Sparse behavior data
Most customers interact with only a few items, which gives collaborative methods little to learn from.
AI & Automation · Recommendation Systems
Recommendation system development grounded in your own data
We build recommendation engines that learn from what customers browse and buy, respect the rules your merchandisers care about, and are tested against simple alternatives before they earn a place on the page.
What is AI Recommendation Systems?
Recommendation system development is the design and engineering of software that suggests products, content or actions to each user based on behavior data, item attributes and business rules. Common methods include collaborative filtering, which finds patterns in what similar users interact with, and embedding-based similarity, which matches items by their descriptions or features. A production recommender also needs fallbacks for new users and items, merchandising controls and a way to test whether it actually helps.
Generic "customers also bought" plugins often show the same bestsellers to everyone, recommend items that are out of stock or recommend the product a customer just purchased. A custom recommender can do better, but only if there is enough interaction data, the catalogue is well described and the results are compared against a simple baseline. Without that discipline it is easy to build something complex that performs no better than a bestseller list.
Why off-the-shelf recommendations disappoint
Cold start
New products and first-time visitors have no history, so purely behavior-based methods can't recommend them.
Ignoring business constraints
Recommendations that ignore stock, margin, region or compliance rules create problems for operations.
No proof of impact
Without controlled testing, it's impossible to know whether recommendations help or simply take up space.
Our AI Recommendation Systems service
Data and event tracking
Defining and capturing the events that matter, such as views, add-to-cart, purchases and dismissals, consistently across web and app.
Catalogue enrichment
Cleaning product attributes and generating text or image embeddings so items can be compared by content as well as behavior.
Recommendation models
Collaborative filtering, item-to-item similarity, embedding-based retrieval or hybrid approaches, chosen by testing on your historical data.
Business rules layer
Filters and boosts for stock, price range, region, margin, exclusions and merchandiser-pinned items applied on top of model output.
Serving API
A fast recommendation API with caching, fallbacks for new users and items, and widgets or endpoints for your site, app or emails.
Testing and monitoring
Offline evaluation on historical data, A/B tests against a baseline, and dashboards that track how recommendations are used.
Included capabilities
- Similar-item recommendations
- Frequently bought together
- Personalized home and category feeds
- Recommendations in emails and notifications
- Cold-start fallbacks
- Stock and availability filters
- Merchandiser pinning and exclusions
- A/B testing against baselines
- Recommendation performance dashboard
What AI Recommendation Systems changes for your business
More relevant discovery
Customers see items related to their interests rather than the same list everyone sees.
Merchandisers keep control
Rules and pinning let your team steer recommendations without retraining models.
Decisions backed by tests
A/B testing shows whether the recommender outperforms a simpler approach on your site.
Reusable across channels
One recommendation API can serve the website, app and emails.
Where it makes the biggest difference
Online retail
Similar products on product pages, complementary items in the cart and personalized home page sections.
B2B reordering
Suggesting products a trading customer usually orders together or is due to reorder, based on their purchase history.
Learning platforms
Recommending courses or lessons based on what a learner has completed and what similar learners took next.
Content and listings
Recommending articles, property listings or services based on browsing behavior and item similarity.
Seven steps from goal to tested recommender
Every model is compared with a simple baseline, first on historical data and then in a live A/B test.
Define the goal
Agree where recommendations appear and which outcome they should improve, such as discovery, basket size or content engagement.
Assess data
Review event history, catalogue quality and volume to choose methods that the data can actually support.
Build baselines
Implement simple approaches such as bestsellers and category popularity to have something to beat.
Model and evaluate offline
Train candidate models and compare them with baselines on held-out historical data.
Integrate
Deploy the serving API with business rules, caching and fallbacks, and connect it to your storefront or app.
A/B test
Run live experiments against the baseline and decide based on measured results.
Iterate
Retrain on fresh data, monitor for drift and adjust rules with your merchandising team.
What it connects to
AI Recommendation Systems
- Shopify and WooCommerce stores
- Odoo eCommerce and product data
- Custom storefronts and mobile apps
- Web and app analytics events
- Email marketing platforms
- Vector databases and pgvector
Built with
Security, quality and scale
Privacy-aware tracking
Behavior tracking aligned with your consent and privacy policies, with options to use anonymous session data.
Data minimization
Models use interaction events and item data, not unnecessary personal details.
Sensitive category handling
Rules help prevent recommendations that could reveal sensitive information, such as health-related purchases, in shared contexts.
Scalable serving
Caching and precomputed results help keep the API responsive during traffic peaks.
What working with us looks like
Baselines before complexity
We always measure against simple methods and recommend a custom model only if it does better.
E-commerce experience
We build storefronts and Odoo eCommerce setups, so recommendations fit into the real buying flow.
Business rules respected
We design for stock, margin and merchandising constraints from the start.
Honest data assessment
If your data volume can't support personalization yet, we'll tell you and suggest what to collect first.
What it costs
Fixed-price packages with the scope written down, paid stage by stage. Your project goes live once it's fully paid.
Related services, technologies and industries
Sources and further reading
Official documentation and guidance if you want to read more about this topic.
AI Recommendation Systems: questions we hear most
How much data do we need for a recommendation system?
Collaborative filtering needs a meaningful volume of user interactions across many items, while content-based and embedding methods can work with less behavior data if the catalogue is well described. We assess your data first and choose methods it can support.
How do you recommend new products with no history?
New items are matched to existing ones using attributes, descriptions or image embeddings, and new visitors see popular or contextual recommendations until there is enough behavior to personalize. This is known as handling the cold-start problem.
Is a custom recommender better than a Shopify or WooCommerce plugin?
Not always. Plugins can be a reasonable starting point for smaller catalogues, while a custom system becomes worthwhile when you need your own business rules, multiple channels or control over the method. Testing against a plugin or baseline is the way to decide.
How do you measure whether recommendations work?
We evaluate offline on held-out historical data and then run A/B tests that compare recommendations against a baseline on the metrics you choose. We report the measured results rather than assume an improvement.
Do recommendations raise privacy concerns?
They can, because they rely on behavior data. We align tracking with your consent mechanisms and privacy policy, minimize personal data in models and avoid recommendations that expose sensitive purchases.
Next step
Check whether your data can support recommendations
Tell us about your catalogue, traffic and the data you already collect. We'll tell you which recommendation approach is realistic and how we'd test it.