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Rivac AI Labs
Vertical AI Solutions: Fintech, Retail, Real Estate & Manufacturing

Product Recommendation & Personalization Engines

Generic "customers also bought" widgets leave revenue on the table because they ignore what actually drives your specific shoppers, browsing behavior, purchase history, inventory position, and margin. This service builds a recommendation engine trained on your own catalog and customer data, surfacing the products most likely to convert for each shopper at each point in their journey, from homepage to cart to post-purchase email. It's for retail and e-commerce teams who've outgrown their platform's built-in recommendations and need lift a generic plugin can't deliver. We test the engine against a control group before it touches your full traffic, so the revenue impact is proven, not assumed. The result is a measurable increase in average order value and conversion, tied to a number you can defend to finance.

This is a capability we deliver for you as a service, not a role we’re hiring for.

How We’d Approach This

A clear, staged plan, not a black box

These are the service-specific steps inside our six-step process.

  1. 1

    Diagnose current recommendation performance and identify where generic or rule-based suggestions are underperforming.

  2. 2

    Pilot the model against a held-out segment of traffic, A/B tested against your current recommendations.

  3. 3

    Review lift in average order value and conversion with merchandising before rolling out to full traffic.

  4. 4

    Launch across your storefront and email channels, with ongoing retraining as catalog and behavior shift.

What You Get

Deliverables from this engagement

  • Trained recommendation model integrated into storefront and email
  • A/B test results quantifying lift in AOV and conversion
  • Merchandising controls for boosting or excluding specific SKUs
  • Real-time personalization API for any customer touchpoint
  • Ongoing retraining schedule as catalog and behavior change

Six Ways We Could Architect This

Different engagement, different build, pick the shape that fits

There’s more than one way to deliver on this service. Browse a few of the ways we’d structure the work, depending on your speed, budget, and integration needs.

Read all 6 architectures as text

Lean Fast-Start Architecture

Data Input

  • Product Catalog Export
  • Customer Purchase History

Simple Model

  • Baseline Recommendation Model
  • Top Product Suggestions

Testing

  • Held Out Traffic Segment
  • Manual Conversion Comparison

Rollout

  • Small Traffic Pilot
  • Merchandising Review
  • Basic Homepage Widget
  • Cart Page Widget Added
  • Email Recommendation Block
  • Performance Notes Documented

Cloud-Native / Managed Services Architecture

Data Layer

  • Clickstream Events
  • Purchase History Store
  • Cloud Data Warehouse

Managed ML Platform

  • Managed Feature Store
  • Managed Recommendation Training Service
  • Model Registry

Serving Layer

  • Real Time Recommendation API
  • Personalization Cache

Operations Layer

  • Storefront Widget Integration
  • Email Platform Integration
  • AB Test Monitoring Dashboard
  • Scheduled Retraining Pipeline

Event-Driven & Automation-First Architecture

Triggers

  • Customer Views Product
  • Behavior Event Stream
  • Customer Completes Purchase

Automation Layer

  • Auto Feature Update
  • Auto Recommendation Refresh
  • Auto Cache Update

Delivery Gate

  • Merchandising Rule Applies
  • Apply Boost or Exclude Rule
  • Default Ranked List

Delivery

  • Rendered on Storefront
  • Included in Next Email Send
  • Conversion Event Logged

Enterprise-Grade & Compliance-Ready Architecture

Governance Layer

  • Customer Data Privacy Policy
  • Consent Management Rules
  • Merchandising Governance Policy

Secure Model Operations

  • Access Controlled Feature Store
  • Role Based Merchandising Controls
  • Encrypted Customer Data

Auditable Model

  • Model Version and Training Data Log
  • Fairness and Bias Check Record
  • Recommendation Decision Log

Compliance Operations

  • Redundant Serving Infrastructure
  • Data Retention Policy Enforcement
  • Signed Off Privacy Review

AI-Augmented, Human-in-the-Loop Architecture

Input Layer

  • Customer Behavior Signals
  • Catalog and Inventory Data

AI Recommendation Layer

  • AI Personalization Model
  • Draft Ranked Recommendations
  • Draft Merchandising Adjustments

Human in the Loop

  • Merchandising Team Review
  • Aligned with Brand and Margin Goals
  • Adjust Boost and Exclude Rules
  • Human Approved Recommendation Set

Delivery

  • Published to Storefront and Email
  • Conversion and AOV Tracked
  • Monthly Lift Report

Integration-Heavy / Ecosystem-Connected Architecture

Client Systems

  • E-commerce Platform
  • Inventory Management System
  • Email Marketing Platform

Recommendation Core

  • Personalization Engine
  • Unified Customer Profile

Two Way Sync Layer

  • CRM Customer Data Sync
  • Analytics Warehouse Sync
  • Marketing Team
  • Merchandising Team

Shared Governance

  • Joint Review with Merchandising Leadership
  • Published Personalization Architecture
  • Ongoing AOV and Conversion Review

More in Vertical AI Solutions: Fintech, Retail, Real Estate & Manufacturing

Ready to get started?

Tell us what you’re trying to get done and we’ll help you find the highest-leverage place to start, scoped small enough to prove itself before you commit to anything bigger.

Talk to us about Product Recommendation & Personalization Engines

Most engagements like this start as a $500–$2,500 pilot, see full pricing.

Questions? Book a free call