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.
The Enterprise Problem
Validated through discovery sessions with enterprise retail stakeholders at multi-location retailers operating 15,600+ combined store locations.
Enterprise Discovery Reach
Direct product discovery sessions conducted with retail leadership and decision-makers at Fortune-500 multi-location retailers across grocery, wholesale, pharmacy, and general merchandise.
13 Buyer Interviews
Validated across Fashion, Sporting Goods, Home & Living, and Jewelry verticals with enterprise retail buyers.
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
A conversational multi-agent system. Ask a question in natural language—11 specialized agents collaborate to deliver store-level intelligence.
Conversational Interface
A single orchestrator receives natural language queries in Hebrew or English, classifies intent, and dispatches work to the right combination of expert agents—no manual configuration required.
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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-001Stable baseline
- Gemini 1.0 ProEvaluated · Not 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. All agents operate within Google Cloud’s enterprise security boundary.
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 strict cloud IAM boundaries and zero-trust network policies.
“Crowd Memory” Architecture
No persistent user profiles. Visitor identifiers are ephemeral and cryptographically hashed. All predictions are derived from aggregate cohort behavior within H3 micro-regions, not individual user histories. The system explicitly avoids persistent user profiling—using “Crowd Memory” instead of “User Memory.”
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 efficiency of physical-context matching.
Independent Claims (Patent Scope)
| Claim | Scope |
|---|---|
| 1. Method Claim | End-to-end process: ingest physical-context signals → transform via sub-agents → compute Demand Index → trigger actions → generate structured output → publish → feedback loop. |
| 2. System Claim | Physical architecture comprising 7 subsystems (ingestion, transformation, computation, trigger, generation, publication, feedback) with closed-loop learning. |
| 3. Medium Claim | Computer-readable medium storing instructions that, when executed, perform the method of Claim 1. |
Key Innovations (Dependent Claims)
| Innovation | Description |
|---|---|
| H3 Geospatial Indexing | O(1) spatial lookups mapping consumer intent to precise retail coordinates via Uber’s H3 hexagonal grid. |
| Grammar-Constrained Decoding | Enforcing strict structural compliance on LLM outputs via context-free grammars to prevent supply chain execution errors. |
| Z-Score Trigger Gating | Statistical thresholding that halts redundant inference when signals fall within normal deviation, reducing compute overhead. |
| Variant-Level Intent (VSIS) | Isolating demand signals down to the specific product variant (size, color, gender) within a geographic radius. |
| Zero-Result Signal Analysis (ZRFAS) | Inverting failed retail searches into predictive signals for unmet local demand. |
| Demand Index (DI) | Composite score combining search velocity, geo-concentration, and temporal signals into a single actionable metric per SKU per location. |
Implementation
Vertex AI Agent Builder in action.
What Buyers Told Us
13 enterprise interviews 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.
Validation Outcome
Enterprise buyers across all four verticals confirmed strong demand for store-level, variant-specific intelligence—a capability none of their existing BI systems provide. However, the 3–5 stakeholder approval chain per retailer extends sales cycles well beyond early-stage runway constraints. This structural friction—not product skepticism—directly informed the strategic pivot to Autonomous Pipeline.
The Decision
Strategic pause driven by data.
Product-Market Fit
Validated. 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. Elysium’s value scales with adoption, not in isolation.
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 surface 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.
Addressable Market: Retail Location Intelligence ($2.8B → $10.9B by 2033) · AdTech ($217B) · MarTech ($6B, 30%+ CAGR) · Loyalty Systems ($12.95B, 18% CAGR)
Published Research
Thought leadership in physical-context AI.
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.