The e-commerce paradox
Everyone loses with the current system
Shoppers
- • 3,000 results, no clear best choice
- • Guess at specs, read 40 reviews
- • Still buy wrong thing
- • 18-25% return rate
- • Frustration + wasted time
Sellers
- • Best products buried on page 3
- • Quality doesn't guarantee visibility
- • Forced to game with keywords
- • Race to bottom on price
- • Pay for promoted listings just to compete
You (Marketplace)
- • 18-25% return rate—your biggest cost
- • $550B+ in returns annually (US alone)
- • Massive environmental impact from returns logistics and waste
- • 60-70% shoppers abandon without buying
- • Gaming undermines trust and quality
- • Low repeat purchase rate
- • Customer lifetime value suffers
Natural-language search → optimal match
Shoppers describe their needs in plain English. Your system returns the provably best products with clear explanations.
Example: "Best value laptop for travel"
From natural language to optimal results
"I need a 14-inch laptop for travel. Best value for money. Lots of browser tabs, Zoom, some coding. Deliver by Nov 1."
System infers baselines:
- 16GB RAM, 512GB SSD (for tabs + dev)
- ≤1.5kg, 8+ hr battery (travel)
- 2× USB-C, Wi-Fi 6E (modern ports)
- Delivery ≤ Nov 1
Preference DAG:
- Level 2: Price buckets (lower wins)
- Level 3: Value-per-dollar tie-breaks
- Level 4: Nice-to-haves (OLED, etc.)
Result shown to shopper:
"We found 2 optimal matches at $799. Both meet your must-haves (RAM, SSD, weight, battery, delivery). Model A has slightly better battery life; Model B has a brighter display. Pick based on your preference—both are equally best."
Reduce returns & environmental impact
Show shoppers products that truly match their needs. Returns drop 15–35% because people get it right the first time—reducing costs, pollution from reverse logistics, and landfill waste.
Boost conversion
Reduce choice paralysis. Shoppers convert 8–22% faster when you confidently present the best options with clear rationale.
Manipulation-proof ranking
Sellers can't game the system by keyword-stuffing or adding irrelevant features. Your shopper's stated preferences always stay on top—quality wins, not manipulation tricks.
Build trust
Every result includes a level-by-level explanation: "We picked this because..." Transparency drives repeat purchases.
Personalized by context
Same query, different profiles: "best value" vs. "premium pick" adjust DAG weights. Each shopper gets their optimal.
Surface hidden gems
Great products from new sellers rise to the top if they truly match needs—no need to pay for promoted placement.
Real impact, real ROI
Return rate reduction
Shoppers find products that truly fit their needs. Fewer "wrong size," "not as described," or "didn't meet expectations" returns.
Lower returns = less reverse logistics, reduced carbon emissions, and decreased landfill waste from damaged or unsellable returns.
Conversion uplift
Confident recommendations reduce choice paralysis. Shoppers add to cart faster when they trust your picks.
Repeat purchase rate
Trust compounds. Shoppers who get it right the first time come back. Higher LTV, lower CAC.