The Challenge with In-House: Building a production-grade recommendation engine requires expertise in machine learning, real-time data processing, and handling millions of calculations per second. Most teams underestimate the complexity: training models, A/B testing algorithms, tuning for latency, managing data pipelines, monitoring performance. You’re also constantly maintaining it as traffic grows and business rules evolve.

The Problem with Embedded Solutions: If your commerce platform (Shopify, BigCommerce, SAP) includes recommendations, they’re tightly coupled to that platform. You can’t use them on your mobile app, email, or other channels without duplicating logic. When you redesign your storefront, recommendation code gets tangled with presentation code. Algorithm changes require coordination across teams.

Our Approach: We’ve already solved the hard problems – we run recommendations as a dedicated service with specialized infrastructure. You get production-grade algorithms, real-time processing, and omnichannel support without maintaining ML infrastructure. More importantly, your recommendations stay independent from your storefront. Rebrand your website, launch a new app, run a campaign—the recommendation engine works everywhere without changes.

The Real Difference: In-house is expensive and slow. Platform recommendations are limited. We’re specialized, maintained, and channel-agnostic.

Most customers integrate in 2–4 weeks and go live with results in the first month.

Here’s What’s Involved:

Phase 1: Data Onboarding (1 week)

• We connect to your commerce platform (Shopify, custom API, whatever you use) to pull product catalogs, pricing, and inventory.
• You point us to your existing behavior data sources: web analytics (Google Analytics, custom events), app events, email platforms, etc. We set up secure pipelines to ingest this data.
• If you have historical customer behavior and transaction data, we can backfill the recommendation engine to warm-start with initial training data.

Phase 2: Frontend Integration (1–2 weeks)

• Your engineering team adds ~5 lines of code per recommendation placement in your storefront. Instead of static product lists, you call our API: GET https://restapiv3.quarticon.com/restapi/{customerSymbol}/recommendation/{placementId}
• No major refactoring. You’re just replacing hard-coded product lists with API calls.

Phase 3: Testing & Launch (1 week)

• We help you set up A/B tests to measure impact. We also help configure business rules (exclude certain products, boost brands, respect inventory constraints).
• You go live gradually: start with one recommendation block, measure results, roll out to others.

Real Example: A mid-market retailer with React storefront + mobile app + Klaviyo email integrated in 30 days.

The Key: You’re not replacing infrastructure. You’re adding a new microservice and pointing your frontends at it.

Your data is yours. Period. We don’t sell it, share it with advertisers, or use it to train models for other customers. Everything stays in your dedicated instance – separate databases, separate compute, no cross-customer data leakage.

What Data We Collect:

• Product catalog data: SKU, title, description, price, category (from your commerce platform)
• Behavior events: Views, clicks, purchases, cart adds, searches (from your storefront and app)
• Customer identifiers: User IDs, email addresses – pseudo anonymized (for matching behavior across channels)
• Inventory levels: Current stock counts (from your commerce platform)

We do NOT collect: personally identifiable information beyond what you send us, credit card data, passwords, or anything else outside the scope of recommendations.

Security & Compliance:

• Encryption: All data encrypted in transit (TLS 1.2+) and at rest (AES-256).
• GDPR compliant: Users can request data deletion; we remove all traces within 30 days. You control consent workflows.
• CCPA compliant: California residents can opt out of data sales (we don’t do this anyway, but we support the legal workflows).
• Isolated infrastructure: Your data lives in dedicated databases. No shared tenancy risks.

You have full audit logs in your dashboard: who accessed what data, when recommendations were served, performance metrics. Export to your security/compliance team anytime.

Enterprise customers can request third-party audits of our systems at any time.

Not necessarily – they solve complementary problems. A recommendation engine usually addresses on-site conversion and real-time product selection, especially for anonymous traffic. In addition to this, Quarticon’s recommendation engine enables cross-channel personalization.

A CDP/MA manages customer identity, cross-channel activation, and CRM workflows useful for retention and lifecycle programs.

Many businesses benefit most by running both: the recommendation engine to maximize immediate on-site revenue across all traffic, off-site personalization, and a simple MA tool just for off-site outreach (without personalization, which is to be delivered by Quarticon).

Use our dashboard to see real-time metrics: recommendation impressions, clicks, conversions, revenue attributed to recommendations.

Our Rules Engine Supports Unlimited Custom Logic.

You’re not stuck with a one-size-fits-all algorithm. Layer business logic on top of recommendations:

Seasonal/Promotional Rules

• “During Q4 holidays, boost gift sets in recommendations by 50%”
• “If a product is on clearance, surface it to customers who’ve viewed similar items”
• “Don’t recommend Black Friday deals to customers who already purchased”

Category & Brand Exclusions

• “Don’t recommend competitor brands to B2B customers”
• “Exclude third-party sellers from recommendations (show only our inventory)”
• “Never show adult products to users under 18”

Price-Based Rules

• “For price-sensitive customers (discount seekers), recommend products under $50”
• “For high-value customers, recommend premium products first”

Inventory Constraints

• “Don’t recommend products with less than 2 units in stock”
• “For oversized items, limit recommendations to customers with free shipping”
• “Prioritize slow-moving inventory; boost older SKUs”

Compliance & Privacy

• “Honor customer preferences: if they opt out of category X, don’t recommend it”
• “For EU customers, apply GDPR rules; for California customers, apply CCPA rules”

You define rules in our dashboard (no coding required for simple cases). Complex logic uses a rule engine syntax that’s human-readable. Rules update in real-time—no retraining, no delays.

Multi-Variant Rules: Different customer segments get different rules. VIP customers see different recommendations than one-time buyers. Seasonal rules activate automatically.

A/B Test Rules: Test two different rule sets: show 50% of users rules version A, 50% version B. Measure which generates more revenue.

Performance: Rules execute in microseconds—they don’t add latency to recommendation requests.