Physical-Context AI.
Intelligence for Retail.
A physical-context AI platform fusing search intent, geo-coordinates, and foot traffic data into store-level retail intelligence. Built and validated with enterprise buyers across multi-national multi-location retail markets.
Enterprise Discovery
Over 20+ Direct product discovery interview sessions conducted with retail leadership and decision-makers at Fortune-500 multi-location retailers across 15,600+ combined store locations validated both the market gap and the barriers to adoption.
retail verticals confirmed the gap: Fashion, Sporting Goods, Home & Living, and Jewelry.
stakeholders per approval chain, from buyer to CEO to VP Commerce. Enterprise adoption friction is structural, not product-related.
Multiple respondents shared contact details for follow-up pilots, a direct product-market fit signal from enterprise buyers.
The Enterprise Problem
Enterprise buyers lack the data infrastructure to make location-aware purchasing decisions.
Gut-Driven Allocation
Enterprise buyers confirmed that <50% of purchasing decisions are data-driven. The remainder relies on intuition, informal signals, and “gut feeling.”
Blind to Geography
Buyers need demand signals by size, color, and gender, segmented by store location. Current BI systems cannot provide geo-specific SKU-level intelligence.
No Unified Signal Layer
Search intent, visitor geo-coordinates, and product catalog data exist in separate silos. No system connects these signals into a unified, store-level demand view, leaving buyers without spatial evidence.
How It Works
AI-Data Chatbot that helps make informed decisions based on first-party local demand data. 11 specialized agents collaborate to deliver store-level intelligence.
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route
OrchestratorIntent classification and dispatch. Routes each query to the optimal agent chain based on context.
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smart_toy
5 Data AgentsGeo-matching, visitor-search fusion, behavior aggregation, spatial visualization, and trend analysis.
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task
5 Business AgentsOffline advertising, store audiences, upselling opportunities, top searched categories, and emerging trends.
Three Signal Sources
- Product Catalog
SKU attributes + store geo-coordinates - Visitor Location
Pseudonymous foot traffic · Hourly - Search Intent
GSC queries, impressions, CTR · Daily
Reliability Guardrails
- ≥50 searches/month
Minimum signal threshold per product - ≥3 months stable data
Temporal consistency before inference - ≥80% label completeness
Data quality gate before any output
Model Selection
- PaLM 2 Bison-002PRODUCTION
- PaLM 2 Bison-001BASELINE
- Gemini 1.0 ProNOT SELECTED
Bison-002 selected for consistent Hebrew geo-query handling and structured output reliability.
Built on Vertex AI
Vertex AI Agent Builder for orchestration. Vertex AI RAG and Vector Search for retrieval-augmented generation. Multiple tool executions per query chain.
Privacy & Security
Enterprise-grade compliance by design.
Anonymization
Mathematical anonymity. Strict k-anonymity (≥10) protocols ensure zero PII ever enters the reasoning layer.
Data Classification
Vertical-specific ontologies. Tailored classification boundaries for Electronics, Fashion, Beauty, Food, Home, and Pet sectors.
Access Control
3-tier isolation (Public / Licensed / Enterprise) backed by VPC Service Controls, strict cloud IAM boundaries, and zero-trust network policies.
“Crowd Memory” Architecture
Visitor identifiers are ephemeral and cryptographically hashed. All predictions are derived from aggregate cohort behavior within H3 micro-regions, not individual user histories.
Innovation
3 independent + 12 dependent patent-pending claims.
Unlike standard conversion rate, CCR measures only impressions that are both temporally relevant and geographically proximate.
The Pivot
Strategic pause driven by data.
Product-Market Fit
High demand confirmed through 13 enterprise buyer interviews across multi-national multi-location retail markets. Buyers confirmed <50% of decisions are data-driven, a clear market gap.
Big Data Dependency
Reliable spatial intelligence requires large-scale, longitudinal datasets aggregated across multiple regions and retail sectors. Without sufficient client adoption, and the data breadth it generates, outputs risk distribution bias, concept drift, knowledge cutoffs, and regional skew.
Stakeholder Friction
High. Enterprise validation confirmed 3–5 stakeholder approval chains (Buyer, CEO, VP Commerce), extending sales cycles beyond runway constraints.
Outcome
Doubled down on autonomous content generation (Autonomous Pipeline), a frictionless adoption layer that scales the client base and the data aggregation layer simultaneously. Once adoption reaches critical mass, Elysium activates as the next-phase intelligence product: an upsell that transforms the Pipeline's aggregated data into store-level demand decisions.
Published Research
Thought leadership in physical-context AI.
Physical-Context AI for Retail
Guest lecture at Bar Ilan University on how local-intent search signals, geo-coordinates, and foot traffic data converge into store-level retail intelligence.
The Physical-Context Flywheel
Why local-intent search is becoming the new GDP layer of retail.
Epiphany
The AI Personalization Paradox—the invisible crisis killing multi-location retail.
The Marco Economy of Hybrid Retail
How physical and digital retail convergence impacts local economies.