Kalon
Personal intelligence engine that diagnoses behavioral patterns and delivers targeted experiments.
The System
Behavioral signal processing → persona modeling → adaptive recommendation engine
Behavioral Signals
All collection is granular opt-in. Metadata only — never content.
What It Does
- 01Diagnoses behavioral patterns across 7 psychological dimensions
- 02Generates personalized stop / start / shift experiments
- 03Time-weights signals for recency without discarding history
- 04Reinforcement loop sharpens recommendations with every interaction
- 05Granular opt-in collection — metadata only, never content
Key Decisions
Dimensional Scoring
7 psychological dimensions vs. flat traits. Each user gets a dynamic vector that shifts over time rather than a static label.
↳ Tradeoff: More computation, richer model.
Time-Weighted Signals
7-day signals weighted 1.0, 30-day at 0.5, decay toward mean. Recent behavior matters more without erasing long-term baselines.
↳ Tradeoff: Recency bias vs. stability.
Metadata-Only Collection
Never image content, only metadata. Photo analysis reads EXIF dates and GPS — pixels never leave the device.
↳ Tradeoff: Less signal, but full privacy compliance.
Reinforcement Learning Loop
Every interaction (thumbs up/down, completion, abandonment) feeds back into the model. The system learns what works for each user individually.
↳ Tradeoff: Cold start problem vs. progressive accuracy.