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Personalization that ships as a product feature, not a third-party tag

We build recommendation engines and behavioral content ranking directly into your product data layer. Your users see what is relevant to them, and you own the model.

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Sound familiar?

Your product shows every user the same content in the same order
A new user and a power user see an identical homepage. The power user ignores what they already know. The new user drowns in options they are not ready for.
Third-party personalization widgets share your user behavior data with competitors
Most plug-and-play recommendation tools train a shared model across all their customers. Your behavioral data teaches a model that also serves your direct competitors.
Cold-start leaves new users with no relevant recommendations
Collaborative filtering breaks down when a user has no history. Without a cold-start strategy, your first-session experience is as generic as no personalization at all.
You cannot test whether personalization actually improves retention
Without an A/B testing framework baked into the serving layer, you have no way to measure whether the model is helping or just rearranging deck chairs.

What we actually do

We instrument your product, build a feature store from your existing behavioral data, train a recommendation or ranking model, and ship a serving API your frontend calls directly. No third-party widgets, no data sharing, no black box.

What's included

Behavioral event pipeline capturing clicks, dwell time, and conversion signals
Feature store with user profiles and item embeddings refreshed on your schedule
Trained recommendation model using collaborative filtering or content-based ranking, depending on your data maturity
A/B testing framework built into the serving layer with statistical significance tracking
Serving API returning ranked results with latency under 100ms at P95
Model performance monitoring dashboard tracking CTR, conversion lift, and drift
Documentation and retraining runbook so your team can iterate without us

How it works

Instrument

We audit your existing event tracking, close the gaps, and define the behavioral signals that will train the model.

Model

We build your feature store and train a recommendation model on your data. We choose collaborative filtering or content-based ranking based on what your data can support today.

Serve

We deploy a serving API into your stack and wire it to your frontend. Results are ranked per user, cached intelligently, and returned in under 100ms.

Optimise

We run A/B tests against the baseline, measure CTR and retention lift, and retrain on a schedule so the model improves as your user base grows.

Pack Assist
8-week delivery, RAG + hybrid AI
Read the case study
Scoped per feature

Discovery scoping call required. Most builds deliver in 6 to 12 weeks depending on data availability.

Frequently asked

Ready to get started?

Let's build your ml-driven personalization system

Hire Us on Upwork