AI Shopping Assistants That
Understand Preferences
Salience-based memory that keeps important purchases fresh while trivial browsing fades. Learn shopping patterns, remember preferences, and personalize recommendations with emotional intelligence.
Make Every Visit Feel Personal
ULPI remembers both what customers say they want AND what they actually choose
Shopping Assistant
Online
Looking for Nike running shoes in darker color
Based on your Nike preference and size 8, I have dark-colored running shoes. I notice you prefer sustainable brands—would you like to see eco-friendly options?
I would like to go for Puma
Sure, checking for Puma running shoes in size 8
Puma Running Shoes
$120
Eco Tote Bag
$18
Olive Utility Jacket
$96
Memories
Prefers Puma brand
Usually chooses darker colors
Size 8
Prefers sustainable/eco-friendly brands
Reads reviews carefully before buying
Replaces running shoes every 6 months
Bought Nike Air Zoom 6 months ago
Olive Utility Jacket
$96
Canvas Mini Tote
$24
Puma Running Shoes
$120
Eco Tote Bag
$18
Why ULPI Wins: Emotional + Episodic Memory
Emotional memory tracks stated preferences (eco-friendly, Puma) AND revealed preferences (always chooses darker colors, free returns over lowest price). Competitors only track clicks—they can't distinguish between what customers say they want vs. what they actually buy.
Intelligent Forgetting
Keep important purchases fresh while letting casual browsing fade
Memory Salience Over Time
Compared 5 brands, chose express shipping, added premium insoles
Why ULPI Wins: Salience-Based Retention
Important purchases and preferences persist with high salience. Casual browsing fades with exponential decay. Recommendations stay relevant—no more 'you viewed this once' spam. Competitors treat all interactions equally, cluttering memory with irrelevant data.
Context-Aware Cart Recovery
Understand WHY customers abandoned their carts and address the actual concern
Abandoned Cart
Nike Air Zoom Running Shoes
Size 10, Black
$180
Premium Insoles
Extra cushioning
$32
Why They Left
Cart abandonment pattern: Customer hesitates when shipping cost exceeds $15
Past behavior: Always chooses free shipping over expedited
Purchase history: Buys running shoes every 6 months, due for replacement
Smart Recovery Solution
"We noticed you left items in your cart. Good news: orders over $200 qualify for free shipping!"
Why ULPI Wins: Multi-Factor Context Retrieval
Multi-factor retrieval connects cart items + browsing behavior + past objections + price sensitivity. We don't just know WHAT was in the cart—we understand WHY they left and HOW to bring them back. Competitors send generic 'complete your order' emails with blanket discounts instead of addressing the actual concern.
Pattern Learning & Timing
Reflective memory identifies shopping frequency, timing patterns, and purchase cycles
Purchase Timeline - Athletic Gear
Nike Air Zoom + Accessories
June 2024$212 • Payday week • Researched 3 days
Compression Socks (3-pack)
December 2024$45 • Payday week • Same-day purchase
Predicted: Running Shoes Replacement
Due: June 2025Based on 6-month replacement cycle + payday pattern
Proactive Outreach Strategy:
- • Send reminder in early June (during payday week)
- • Highlight sustainable running shoe options (matches values)
- • Suggest complementary accessories for impulse add-on
Athletic gear every 5-6 months, always around payday
Researches main items 2-3 days, impulse-buys accessories
Prioritizes free shipping and sustainability over price
Why ULPI Wins: Reflective Pattern Learning
Reflective memory meta-learns from behavior: shops every 6 months, always around payday, researches main items but impulse-buys accessories, prioritizes free shipping. We don't just remember purchases—we learn the underlying patterns that predict future behavior. Generic systems send random promotional emails; ULPI times outreach perfectly.
Build Shopping Experiences That Feel Personal
Stop bombarding customers with irrelevant recommendations. Start building AI that remembers what matters, learns preferences, and times outreach perfectly.