EPE Intent Graph
Structures declared intention, including tensions such as authority without severity, visibility without loudness, and protection without bulk.
®EPE / Emotional Prompt EngineLabNoir / Retail technology
Turn how customers want to feel into what they can actually buy.
EPE, LabNoir's Emotional Prompt Engine, translates declared intention and practical needs into eligible products, complete looks, controlled refinements, and merchant intelligence.
Declared intention | Commercial constraints | Persistent refinement | Merchant intelligence
Demonstration interpretation / 0042
“Powerful but not corporate. Refined, comfortable, and slightly unexpected.”
- Presence
- Controlled authority
- Structure
- Defined, not rigid
- Edit action
- Relaxed tailoring + one directional accent
Structured demonstration catalogRule-governed orchestrationExplainable retail signals
The customer problem
Fashion discovery asks customers to describe a product before they have described the outcome.
Search, recommendations, and AI shopping tools can help customers locate products. EPE adds a different layer.
It structures the outcome the customer wants, applies the retailer's commercial rules, preserves that intention through refinement, and records what the interaction reveals.
How EPE works
One explainable intelligence layer from declaration to commercial action.
- 01
Declare
The customer describes the occasion, desired feeling, and practical realities in their own language.
- 02
Interpret
EPE converts the statement into an editable intent profile, including emotional tensions, design direction, and practical requirements.
- 03
Resolve
EPE applies hard commercial constraints before emotional ranking, then coordinates and explains eligible products or approved product configurations.
- 04
Learn
Acceptance, rejection, locking, replacement, refinement, and constraint signals become actionable retail intelligence.
EPE Core
The intelligence system beneath every EPE application.
LabNoir LLC develops and owns EPE. One shared intelligence system powers Commerce and Intelligence.
Constraint Ledger
Records which commercial requirements every recommendation satisfies or fails, including department, category, size, inventory, delivery, material, coverage, and budget.
Look State Engine
Preserves the logic of a complete look through multiple refinements. Keep one piece, replace another, lower the total, and retain everything that still works.
Fashion Reasoning Benchmark
Tests EPE against structured fashion missions, hard constraints, multi-step refinements, and no-result conditions before customer release.
Integration Layer
Connects EPE with existing catalog, inventory, search, sizing, clienteling, cart, and checkout systems.
EPE demonstration / Constraint-aware refinement
See what EPE protects when the customer changes direction.
This rule-governed demonstration uses synthetic product and delivery records. It does not represent live retailer inventory or a verified purchase.
“Wedding guest. Elegant and visible, but not romantic or overly formal. No white. Dress size US 8. Low-profile sneakers EU 39. Under $1,500. Needed within 24 hours.”
Initial Complete LookSynthetic product demonstration
$1,010Total price
Common StateOlive / Washed cotton
Dress / Structured demonstration catalog
Soft Utility Dress
The garment carries elegant visibility through proportion and movement without white, romantic ornament, or rigid evening formality.
Synthetic record meets the 24-hour rule; live retailer confirmation requiredCommon StateStone / Recycled technical knit
Footwear / Structured demonstration catalog
Transit Minimal Sneaker
A quiet, low-profile sneaker with genuine unisex product sizing, low-contrast branding, and all-day walking support. It satisfies the explicit sneaker, size, walking, palette, and occasion gates before emotional ranking.
Synthetic record meets the 24-hour rule; live retailer confirmation requiredCustomer refinement
“Keep the dress. Make the sneakers quieter and less athletic. Remove the bag. Preserve delivery within 24 hours.”
The EPE product suite
Two applications. One shared intelligence system.
Morrow IndexWarm grey / Cashmere blend
Multi-brand product discoveryEPE Commerce
Intent-led product and complete-look orchestration.
- Hard commercial constraints applied before emotional ranking
- Complete looks with category-specific sizing
- Lock, replace, and refine without starting over
Illustrative pilot dashboard.Sample demonstration data, not verified commercial performance.
IntentJourney signalsGapsAssortment blockerspowerful + comfortableEPE Intelligence
Declared intention and refinement signals for retail teams.
- Product acceptance, rejection, locking, and replacement
- Size, budget, delivery, and assortment gaps
- Merchandising direction from unmet demand
Connected product system
From customer intention to product opportunity.
EPE Commerce identifies what customers seek. EPE Intelligence reveals where the assortment succeeds or fails and turns those gaps into merchant action.
- 01Intent
- 02Discovery
- 03Gap
- 04Intelligence
- 05Action
- 06Commerce
Retailer benefits
Make discovery more human and merchandising more informed.
Eligibility before emotional ranking
EPE first verifies department, category, size, inventory, delivery, materials, coverage, and budget. Emotional relevance ranks only the products that remain eligible.
Pilot measuresProduct recommendation acceptanceTime to relevant product
Explainable recommendations
Make every product ranking legible to customers, stylists, merchants, and product teams.
Pilot measuresRefinement behaviorProduct recommendation acceptance
Stronger complete looks
Coordinate products around one emotional intention while respecting size, price, and delivery.
Pilot measuresAdd-to-look rateSaved-look rate
New demand signals
See where customer intentions are underserved by the current assortment.
Pilot measuresCatalog gaps identifiedRefinement behavior
Integration concept
Designed to add intelligence without replacing the systems that already work.
EPE sits above or alongside existing retail infrastructure as an interpretation, orchestration, and intelligence layer.
InputsProduct catalogVariantsInventoryPricingSize availabilityDeliveryCustomer-declared contextMerchandising rulesExisting search or recommendation outputs
→EPE CoreInterpretApply constraints · Rank · Coordinate · Preserve state · Explain · Learn
→OutputsEligible product editsComplete looksControlled refinementsExplainable decisionsCheckout-ready selectionsUnmet-intent signalsMerchant intelligence
Pilot structure
A focused proof of relevance using one real assortment.
Map
One product category or defined assortment.
Calibrate
Retailer-specific product attributes, emotional vocabulary, constraints, and success measures.
Validate
Fashion Reasoning Benchmark, constraint, urgent-delivery, lock, replacement, no-result, and checkout-handoff testing.
Run
Limited audience, recommendation journey, refinement, and saved-look signals.
Evaluate
Relevance, engagement, purchase-intent actions, customer response, catalog gaps, and next integration scope.
Indicative pilot structure. Final scope depends on assortment, traffic, integration, and measurement requirements.
Request a demonstration
See EPE against the decisions your customers and teams already make.
We will frame the conversation around one assortment, one customer journey, and one measurable retail opportunity.