"Your personal AI concierge that remembers every great experience you've ever had."
TasteGraph is an AI-native application built on the AINative Studio platform that automatically captures, organizes, understands, and recommends the real-world experiences of its users.
Unlike Yelp, Google Maps, TripAdvisor, or public review platforms, TasteGraph builds a private lifelong memory of a user's own experiences.
Over time, the system develops a semantic understanding of the user's tastes, preferences, relationships, favorite cities, favorite restaurants, favorite events, and favorite experiences.
The result is an AI agent capable of acting like a trusted friend who has perfect memory.
Imagine asking:
"Where should my parents eat in Austin?"
Instead of searching Google, the AI searches years of your own memories and responds:
Based on your experiences, your parents would probably love these five restaurants. They all have quiet atmospheres, excellent seafood, outdoor seating, and you've rated each at least 4 stars.
The recommendations come from your own life, not anonymous internet reviews.
TasteGraph is built around one principle:
Your own experiences are more valuable than everyone else's reviews.
Rather than collecting public ratings, TasteGraph builds a living knowledge graph of everything you've personally experienced.
Every restaurant...
Every concert...
Every vacation...
Every coffee shop...
Every park...
Every hotel...
becomes part of your personal intelligence.
The platform should:
- Automatically remember experiences
- Require almost zero manual effort
- Continuously learn preferences
- Build lifelong memory
- Generate personalized recommendations
- Recommend only experiences the user genuinely loved
- Become smarter over time
This project should leverage existing AINative services wherever possible to minimize custom application code.
Primary orchestration layer.
Responsibilities:
- Long-running agents
- Background jobs
- Event processing
- Scheduled workflows
- Multi-agent coordination
Primary datastore.
Use ZeroDB for:
- Structured tables
- Vector storage
- Semantic search
- Metadata
- Event storage
Persistent AI memory.
Stores:
- User preferences
- Long-term facts
- Conversation memory
- Learned taste
- Friend preferences
Responsible for:
- Classification
- Summarization
- Embeddings
- Extraction
- Recommendation reasoning
- Semantic tagging
Stores relationships between:
User
↓
Experiences
↓
Cities
↓
Venues
↓
People
↓
Preferences
↓
Recommendations
↓
Ratings
Frontend components.
Reuse wherever possible.
Examples:
- AI Chat
- Timeline
- Cards
- Voice UI
- Maps
- Search
- Recommendation Lists
Used for analytics.
Examples:
- Favorite cities
- Favorite cuisine
- Recommendation quality
- Preference evolution
- Travel history
- Long-term insights
User visits location
↓
Agent Cloud detects visit
↓
Experience Agent creates experience
↓
Models API extracts metadata
↓
ZeroDB stores structured data
↓
ZeroMemory updates long-term memory
↓
Knowledge Graph updates relationships
↓
User rates experience
↓
Taste Agent updates embeddings
↓
Future recommendations improve
Responsibilities
- Detect experiences
- Merge duplicate signals
- Create Experience records
Inputs
- Calendar
- GPS
- Photos
- Gmail
- Reservations
- Travel confirmations
Outputs
Experience objects
Responsibilities
Generate structured memories.
Outputs
- Summary
- Metadata
- Embeddings
- Highlights
- Complaints
Responsibilities
Collect lightweight feedback.
Target completion:
Under 30 seconds.
Captures:
- Overall Rating
- Food
- Service
- Atmosphere
- Value
- Return Intent
Responsibilities
Learn preferences.
Examples
User likes:
- Outdoor seating
- Craft cocktails
- Quiet restaurants
- Walkable neighborhoods
User dislikes:
- Loud music
- Tourist traps
- Poor service
These become weighted preferences.
Answers questions like:
Where should I eat?
Best tacos I've ever had?
Weekend in Portland?
Best date night?
Kid friendly?
Business dinner?
Parents visiting?
Uses:
- Ratings
- Embeddings
- Preferences
- Context
- Knowledge Graph
Stores recurring profiles.
Examples
Mom
Seafood
Gardens
Quiet
Dad
Steak
Museums
Sarah
Vegan
Coffee
Children
Parks
Ice cream
Interactive museums
Recommendations become personalized.
The application revolves around these primary entities:
User
Experience
Venue
City
Rating
Preference
Person
Recommendation
Recommendation Feedback
Knowledge Graph Edge
Embedding
Memory
Share Session
By default, recommendations should never include mediocre experiences.
Default filter:
Overall Rating >= 4
Only if explicitly requested should the AI recommend lower-rated places.
Recommendation ranking should prioritize:
- Rating
- Semantic similarity
- User preferences
- Companion preferences
- Occasion
- Recency
- Confidence score
Experience detected
↓
Metadata extracted
↓
Experience summarized
↓
Stored
↓
Embedded
↓
Knowledge Graph updated
↓
User rates experience
↓
Preferences updated
↓
Recommendation engine updated
Natural language search should work everywhere.
Examples
Best tacos I've ever had.
Favorite coffee in Seattle.
Restaurants my wife loved.
Every five-star restaurant in New York.
Quiet dinner with outdoor seating.
Family-friendly places in Austin.
Concerts from 2025.
Best date nights.
Example:
Parents visiting Chicago.
Agent asks:
Budget?
Walking?
Kids?
Cuisine?
Then returns:
- 5 restaurants
- Coffee
- Walk
- Dessert
- Museums
using only experiences the user rated highly.
Simple.
Fast.
Private.
Ratings
⭐ 1
Never again.
⭐⭐ 2
Below average.
⭐⭐⭐ 3
Good.
⭐⭐⭐⭐ 4
Recommend.
⭐⭐⭐⭐⭐ 5
Exceptional.
The rating system is not intended to compete with public reviews.
It exists to strengthen future recommendations.
Relationships include
User
VISITED
Venue
Experience
IN_CITY
City
User
LIKES
Attribute
User
DISLIKES
Attribute
Experience
WITH
Person
Recommendation
FOR
Person
Venue
TAGGED_AS
Cuisine
Venue
TAGGED_AS
Atmosphere
Version 1 should include:
✅ Authentication
✅ User onboarding
✅ Experience detection
✅ AI summaries
✅ Timeline
✅ Ratings
✅ Recommendation engine
✅ Taste learning
✅ Friend profiles
✅ City pages
✅ AI chat
✅ Share recommendations
Everything else should be deferred until after MVP.
- Apple Watch integration
- Oura integration
- WHOOP integration
- Camera vision
- Receipt OCR
- Menu recognition
- Trip planner
- AI travel guide
- Collaborative recommendations
- Taste similarity
- Public recommendation publishing
- Voice-first interaction
- Offline mode
- Build on existing AINative APIs before writing custom code.
- Treat every feature as an Agent Cloud workflow where possible.
- Use ZeroDB as the single source of truth for structured and vector data.
- Store durable user preferences in ZeroMemory and expose them through semantic retrieval.
- Model relationships in the Knowledge Graph instead of duplicating data.
- Favor event-driven workflows over synchronous processing.
- Design agents to be idempotent and composable.
- Keep AI prompts versioned and configurable.
- Every experience should improve future recommendations.
A feature is considered complete when:
- It is orchestrated through Agent Cloud where appropriate.
- Data is persisted in ZeroDB using the agreed schema.
- Relevant memories are written to ZeroMemory.
- Knowledge Graph relationships are updated.
- Embeddings are generated for semantic search.
- AI reasoning uses structured data before free-text prompts.
- The frontend uses AIKit components whenever available.
- Events are emitted for analytics and Lakehouse ingestion.
- Tests cover business logic and agent workflows.
- Documentation is updated.
TasteGraph transforms a lifetime of experiences into a living, continuously learning AI memory. Every meal, trip, concert, hike, hotel, and conversation enriches a personal knowledge graph that becomes more valuable with time. By combining explicit ratings, semantic memory, vector search, and autonomous agents, TasteGraph evolves into a trusted personal concierge capable of delivering recommendations that feel less like search results and more like advice from your future self.