Personalization built into your product, not a third-party tag
We build recommendation engines and content ranking into your own data layer. Users see what fits them. You own the model.
Last updated September 2026. US LLC in Wyoming. Our team works in your timezone.
The problem
Sound familiar?
Every user sees the same content in the same order
A new user and a power user see the same homepage. One ignores what they already know. The other drowns in options.
Third-party widgets share your user data with competitors
Plug-in tools train one shared model across all their customers. Your data helps a model that also serves your competitors.
Cold-start leaves new users with no relevant recommendations
Collaborative filtering fails when a user has no history. Without a cold-start plan, the first session is as generic as no personalization at all.
You cannot prove personalization improves retention
Without A/B testing built into the serving layer, you cannot tell if the model helps or just shuffles things around.
What do we actually do?
We track your product events, build a feature store, train a recommendation model, and ship an API your frontend calls. No widgets, no data sharing, no black box.
What changed in 2026?
In 2026 plug-in personalization widgets train one shared model across every customer, and privacy rules make that harder to justify each year. Owning the model on your own data layer is now the safer choice and the better one. Serving ranked results under 100ms is a solved problem when the feature store is built right.
What's included?
- Event pipeline for clicks, dwell time, and conversions
- Feature store with user profiles and item embeddings
- Recommendation model, collaborative or content-based, to fit your data
- A/B testing framework with statistical significance tracking
- Serving API returning ranked results under 100ms at P95
- Monitoring dashboard for CTR, conversion lift, and drift
- Docs and retraining runbook so your team can iterate
How it works
01.
Instrument
We audit your event tracking, fill the gaps, and define the signals that train the model.
02.
Model
We build the feature store and train a model on your data, using the method your data supports today.
03.
Serve
We deploy a serving API and wire it to your frontend. Ranked per user, cached, under 100ms.
04.
Optimise
We A/B test against the baseline, measure CTR and retention lift, and retrain on a schedule.
Proof it works
What we have already shipped
Case study
Pack Assist
8-week delivery, RAG + hybrid AI
Read the case study
Free tool
Personalization Readiness Checklist
Free resource for Software Founders & Builders.
Open the free tool
The offer
Free 30-minute discovery call
We listen first. No pitch decks.
Book a discovery call
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