure
Language
Works best with English; other languages may have reduced accuracy
Privacy
URLs are sent to Google’s servers for processing
API Reference
For developers integrating with Veni, here are the core endpoints:
/ai/extractURL metadata extractionExtracts product metadata from a given URL. Returns structured JSON matching your item schema.
/ai/chat/historyChat history retrievalLoads the persistent chat history for the current user from the server.
See the full API Reference for complete documentation on the Chat Service and AI Service implementations.