Funding
Funding not disclosed
Founders
Product
Problem
AI-driven applications often lack a nuanced understanding of individual user preferences, resulting in generic content that fails to align with a user's style, tone, mood, or personality. This gap reduces engagement and limits the effectiveness of personalization in real-time interactions.
Solution
Galya offers a taste intelligence platform that continuously maps user behavior onto a real-time affinity graph, translating signals into structured contextual insights. These insights are injected directly into AI agents and generation pipelines, enabling dynamic personalization of content across style, tone, mood, and narrative fit. The platform operates on an infrastructure-level workflow—Signal → Structure → Action—ensuring that new behavioral data instantly updates the taste graph. By providing AI-native systems with up-to-date contextual relevance, Galya enhances user understanding and engagement without requiring extensive manual tuning.
Target Audience
Primary customers are developers and product teams building AI-native systems—such as conversational agents, recommendation engines, and content generation platforms—that require real-time, behavior-driven personalization.
Features
- Real-time affinity graph that continuously updates as user behavior streams in
- Signal → Structure → Action workflow that converts raw interaction data into actionable context for AI models
- Contextual tags covering style, tone, mood, personality, travel intent, commerce affinity, and narrative fit
- API integration that injects taste intelligence directly into content generation and decision-making pipelines
- Scalable infrastructure built on cloud services (AWS, Databricks) for low-latency personalization