Preference models
Train models that understand the difference between fitting a pattern and making the right creative judgment.
Signals become structure
Palate Engine maps the judgments people make every day—what fits, resonates, belongs, and matters—so intelligent products can reason beyond relevance alone.
Join the teamIntelligence can generate nearly anything. The harder question is knowing what deserves to exist—and how to make it feel right.
Mission
It is a web of identity, context, memory, culture, and intention. Most recommendation systems flatten that richness into clicks.
We build models and agents that preserve the nuance—so software can understand not just what people choose, but why.
Make the unquantifiable useful without sanding it smooth.Train models that understand the difference between fitting a pattern and making the right creative judgment.
Give products and creative tools a reasoning layer for selection, synthesis, critique, and curation.
Build the missing layer
Bring your craft to a small team of researchers, designers, and engineers exploring the systems behind human preference.
Notes from the frontier
Essays, experiments, and technical work about modeling preference without reducing its complexity.
Why consensus metrics erase the exact edge that makes preference useful.
Explore →A framework for context, drift, and decisions that change meaning over time.
Explore →The systems that create more need stronger judgment, not merely more output.
Explore →A small field test
Preference becomes legible one considered choice at a time.