• Generic recommendation engine not reflecting your actual catalogue structure or user behaviour?

  • No way to measure whether your recommendations are actually driving engagement or revenue?

Recommendation System Development

Generic recommendation systems trained on open datasets don't understand your catalogue, your users, or your business context. A product recommendation engine for an electronics retailer needs different signals than one for a streaming platform or a B2B SaaS tool.
We build custom recommendation systems trained on your interaction data -- collaborative filtering, content-based filtering, hybrid models, and LLM-powered recommendations -- designed around your specific catalogue, user behaviour, and business objectives.

  • Custom recommendation models trained on your user interaction and product data

  • Collaborative filtering, content-based, hybrid, and LLM-powered recommendation approaches

  • Real-time and batch recommendation pipelines integrated into your product

  • A/B testing infrastructure to measure recommendation impact on engagement and revenue

RaftLabs builds custom recommendation systems for e-commerce, media, SaaS, and marketplace platforms -- including collaborative filtering, content-based filtering, hybrid recommendation models, and LLM-powered recommendation engines. Recommendation systems are trained on your specific user interaction data and catalogue, not generic datasets. We build real-time recommendation APIs, batch recommendation pipelines, and A/B testing infrastructure to measure recommendation impact on engagement and revenue. Most recommendation system projects deliver in 8--16 weeks at a fixed cost.

Vodafone
Aldi
Nike
Microsoft
Heineken
Cisco
Calorgas
Energia Rewards
GE
Bank of America
T-Mobile
Valero
Techstars
East Ventures

Generic recommendations recommend the wrong things

A recommendation engine that doesn't understand your catalogue recommends items that are superficially similar but not actually relevant. A collaborative filtering model trained on too little data recommends popular items to everyone. A content-based model without proper item attributes recommends based on surface characteristics rather than the features users actually care about.

Custom recommendation systems are trained on your data, tuned for your business objectives, and measured against your actual engagement and revenue metrics.

What we build

Collaborative filtering models

User-based and item-based collaborative filtering trained on your interaction data -- purchase history, click streams, view events, ratings, and engagement signals. Matrix factorisation approaches (ALS, SVD) for large-scale user-item interaction datasets. Real-time user similarity computation for personalised recommendations. Cold-start handling for new users with content-based fallbacks.

Content-based recommendation engines

Item similarity models built from catalogue attributes -- product categories, tags, descriptions, price ranges, and custom metadata. User preference profiles built from interaction history. Hybrid content-item representations that combine structured attributes with text embeddings from product descriptions. Effective for catalogues with rich metadata and for new-item cold-start scenarios.

LLM-powered recommendations

Recommendation systems that use large language models to understand item descriptions, user queries, and preference signals in natural language. Semantic similarity between user intent and catalogue items. Recommendation explanations in natural language ("Recommended because you bought X"). Effective for conversational recommendation interfaces and for catalogues where text descriptions carry the primary signal.

Real-time recommendation APIs

Low-latency recommendation APIs that serve personalised recommendations in real time -- typically under 100ms for homepage, product detail page, and cart recommendations. Precomputed recommendation caches for high-traffic surfaces, real-time user event processing for recency weighting, and feature stores that make user context available to the recommendation model without repeated computation.

Email and push personalisation

Batch recommendation pipelines for personalised email and push notification content -- product recommendations, content suggestions, and re-engagement recommendations based on user history and current context. Scheduled generation of personalised content for sending time optimisation. Integration with email platforms (Klaviyo, Mailchimp, Iterable) and push notification services.

A/B testing and impact measurement

Experimentation infrastructure for recommendation systems: user assignment to control and treatment groups, metric tracking for business impact (CTR, conversion, revenue per user, engagement), statistical significance testing, and reporting dashboards. A/B testing that tells you whether your recommendations are actually driving the outcomes you care about, not just whether they look different.

Recommendation systems trained on your data, measured against your metrics

Collaborative filtering, content-based, hybrid, and LLM-powered recommendations. Fixed cost delivery.

How we build recommendation systems

Data assessment and approach selection

Before building, we assess your data -- interaction volume, catalogue size, metadata quality, and cold-start severity. The assessment determines which recommendation approach will work for your specific data state. We don't recommend collaborative filtering if you don't have sufficient interaction data, or content-based filtering if your item metadata is sparse. Honest assessment before any development commitment.

Offline evaluation before deployment

Every recommendation model is evaluated on historical data before deployment: precision and recall at K, NDCG, coverage, and novelty metrics measured on a held-out test set. Offline evaluation catches approaches that look good on average but fail on specific user segments or catalogue sections. We establish minimum performance thresholds before the model goes to production.

Incremental improvement with A/B testing

Production recommendation systems improve over time through experimentation. We build the A/B testing infrastructure so your team can run controlled experiments on recommendation changes and measure the actual business impact. Recommendation quality is tracked as a product metric, not a one-time engineering deliverable.

Integration with your product stack

Recommendation APIs integrated into your product -- e-commerce platform, mobile app, content management system, or custom application. Event tracking for interaction data collection (views, clicks, purchases, ratings) that feeds back into model retraining. Data pipeline from your product database to the recommendation model. The full integration, not just a model.

Recommendations that drive revenue, not just clicks

Custom models trained on your data with A/B testing to prove business impact. Fixed cost.

Let's talk about your project

Tell us your catalogue size, the user interaction data you have, and where recommendations will appear in your product. We'll scope the right approach and give you a fixed cost.

Frequently asked questions

We build across the main recommendation approaches: (1) Collaborative filtering -- recommendations based on the behaviour of similar users (user-based) or similar items (item-based). Works well when you have sufficient interaction data (views, purchases, ratings, clicks). (2) Content-based filtering -- recommendations based on item attributes and user preference profiles. Works when you have rich item metadata and can profile user preferences. (3) Hybrid models -- combining collaborative and content-based signals for better coverage and accuracy. Most production systems use hybrid approaches. (4) LLM-powered recommendations -- using language models to understand item descriptions, user queries, and preference signals in natural language. Effective for new-item cold-start problems and when catalogue items have rich text descriptions. (5) Session-based recommendations -- predicting the next item based on current session behaviour, without requiring user history. We select the approach based on your data availability, catalogue size, and use case requirements.

Data requirements depend on the approach. For collaborative filtering: user-item interaction data -- at minimum, implicit feedback (clicks, views, add-to-cart, purchases) across a sufficient user and item population. Typically need 100,000+ interactions for stable collaborative filtering; more is better. For content-based filtering: structured item attributes (category, brand, price range, tags) and either user preference history or signals you can use to build preference profiles. For LLM-powered recommendations: item text descriptions (title, description, features). Cold-start is a solvable problem -- we design systems that handle new users and new items with content-based fallbacks. We assess your data during scoping and design the right approach for what you have.

We build measurement infrastructure as part of every recommendation system: A/B testing framework to compare recommendation variants against each other or against a baseline, online metrics (click-through rate, add-to-cart rate, conversion, revenue per user, session depth), and offline evaluation metrics (precision, recall, NDCG) on historical data during development. Business impact metrics are agreed before development starts -- the recommendation system should improve specific measurable outcomes, not just produce plausible-looking results. We design the A/B testing infrastructure so you can run experiments and measure the actual revenue or engagement impact of recommendation changes.

A focused recommendation system -- one recommendation use case (product recommendations, content recommendations, or similar items), trained on your data, with a production API and basic A/B testing -- typically runs $25,000--$60,000. A full recommendation platform with multiple recommendation surfaces (homepage, PDP, cart, email), real-time personalisation, and advanced A/B testing infrastructure runs $60,000--$150,000. Cost depends on the algorithmic complexity, data pipeline requirements, real-time vs. batch serving, and the number of recommendation surfaces. We scope every project before pricing it.