AI that understands
how people behave.
Ningen models user behavior from real interactions to simulate decisions and generate context-aware recommendations grounded in real-world patterns.
User Modeling
Predicts user decisions from behavioral history and context-aware inference signals.
- •Behavioral profiling from review history
- •Fit scoring against target item
- •Voice mimicry with few-shot examples
Recommendations
Semantic retrieval + reasoning layer that adapts to user context in real time.
- •Multi-turn dialogue support
- •Cross-domain retrieval
- •Cold-start & contextual handling
Research & Publications
Read the theoretical foundations behind our models.
Dataset Strategy
How we securely process behavioral logs, manage differential privacy layers, and build context-rich embeddings to train our underlying recommendation models.
Data Curation & Cold-Start Handling
Aggregates behavioral logs and raw multi-turn dialogue histories. It cleans noise, identifies interaction patterns, and builds baseline user profiles to mitigate cold-start issues before processing.
Anonymization & Differential Privacy
Strict PII scrubbing pipelines mask sensitive fields. By enforcing differential privacy standards directly at the feature layer, we ensure absolute user data privacy before it hits training pipelines.
Reasoning & Contextual Alignment
Converts curated data points into high-dimensional context embeddings. This continuously structures user interaction histories into real-time reasoning loops for precise recommendation scoring.
Architecture
API Server
Go + stdlib HTTP
Vector DB
PostgreSQL + pgvector
Embeddings
MiniLM-L6 / ONNX
LLMs
Gemini · Kimi · OpenAI
Yelp · Amazon Reviews · Goodreads · 100k indexed items via HNSW vector search