Recommendation systems development · EonTech

The right item, for
the right person, in the right order.

We build personalisation and ranking engines on your own catalogue and behaviour — handling cold-start, proving relevance with online tests, and serving fast enough for request time. Measured lift, not magic, and models you own.

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  • Trained on your data
  • Proven with online tests
  • GDPR · ISO 27001
Your catalogue
Personalisation trained on your items and behaviour
Measured live
Relevance proven with online tests, not just offline
You own it
Models, features, and serving stack stay yours

Relevance you can measure

A recommender is only as good as its lift

Offline scores are a guide, not a verdict. We prove recommendations on live traffic with A/B tests against the metric that matters on each surface, and report the lift against a strong baseline.

We handle cold-start so new users and items get credible results, and we serve fast enough to recommend at request time — then keep the model current as your catalogue moves.

See our custom model practice

What we build

Personalisation across every surface

One recommender rarely fits every placement. We build the right approach for each surface, all on your data and your serving stack.

  • Product recommendations

    Related, complementary, and personalised picks across listing, detail, cart, and email — tuned to the goal of each surface, not one generic widget.

  • Content & media

    Feeds and next-up suggestions that balance relevance with freshness and diversity, so users discover rather than loop.

  • Search ranking

    Learning-to-rank that orders results by what users actually engage with, blending relevance signals with personalisation.

  • Personalised offers

    Match promotions and bundles to intent and history, with humans setting the guardrails on what may be offered.

  • Cold-start strategies

    Content-based and popularity fallbacks that give new users and new items credible recommendations from day one.

  • Diversity & fairness

    Controls that prevent feedback loops, over-concentration, and filter bubbles, tuned to outcomes you actually want.

Why EonTech

A partner for recommenders that earn their keep

  • Senior only

    Engineers who have run recommenders in production at request-time latency, not just in notebooks.

  • Measured honestly

    We prove lift with online tests against your metrics, not cherry-picked offline scores.

  • You own it

    Models, feature pipelines, and the serving stack remain yours to run and extend.

  • MLOps-ready

    Recommenders monitored and retrained as catalogue and behaviour shift.

How we deliver

From a surface goal to served relevance

We start from what each placement should achieve and end at a model that is tested on live traffic and fast enough to serve.

  1. 01

    Define the surface and metric

    We agree what good looks like on each surface — clicks, conversions, retention — before any modelling. Different surfaces want different things.

  2. 02

    Build features and baselines

    We engineer features from your behaviour and catalogue and stand up a strong baseline, so every later gain is measured against something real.

  3. 03

    Model and rank

    We develop candidate generation and ranking, validate offline, and handle cold-start so new users and items are never left with nothing.

  4. 04

    Test online and serve

    We A/B test against your metrics, ship low-latency serving, and monitor and retrain as the catalogue and behaviour change.

What you get

Recommenders built for production reality

  • Surface-specific tuning — each placement optimised for its own goal.
  • Strong baselines — every gain measured against a real comparison.
  • Cold-start coverage — credible results for new users and items.
  • Online A/B testing — lift proven on live traffic, not just offline.
  • Low-latency serving — recommendations returned at request time.
  • Owned serving stack — models and pipelines run on your infrastructure.

Common questions

What teams ask before personalising

  • How do you handle new users and new items?

    Cold-start is designed in, not patched later. For new users we fall back to content-based and popularity signals; for new items we lean on their attributes and similarity to known items. As behaviour accumulates, the system shifts toward personalised collaborative signals — so a new user or product always gets credible recommendations from day one.

  • How will we know the recommendations actually work?

    We measure them, honestly. Offline metrics guide development, but the real proof is online A/B testing against the business metric that matters on each surface — clicks, conversion, or retention. We report lift against a strong baseline rather than cherry-picking favourable offline numbers.

  • Do we own the models and serving stack?

    Always. The models, feature pipelines, and the serving infrastructure remain yours to run, audit, and extend. We build on infrastructure you control, keep latency low enough for request-time use, and avoid lock-in to any single vendor.

Personalise what your users see next

Tell us the surfaces you want to personalise and the metric each should move. We will scope a baseline and a first online test in days.

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