Collaborative Filtering
Learn from user behavior patterns to recommend items similar users enjoyed.
AI-powered personalization · built to convert
Recommendation engine development that earns the click.
We build AI recommendation engines for fashion, apparel and wider retail — personalized product discovery that increases engagement and drives measurable revenue growth, integrated into the storefront you already run.
Click-Through Rate2-5% → 3.5-8%
Conversion Rate1-3% → 2-5%
Average Order Value$50 → $75-85
Customer Retention60% → 75-85%
TRUSTED BY GROWING BUSINESSES
What we build
Recommendation
capabilities.
Comprehensive recommendation engine development for e-commerce, fashion, media, and SaaS platforms.
Content-Based Recommendations
Match users with products based on item attributes and user preferences.
Hybrid Recommendation Systems
Combine multiple algorithms for superior accuracy and diverse recommendations.
Real-Time Personalization
Dynamic recommendations that evolve instantly based on current user actions.
A/B Testing & Optimization
Continuously improve recommendation accuracy and conversion rates.
Cold Start Problem Solutions
Intelligent recommendations for new users and new products.
Under the hood
Advanced algorithms,
real infrastructure.
Chosen for your data density and traffic, not for the pitch deck.
Algorithms
Infrastructure
A disciplined route to value
From data audit to
continuous optimization.
Six stages, from your first data audit to a continuously improving production system.
01
Data Audit & Strategy
Assess your existing data quality and define the personalization strategy that fits your catalog.
02
Algorithm Selection
Choose collaborative, content-based, or hybrid models suited to your data density.
03
Model Development
Build and train the recommendation model against your real product and behavior data.
04
A/B Testing
Validate lift in CTR, conversion, and AOV against your existing experience before full rollout.
05
Deployment
Ship to production with real-time serving infrastructure and monitoring.
06
Continuous Optimization
Retrain and tune models as user behavior and catalog evolve.
Vertical focus
Fashion and apparel recommendation engines.
AI fashion recommendation engines have to solve a problem that generic retail engines do not. Fashion is the hardest category to recommend in, and the reasons are structural rather than technical. A meaningful share of an apparel catalog did not exist last season and will be gone by the next, so items rarely accumulate enough interaction history to be confidently placed. Taste is not quality — a garment one shopper loves is one another would never wear — which makes popularity a far weaker signal here than in books or film. And intent shifts within a single session: the same person shops for work clothes on Monday and a wedding outfit on Saturday.
That combination is why generic recommendation platforms underperform on apparel and why the engines we build for fashion retailers lean on catalog attributes — cut, color, fabric, price band — alongside behavioral signals, rather than behavior alone. Attributes describe a garment from the moment it is created, so a new arrival can be positioned without waiting for anyone to interact with it.
We built exactly this for a fashion retailer, integrated into their existing platform rather than replacing any part of it. Read the case study ↗
Where it works
Perfect for every
industry's catalog.
Tuned to how each industry's catalog and users actually behave.
| E-Commerce | 40-60% higher CTR, 25-40% conversion increase |
| Streaming & Media | Reduce churn by 20-30%, increase watch time |
| SaaS | Boost feature adoption, improve retention |
| News & Content | Increase engagement and time on site |
| Fashion & Retail | Personalized style recommendations |
| Music & Audio | Discovery-driven engagement |
Client success stories
Representative
results, not outliers.
A sample of recent recommendation engine development engagements.
Online Fashion Retailer1.8% conversion→3.9% conversionHybrid recommendation model personalized to browsing and purchase history.
Streaming Platform22% monthly churn→15% monthly churnContent-based recommendations tuned to watch history and session context.
B2B Marketplace$62 AOV→$94 AOVCross-sell recommendations surfaced at checkout using collaborative filtering.
Cost
What drives the cost of
recommendation engine development.
A recommendation engine can start small and grow. These factors set the cost of the first phase, which we quote as a fixed price after reviewing your data.
Catalog size and data
The number of products and how much browsing and purchase history you already collect decide which methods will work and how much preparation is needed.
Where recommendations appear
One placement, such as product pages, is the simplest start. Home page, cart, email and app placements each add work.
Your store platform
Shopify and WooCommerce integrate quickly through their APIs. Custom platforms may need more integration work.
Real-time or batch
Recommendations refreshed nightly are simpler and cheaper to run than ones that react instantly to what a shopper is doing.
Common questions
Recommendation engine development FAQs
Short answers to the questions we are usually asked before an engagement begins.
Recommendation engines increase conversion by 25-40% by personalizing product discovery, showing users relevant items they're likely to purchase, and reducing decision fatigue.
Collaborative filtering, content-based filtering, hybrid models, and deep learning networks are used to deliver accurate, personalized recommendations.
The cold start problem occurs when there's not enough data on a new user or product to personalize recommendations. We solve it with content-based fallbacks and popularity-weighted defaults until behavioral data accumulates.
Collaborative filtering typically needs a few weeks of interaction data at moderate traffic; content-based and hybrid models can perform well from day one using product attributes alone.
A production-ready recommendation engine typically takes 6-12 weeks, including data audit, model development, A/B testing, and deployment.
It depends on your catalog size, the data you already collect and where recommendations appear. Starting with one placement, such as product pages, keeps the first phase small. We quote after reviewing your data.
Yes. We integrate with Shopify, WooCommerce, Magento and custom stores, using their APIs or a lightweight script, without rebuilding your store.
Yes. Recommendations are built from behavior data you already hold, stored in your own environment and processed in line with your privacy policy and applicable laws such as GDPR.
You do. Source code, prompts, evaluation sets, trained model weights and documentation are handed over and belong to you, and we sign an NDA before any detailed discussion.
Ready when you are
Ready to boost engagement?
Tell us about your catalog and traffic, and our team will get back to you within one business day.
READY WHEN YOU ARE
Personalize with confidence.
Tell us about your catalog and we'll help you scope it.
