Is it GDPR and CCPA compliant?

Yes. We use anonymized behavioral data and never store unencrypted PII. Our systems are designed for 'Privacy-First' personalization.

Does it work for small catalogs (under 100 SKUs)?

Yes. Even with a small catalog, the *way* you present products to different user segments matters. We focus on 'Segment-Based' personalization for smaller brands.

How long does it take to 'Train' the AI?

We typically need 30 days of historical data to build the initial models. Once live, the AI starts 'Learning' from every click, becoming significantly smarter within 14 days.

Can we override the AI for specific promotions?

Absolutely. You have 'Mercantile Control.' If you want to force-promote a specific SKU for a brand deal, you can easily override the AI recs for that period.

How do you measure the 'Lift'?

We run continuous A/B tests, showing AI recs to 50% of traffic and static recs to the other 50%. We provide transparent dashboards showing exactly how much revenue the AI is generating.

E-commerce Support

Hyper-Personalized AI Recommendations & Search that Predicts Every Purchase

Drive higher average order values and repeat customer purchases using hyper-personalized AI recommendation models, individualized behavioral discovery feeds, and semantic vector search.

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Predicting the Next Purchase

Generic recommendation widgets fail to satisfy buyers who expect dynamic, personalized digital journeys. In the age of TikTok and Netflix, storefronts must adapt to individual shopper intent and historical behavior in real time. We build recommendation systems that analyze visitor navigation patterns and contextual cues like weather or trending events to deliver personalized homepage feeds. By deploying semantic vector search models and custom upselling logic, we present relevant alternatives at high-intent touchpoints, driving major lifts in average order value and customer lifetime value.

The Old Way

Static Stores

The Intelegencia Way

Predictive Stores

Random 'Suggested' items

Individualized Product Feeds

Ignored 'You May Like' widgets

High-Intent Recommendations

One-size-fits-all homepage

Personalized Homepage Logic

Irrelevant search results

Semantic-Aware Search

Low cross-sell conversion

High-Yield Upsell Engine

Individualized Behavioral Feeds

We transform your homepage into a personalized 'Discovery Feed' for every returning user. By analyzing past purchases, browsing patterns, and cart-abandonment data, we show the products most likely to trigger an immediate 'Add-to-Cart'. This isn't about stalker-like tracking; it's about reducing 'Choice Overload.' We help your customers find what they love faster, creating a 'Frictionless Path' to their next purchase.

Dynamic User-Profile Mapping

Real-Time Intent Detection

Personalized Homepage Widgets

Bespoke 'For You' Categories

Returning-User Affinity Scoring

Semantic Search Engineering

Most site-searches are 'Keyword-First.' We move you to 'Meaning-First.' If a user searches for 'Warm Beach Wear,' our AI understands the context and shows linen shirts and sun hats, even if the exact words 'Beach' aren't in the product title. We eliminate 'No Results' pages by using vector-search technology that understands the *vibe* and *use-case* of your products. We turn your search bar into your highest-converting salesperson.

Vector-Based Semantic Search

Contextual Intent Mapping

Visual-Search Integration

Synonym & Typo-Tolerance

Zero-Result Recovery Logic

High-Yield Upsell Orchestration

We engineer the AI logic that powers your 'Complete the Look' or 'Bundle and Save' recommendations. We go beyond basic SKU mapping to analyze color compatibility, technical synergy, and price-point logic. We ensure that an upsell is never a 'Random Guess.' It is a statistically-backed suggestion designed to increase the total basket value without increasing the customer's perceived effort. We drive AOV through genuine helpfulness.

Contextual Bundle Logic

Synergy-Based Upselling

Price-Tier Sensitive Recs

Cart-Aware Cross-Sells

AOV-Optimization Loops

The Intelligence Stack

AI Capabilities

We provide specialized AI models to turn your data into a predictive sales engine.

Visual Recognition

Tagging products based on visual attributes for better search.

Sentiment Triage

Identifying unhappy customers in reviews to suppress recs to them.

Next-Purchase ML

Predicting when a customer is likely to need a refill or upgrade.

Dynamic Pricing

Optimizing prices in real-time based on demand and competitor stock.

Recursive Model Tuning

AI models decay if not maintained. Our team of data engineers performs weekly 'Relevance Audits', tuning the recommendation algorithms based on actual purchase outcomes and customer feedback. If the AI is recommending winter coats in July, we fix the seasonal logic. If it's missing a new trend, we inject the trend data. We ensure your AI remains 'Human-Smart' and perfectly aligned with your current business goals.

Weekly Relevance Audits

A/B Recommendation Testing

Seasonal Logic Injection

Trend-Aware Model Training

Outcome-Based Reward Tuning

The Path to Prediction

Our AI Roadmap

We implement AI recommendations through a structured data-science and integration phase.

Data Ingestion

Connecting to your Google Analytics and CRM to build user profiles.

Model Training

Teaching the AI your catalog's unique 'Relationship Logic'.

Widget Integration

Placing the 'Smart Widgets' across your Homepage, PDP, and Cart.

A/B Pilot

Testing the AI recs against your current static rules.

Infinite Tuning

Weekly model updates to keep recommendations fresh and high-converting.

Measured Performance. Proven Growth.

AOV Lift

Rec. Click-Rate

Search Conversion

Retention Rate

Frequently Asked Questions
About Hyper-Personalized AI Recommendations & Search

Here you will find answers to questions we get asked the most about our offerings.

No. *We* are your data-science team. We provide the expertise and the infrastructure to run these models so your internal team can focus on marketing and brand.