Galya

Taste Infrastructure for AI Systems

Aesthetic Intelligence for AI Native Platforms

The missing layer between what users want and what AI produces. We've built the multimodal Taste Graph that allows platforms and agents to understand, model, and act on human preference — in real time.

Early design partners welcome. Limited slots available.

Explore the Playground

We’re selectively onboarding design partners.

If you're building AI systems that make decisions on behalf of users — this is the missing layer. Most AI systems don’t understand user preference, they approximate it. We’re working with a small number of teams to fix that at the infrastructure level. If you're building agents, marketplaces, or AI-native platforms and care about alignment → decision → conversion, we should talk. We work closely with partners to: – structure user preference into computable taste – integrate it into ranking, generation, and agents – measure impact on conversion, engagement, and alignment

Includes direct access to the Taste Graph, custom indexing + data enrichment, and weekly iteration + benchmarking.

– Get in touch to join

What We Do

Turn User Signals into Structured Taste Intelligence

What is Aesthetic Intelligence?

Aesthetic intelligence is how systems understand not just what is relevant, but what is right for the user. It is the missing layer between relevance and preference. AI systems today can retrieve what is relevant. They cannot determine what is aligned with a user. That’s why users regenerate outputs, refine queries, and abandon results — not because the system failed, but because it didn’t understand what the user actually wanted.
Aesthetic intelligence powers: – modeling human preference as structure – understanding why something resonates with a user – acting on that preference in real time
We believe taste is patterned, not random, and that those patterns can be computed and modeled.
Aesthetic Intelligence is the ability of a system to understand, model, and predict human taste. It goes beyond demographics or surface behavior. It captures the visual, cultural, contextual, and emotional signals that shape what someone finds aligned, beautiful, or desirable.

"Traditional AI optimizes for relevance. Aesthetic intelligence optimizes for resonance."

How Galya Powers It

At Galya, aesthetic intelligence is powered by a multimodal taste graph that maps relationships between user signals, content entities, style patterns, and real-world inventory. All entities exist in a shared space.

We index destinations, hotels, brands, storefronts, and digital content — computing their aesthetic identity in a shared vector space.

When partner-side behavioral data is sent to Galya, we compute probabilistic archetype compositions and return structured preference intelligence.

The output is not recommendations. It is structured preference intelligence that systems can act on.

Powers

AI-native itinerary platformsHospitality chat agentsDiscovery systemsMedia segmentation layersConversational AI systemsAI agentsMarketplaces

Core Workflow

01

Sync User Signals

Send behavioral events via API (saves, clicks, dwells) or batch import.

02

Map Against the Taste Graph

Signals are cross-referenced against indexed content clusters and archetype models.

03

Generate Structured Preference Outputs

Receive probabilistic archetype compositions and entity-level affinity scores.

04

Power Personalization

Inject enriched context into your LLM pipeline, re-ranking logic, or UI layer.

How It Integrates

Model Wrapper (Fastest Path)

Route LLM calls through Galya's /ask endpoint. We:

  • • Enrich prompts with structured taste signals
  • • Pass enhanced context to your underlying model
  • • Return outputs aligned with user preference
  • • Minimal engineering friction.

API Enrichment Layer

Use /users/create, /index, /users/composition, and /recommend to integrate preference intelligence directly into your stack.

  • • Structured user modeling
  • • CRM-level taste visibility
  • • Full control over inference

Why Galya

Most personalization systems rely on static segments or surface-level embeddings. Galya is different.

Dynamic graph (not fixed segments)
Multimodal content ingestion
Probabilistic archetype modeling
Works with minimal user data
Designed for real-time enrichment
Two-sided web indexing

We don't just compute similarity — we compute preference structure.

Early Traction

80K+

User Records Indexed

200+

Archetype Compositions Generated

150K+

Content Entities Analyzed

Graph density and indexing depth expand continuously as we onboard new partners.