Egon Technical Whitepapers

Explore the technical architecture behind Egon's AI-powered art advisory platform. Comprehensive explanations of our data infrastructure, AI systems, investment frameworks, and aesthetic intelligence.

Data Architecture AI/LLM Systems Investment Frameworks Aesthetic Intelligence

Data Infrastructure

Egon creates a unified artist universe from 10+ disparate data sources using a 7-tier canonical resolution system, enabling cross-dataset intelligence impossible elsewhere.

The Art Market Graph

Unified Knowledge Architecture for an Opaque Market

8 min read

How Egon solves art market identity chaos with a 7-tier canonical resolution pipeline (99.98% link rate) that unifies 10+ disparate data sources, enabling impossible-elsewhere queries like "Find artists aesthetically similar to Basquiat but cheaper" via multi-dimensional vector embeddings.

Key Topics:
  • Unified artist resolution across fragmented data sources with high accuracy
  • Multi-dimensional vector search: aesthetic + market + institutional + investment embeddings
  • Cross-dataset intelligence: auction history + news signals + museum data linked via canonical IDs
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AI Systems

Egon democratizes expert art advisory through autonomous AI orchestration with 21 specialized tools, delivering comprehensive analysis in 3-5 minutes.

Agentic Art Intelligence

Multi-Agent Orchestration for Institutional-Grade Research

10 min read

How Egon democratizes expert art advisory with autonomous AI tool orchestration. Specialized tools replicate human advisor workflows—market research, artist discovery, portfolio analysis—delivering comprehensive analysis in minutes. Ask Egon chatbot provides conversational access to all tools simultaneously, enabling complex multi-source queries impossible with static analyses.

Key Topics:
  • Multi-stage analysis pipelines: mimics advisor workflow (gather data → synthesize → recommend)
  • Specialized tools replicating advisor expertise across research domains
  • Web search + proprietary data fusion: real-time market intelligence layered on canonical data
  • Ask Egon chatbot: conversational access to all tools with autonomous orchestration
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Investment Science

Egon's quantitative frameworks combine multi-signal confluence scoring, tier-aware investment grading, aesthetic alignment, and portfolio optimization.

Confluence: Art Investment Scoring

A Quantitative Framework for Acquisition Decisions

7 min read

How Egon detects breakout momentum before broader market awareness with proprietary tier-aware confluence scoring. Discovery-tier signals receive elevated weighting (rare, highly predictive) versus Blue-chip signals (expected performance, less alpha potential).

Key Topics:
  • Tier-aware confluence scoring: proprietary algorithm with methodological framework
  • Investment tiers reflecting market segmentation and liquidity characteristics
  • Multi-category investment grading with confidence scoring
  • Stage-aware portfolio optimization across collection maturity levels
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Aesthetic Intelligence

Egon learns each collector's unique aesthetic vision through conversational AI onboarding and evolving multi-dimensional preference profiles, building collections with curatorial merit.

Scaling Connoisseurship

Learning Aesthetic Identity Through Interaction

9 min read

How Egon learns each collector's unique aesthetic vision through conversational AI onboarding, evolving multi-dimensional preference profiles, and vector embeddings. Goes beyond investment metrics to build collections with curatorial merit—balancing financial returns (A+ to C- grading) with aesthetic alignment (hierarchical scoring) to help collectors build narratively coherent portfolios tailored to their taste.

Key Topics:
  • Conversational AI onboarding: multi-stage discovery extracting unarticulated preferences
  • Sophisticated preference architecture separating core identity from evolving taste
  • Implicit learning from user interactions across multiple engagement patterns
  • Multi-dimensional embeddings for semantic matching across collection dimensions
  • Aesthetic alignment scoring with hierarchical weighting and dynamic thresholds
  • Curatorial analysis for building collections with narrative merit
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