Recommendation systems, by the numbers
- 5-15%
- revenue lift from personalization done well
- McKinsey, 2021
- 4.9/5
- rated by clients on Clutch
- Verified reviews
- Since 2015
- shipping production AI and software
- Hybrid
- collaborative and content signals, tuned to your data
A product recommendation engine that doesn't understand your catalog recommends items that are superficially similar, 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.
According to McKinsey (2021), companies that get personalization right generate 40% more revenue from it than average performers, and personalization drives a 5 to 15 percent revenue lift across sectors. For e-commerce and content platforms, the primary lever is recommendation quality: the right item, the right user, the right moment.
Capabilities
What we build
01Collaborative 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 factorization approaches for large-scale user-item interaction datasets, real-time user similarity computation for personalized recommendations, and cold-start handling for new users with content-based fallbacks.
- Built with
- ALS · SVD
02Content-based recommendation engines
Item similarity models built from catalog 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 catalogs with rich metadata and for new-item cold-start scenarios.
03LLM-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 catalog items, with recommendation explanations in natural language ("Recommended because you bought X"). Effective for conversational recommendation interfaces and for catalogs where text descriptions carry the primary signal.
04Real-time recommendation APIs
Low-latency recommendation APIs that serve personalized 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.
05Email and push personalization
Batch recommendation pipelines for personalized email and push content: product recommendations, content suggestions, and re-engagement picks based on user history and context. Jobs run nightly or hourly from precomputed recommendation vectors. Cold-start users get popularity-based fallbacks from trending items in their most-visited categories, and re-engagement sequences surface products a user showed intent on but did not buy, rather than generic bestsellers.
- Built with
- Klaviyo · Mailchimp · Iterable · OneSignal · Firebase Cloud Messaging
06A/B testing and impact measurement
Experimentation infrastructure for recommendation systems: control and treatment assignment, business metric tracking (CTR, conversion, revenue per user), statistical significance testing, and reporting dashboards. Users are hashed into consistent buckets across sessions. Offline evaluation uses precision@10 and NDCG on a time-based split, never a random split that leaks future signal. Experiment duration is calculated upfront so you know a test can reach significance before it launches.
The right approach depends on what data you actually have, not on what is fashionable. Collaborative filtering learns from behavior, content-based learns from item attributes, and hybrid combines both. Here is how they trade off.
| Dimension | Collaborative filtering | Content-based | Hybrid |
|---|
| Learns from | User behavior: clicks, buys, ratings | Item attributes and preference profiles | Both signals combined |
| Needs | High interaction volume | Rich item metadata | A workable amount of each |
| Cold-start | Weak for new users and new items | Handles new items well | Best coverage of the three |
| Fails when | Data is sparse, so it recommends bestsellers to everyone | Metadata is thin or generic | Neither signal is strong |
| Typical fit | Mature catalog with lots of history | New catalog with detailed attributes | Most production systems |
A production recommender is a pipeline, not a single model. These are the parts we build and the choices that matter at each one.
- Candidate generation
- A fast first pass narrows your whole catalog to a few hundred plausible items per user. It favors recall over precision, so nothing relevant is dropped before ranking sees it.
- Ranking
- A second model scores the shortlist on the signals that predict action, click, add-to-cart, or purchase, and orders the final list. This is where business objectives, not raw similarity, decide what surfaces.
- Offline evaluation
- Before anything ships, we measure candidate models on historical data with precision@K, recall, and NDCG on a time-based split, never a random one that leaks future behavior into the test.
- Online evaluation
- Once live, an A/B test compares the model against a control on real metrics: click-through, conversion, and revenue per user. Offline scores and live behavior often disagree, so both matter.
- Cold-start
- New users and new items have no history. We fall back to content-based signals and popularity within a user's most-visited categories until enough interaction data accrues.
- Feedback loop
- Every impression, click, and purchase flows back into training data. We watch for the loop reinforcing its own past picks, which quietly narrows what users ever get to see.
How we work
From scope to shipped
Every project follows the same four phases. Scope is locked and price is fixed before development starts.
- Week 1
01Data assessment and scope
We assess your interaction volume, catalog size, metadata quality, and cold-start severity. You leave week 1 with a written scope document, the recommendation approach selected for your data state, and a fixed-price quote. No development starts without your sign-off.
- Weeks 2-4
02Model design and offline evaluation
We build and evaluate candidate models on your historical data before any production work. Offline metrics (precision, recall, NDCG) are measured on a time-based held-out split. Minimum performance thresholds are agreed before the model goes to the build phase.
- Weeks 4-12
03Build, integrate, and A/B test
Production API development, event tracking integration, and A/B testing infrastructure. Working recommendation API at a staging URL by the end of sprint one. Bi-weekly demos. QA runs in parallel with every sprint.
- Weeks 12+
04Launch and post-launch support
Production deployment with monitoring activated on launch day. A/B test results reviewed at 4 weeks post-launch. 8 weeks of post-launch support included in every project.
Why us
Why teams choose RaftLabs
01Senior engineers build what they scope
The engineers who assess your data and recommendation approach also build the system. No bait-and-switch, no offshore handoff after the contract is signed. The team you meet in week 1 ships in week 12.
02Fixed price before development starts
We scope the work, calculate the cost, and lock it in writing before any development starts. A scope change is a change request: priced, agreed, or dropped. It never absorbs into the project and appears on the final invoice.
03Shipping production software since 2015
Clients include Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin. Products in production across AI, SaaS, mobile, automation, and enterprise platforms in healthcare, fintech, logistics, and hospitality.
04ROI measured before and after
We agree on the business metrics that matter before development starts. CTR, conversion rate, revenue per user, session depth. The A/B testing infrastructure is built into every recommendation system so you can prove the impact, not just observe it.
Recommendation systems trained on your data, measured against your metrics
Collaborative filtering, content-based, hybrid, and LLM-powered recommendations. Fixed cost delivery.
Process
How we build recommendation systems
01Data assessment and approach selection
Before building, we assess your data, interaction volume, catalog 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.
02Offline 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 catalog sections. We establish minimum performance thresholds before the model goes to production.
03Incremental 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.
04Integration 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.
Recommendation systems fail in predictable ways. We design against these from the first sprint, not after they show up in production.
| The failure mode | How we design around it |
|---|
| Cold-start | New users and new items get poor or empty recommendations. | Content-based and popularity fallbacks, scoped to the user's categories, until interaction data builds up. |
| Popularity bias | The model keeps pushing bestsellers and buries the long tail. | Coverage and novelty are part of offline evaluation, and we re-weight so mid-tail items get a fair chance. |
| Filter bubble | The feedback loop narrows recommendations to a user's past behavior. | Controlled exploration and diversity constraints keep fresh, unexpected items in every list. |
| Stale embeddings | Item and user vectors drift as the catalog and behavior change. | Scheduled retraining and freshness monitoring, with alerts when offline metrics start to slip. |
Ready to scope your recommendation system project?
Thirty minutes to talk through scope. You get a costed plan within two business days. No commitment.
What clients say
What clients say
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
Grady Lakshmono
IndonesiaCoFounder, Gula (acquired by Runchise)
“RaftLabs elevated my ideas and brought them to life when everything seemed impossible.
