Summary
Wanna Try organizes saved content into actionable plans using AI extraction, reminders, and natural-language queries.
About
Wanna Try applies AI to personal content organization, converting saved recommendations into context-aware plans and reminders. Its strongest fit is individual travel and activity planning rather than enterprise workflows.
Wanna Try accepts reels, posts, links, screenshots, and voice notes, including Hindi-language content. It extracts details such as location, price, and key points, then organizes saved items for later action. Reminders can be timed around weekends, favorable weather, or proximity. Users can ask questions about their saves, such as requesting a Saturday plan, and confirm actions including marking an item as tried, planning a day, or setting a reminder. Spoken trip plans can also be used to create reminders. The product is described as free and built in India.
Key Features
Accepts reels, posts, links, screenshots, and voice notes as inputs
Extracts locations, prices, and key points from submitted content
Supports Hindi-language content
Provides reminders based on timing, weather, and proximity
Answers questions about saved items and supports one-tap planning actions
Real-World Use Cases
Personal travel planning
Collect destination recommendations from social media and other sources, then use extracted details and reminders to plan visits or trips.
Local activity discovery
Save restaurants, attractions, and other places and receive prompts when timing, weather, or proximity makes a visit practical.
Managing saved recommendations
Turn accumulated links and media into an organized list of activities, mark completed experiences, and retrieve items through natural-language questions.
Business Opportunities
Travel and hospitality discovery partnerships
Problem: Travelers often save recommendations across social platforms but may not convert them into visits or bookings.
Solution: Use the product's place extraction and context-aware reminders as a discovery layer that could direct users toward relevant travel or hospitality offerings.
Estimated ROI: Potentially moderate through referral or booking conversion, but outcomes cannot be estimated from the available product information.
Execution: Explore partnerships that connect saved places with relevant destination information or booking pathways; these integrations are potential extensions, not stated product features.
Localized recommendation management
Problem: Users who save recommendations in multiple languages may struggle to extract useful details and act on them later.
Solution: Build on the stated Hindi support and multimodal input workflow to serve regional audiences collecting local travel and activity recommendations.
Estimated ROI: Uncertain; depends on user adoption, extraction reliability, and any future commercial partnerships.
Execution: Validate demand in Hindi-speaking markets and assess extraction accuracy across local content formats before expanding language or partner coverage.