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SOLUTION
E-COMMERCE AI SEARCH
Search for e-commerce with dynamic assortment and real-time stock — grocery, q-commerce, marketplaces. Reduce empty results and increase conversion through inventory-aware ranking
RESULTS WITH YANGO TECH
+12%
GMV Uplift
<1%
Empty results
+15%
Retention
2–4 wks
To Go Live
PURPOSE-BUILT
Grocery &
Q-Commerce
- Online supermarkets with 10,000–100,000 SKUs
- Express / dark store delivery platforms
- High-frequency stock updates (minutes, not days)
- Diet-aware and occasion-based queries
- Locality-specific assortments per warehouse
Marketplaces
- Multi-seller catalogs: 100K–10M+ SKUs
- Seller-driven inventory and pricing volatility
- Cross-category and long-tail query handling
- Seller quality signals in ranking
- Sponsored product blending without relevance loss
Online Retail
- Pharma, electronics, home goods, sports
- Image-based search and recommendations
- Seasonal assortment swings and clearance handling
- Cross-sell and bundle-aware results
- Multi-language & regional catalog support
Built on real grocery and q-commerce systems with high-frequency assortment updates, not adapted from enterprise search or content platforms. AI Search’s ranking models are trained on real retail signals: stock levels, sell-through rates, geolocation, and conversion history. The solutions focuses exclusively on verticals where assortment changes by the hour.
Search must reflect what is actually available right now
Search must reflect what is actually available right now
QUERY › UNDERSTANDING › RESULTS
See how the system interprets real shopper queries, from simple product lookups to complex intent-driven and conversational searches. Live product demo available
«lactose-free milk»
UNDERSTANDS
Lexical match on «milk» + semantic expansion to «lactose-free», «lactose intolerant», dairy-free alternatives. Attribute filter applied automatically.
RESULTS
In-stock lactose-free SKUs first, ranked by conversion rate. Dairy alternatives surfaced as secondary results.
LEXICAL
ATTRIBUTE FILTER
INVENTORY-AWARE
«healthy snacks for kids»
UNDERSTANDS
LLM classifies as intent query (not a product name). Decomposes into: low-sugar, child-appropriate, snack category. No-added-sugar attribute applied.
RESULTS
Fruit bars, rice cakes, yogurt pouches — ranked by age-appropriate signals and in-stock status. Empty result rate: 0%.
LLM INTENT
QUERY REWRITE
SEMANTIC
«what to cook for dinner»
UNDERSTANDS
LLM agent classifies as conversational / recipe query. Routes to multi-step resolution: identifies meal categories, maps to ingredient clusters available in current stock.
RESULTS
Meal suggestions with grouped ingredient sets — pasta, rice dishes, stir-fry — all filtered to available inventory. LLM generates guided response, not a raw product list.
CONVERSATIONAL
LLM AGENT
GUIDED RESPONSE
«cheap protein powder no artificial»
UNDERSTANDS
Multi-attribute decomposition: protein powder (category) + price-sort + no artificial ingredients (attribute filter). Semantic ranking used for attribute matching.
RESULTS
Clean-label protein SKUs sorted by price ascending. Out-of-stock items suppressed. Alternative brands included if primary brand unavailable.
MULTI-ATTRIBUTE
PRICE-SORT
INVENTORY FILTER
«something for a birthday party»
UNDERSTANDS
LLM agent identifies occasion query. Maps to clusters: cakes, candles, snacks, drinks, decorations. Pulls from current stock per cluster.
RESULTS
Organized result set by occasion category, with "Add all to cart" groupings. Guided conversational response explaining categories chosen.
OCCASION INTENT
LLM AGENT
LLM AGENT
Cities using our platform grow fare
revenue by up to 30%
BM25 — The Reliable Foundation
Classic term-based retrieval handles exact product names, SKU lookups, brand searches, and misspellings. Acts as the robust fallback when neural models have sparse data for a query type. Always fast, always available
BERT Neural Embeddings — Intent Understanding
Vector-based retrieval with BERT embeddings understands meaning, not just tokens. "Lactose-free milk" finds dairy alternatives even if the exact phrase isn't in product titles. Handles cross-language queries and attribute-rich searches
LLM — Conversational Layer Only
The LLM is used selectively for complex, multi-component, and conversational queries. It classifies query type, rewrites queries into structured sub-searches, and generates guided responses for occasion or recipe intents. It is NOT used for primary retrieval — that keeps latency low and results deterministic
Dynamic Ranking — Where Conversion Happens
Candidate results from lexical and semantic layers are re-ranked using real-time signals: stock availability, location-specific assortment, historical conversion rates, sell-through velocity, and promotional rules. Out-of-stock items are suppressed. Sponsored items are blended without harming relevance
1
User Query | Input
2
Lexical Search | BM25
3
Semantic Search | BERT Embeddings
4
LLM Agent | Complex Queries Only
5
Ranking | Inventory · Conversion
6
Results | In-Stock · Relevant
TECH STACK
RETRIEVAL
Combines keyword matching with AI embeddings for comprehensive product discovery
HYBRID SEARCH TECHNOLOGY
LLM LAYER
Interprets complex queries and generates guided shopping responses
NATURAL LANGUAGE PROCESSING
RANKING
Sorts results by real-time stock, conversion rates, and location data
INVENTORY-AWARE RANKING
INTEGRATION
Plug-and-play connection to any e-commerce platform or catalog system
E-COMMERCE API INTEGRATION
A/B TESTING
Run parallel search experiments to measure performance before full rollout
TRAFFIC OPTIMIZATION TOOLS
ANALYTICS
Monitor query trends, conversion metrics, and catalog gaps in real time
SEARCH INTELLIGENCE DASHBOARD
CASES
Grocery Retail
Yango Tech eliminates "out-of-stock" frustration by syncing the search bar with real-time, store-specific inventory. The AI suppresses unavailable items and suggests intelligent substitutes for semantic queries like "quick breakfast," ensuring customers only see what is actually on their local shelves
Multi-Category Marketplace
To solve catalog noise, Yango Tech uses session-based personalization to adapt search results to a user’s current intent instantly. It distinguishes between conflicting categories — like "monitor" for gaming versus baby safety — while balancing relevance with business KPIs like profit margins and shipping efficiency
Q-Commerce / Dark Store
Yango Tech bridges the gap between storefronts and warehouses by linking search directly to the Warehouse Management System. It automatically down-ranks bulky items during peak delivery times, reducing order cancellations and ensuring the displayed products align with real-time courier capacity and picker availability
WHY YANGO TECH
Inventory-Native Architecture
Stock levels, availability per warehouse, and sell-through velocity are first-class signals — not an afterthought filter layer added on top of a generic index
High-Frequency Assortment Ready
Designed for catalogs that change 40–60 times per day. Real-time stock integration with <90 second latency. Built on the same infrastructure as large-scale grocery platforms
Vertical-Specific Ranking Models
Ranking models trained on real retail signals — not repurposed document search or enterprise content retrieval. Grocery intent patterns, marketplace seller signals, and q-commerce session behavior all modeled natively
INTEGRATION Q&A
2–4 weeks from kickoff to live A/B test in production.
- Phase 1 (sandbox + catalog import): 1 week.
- Phase 2 (API integration + stock feed + A/B): 1–2 weeks.
- Phase 3 (go-live decision): 1 week.
Timeline depends on IT availability and catalog complexity.
Three inputs:
- (1) Product catalog — name, category, SKU, description (if available).
- (2) Stock availability feed — real-time or near-real-time per location / warehouse.
- (3) Query logs — historical search queries for training relevance signals (optional but accelerates tuning).
Yes — this is the default approach. We run detailed calculations over both traffics — on Smart Search and your existing search. Both run simultaneously. You see direct metric comparison before committing to full rollout.
No. Integration is entirely API-based. Your app or website sends search queries to the Yango API endpoint and receives ranked results. No changes to catalog structure, CMS, or storefront architecture required.
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