Self-learning recommendation technology
Theklen is a continuously learning neural network recommendation engine for ecommerce and retail. It turns customer behaviour into ranked product decisions designed to improve conversion, basket value and revenue per visitor.
Built for prediction, not conversation.
Why neural recommendations
Customer behaviour changes. Your ranking model should change with it.
Static segments and manually maintained rules age quickly. Theklen learns from product impressions, views, clicks, cart additions and purchases, then uses that feedback to improve future rankings.
Individual behaviour
Rank products around the signals of a visitor or session, not only broad customer groups.
Commercial context
Account for placement, catalogue, availability and the outcome the retailer needs to improve.
Continuous adaptation
New interactions become training signal. Recommendations can evolve as customers and assortments change.
The learning loop
Every interaction can improve the next decision.
The engine does not return a fixed list. It scores eligible products for the current customer and context, observes the outcome and feeds that evidence into later rankings.
01
Collect
Receive behavioural events and catalogue updates.
02
Learn
Turn customer, product and context signals into neural representations.
03
Rank
Score eligible products against the current commercial objective.
04
Improve
Use subsequent engagement and purchase outcomes as new feedback.
For a closer look at inputs, outputs and integration, see the product recommender system.
A different kind of AI
Recommendation is a ranking problem, not a writing exercise.
Theklen neural engine
Learns behavioural patterns, continuously adapts product ranking and is built for commercial prediction at catalogue scale.
Rule-based systems
Useful for hard constraints and campaigns. As products, contexts and segments multiply, manual logic becomes expensive to maintain and slow to adapt.
LLM-only approaches
Excellent for language and conversation. They are not inherently trained to optimise behavioural product ranking, and generated answers can introduce catalogue or factual errors.
Commerce outcomes
Optimise what matters, not merely what looks similar.
A product can be textually or visually similar and still be the wrong commercial recommendation. Theklen is designed to rank products against the retailer’s objective.
Conversion rate
Help more visitors reach a product worth buying.
Average order value
Surface relevant cross-sell and upsell opportunities.
Revenue per visitor
Measure value across the shopping session rather than a single click.
Product discovery
Make large and changing catalogues easier to explore.
Category ordering
Arrange the same catalogue differently for different customers and contexts.
Next-best product
Predict what each shopper is most likely to engage with or purchase next.
Integration
Add intelligence without rebuilding the commerce stack.
Connect product data and behavioural events to Theklen. The engine returns ranked product identifiers through the agreed integration layer, while the webshop or app renders its own current content, price and availability.
Webshop or app
Events, catalogue data and recommendation request.
Theklen engine
Neural scoring, ranking and continuous learning.
Ranked products
Storefront-ready product IDs for each placement.
Measured response
Customer outcomes return to the learning loop.
Production reference
Used in real-world retail at significant scale.
Theklen’s recommendation technology has been used by the largest tax-free retailer in Northern Europe. We keep that claim deliberately narrow until a quantified case study is approved. Marketing arithmetic has never improved a model.
Frequently asked questions
Neural network recommendation engine FAQ
What is a neural network recommendation engine?
It is a machine-learning system that learns patterns from customer, product and contextual data, then scores and ranks eligible products for a specific visitor or placement.
How does continuous learning improve ecommerce recommendations?
New behavioural events provide fresh evidence about what customers engage with and buy. Feeding those outcomes back into the model allows rankings to adapt as behaviour, products and demand change.
How is Theklen different from a rule-based recommender?
Rules explicitly define what should appear in a given situation. Theklen learns ranking patterns from behaviour. Rules can still enforce stock, eligibility or campaign constraints, but they do not replace the underlying customer model.
How is Theklen different from an LLM recommender?
Large language models are designed primarily to understand and generate language. Theklen’s core task is different: predict and rank eligible catalogue products from behavioural signals and commercial context. An LLM can support a conversational interface, but it is not a substitute for a specialised ranking model.
Which ecommerce use cases does Theklen support?
Typical uses include personalised product recommendations, cross-sell and upsell, personalised category ordering, product discovery and next-best-product ranking.
What data does an implementation require?
The exact setup depends on the retailer. Common inputs include product impressions, views, clicks, cart additions, purchases, customer or session identifiers, catalogue attributes, price and availability.
Does Theklen generate product descriptions or invent products?
No. The recommendation engine returns an ordered selection from eligible catalogue products. The retailer remains the source of truth for product content, pricing and availability.
Evaluate the fit
See what Theklen could learn from your customers.
We will review your catalogue, traffic, behavioural data and current recommendation setup, then tell you whether there is a credible path to measurable improvement.