AI Personalization Engine Development

Reference architecture

1. Event & Profile Ingest
2. Feature Store
3. Segmentation + Decisioning
4. Real-time Inference API
5. Content / Offer Delivery
6. Experiment & Lift Tracking

A typical AI personalization system: events and profile data land in a feature store that a decision layer reads at request time; the inference API returns a personalized content or offer decision inside the page’s latency budget, and every impression flows into experiment tracking so lift is measured against a holdout.

Frequently asked questions

• How do you keep personalization fast enough to run on a page render?+

  We treat latency as a hard budget, not an aspiration. Signals are resolved from a feature store and served through a low-latency inference API (typically backed by Redis or DynamoDB), with a control path that returns the default experience the moment a decision would blow the budget. The visitor never waits on the model.

• Can you integrate with our CDP, like Segment or RudderStack?+

  Yes. Segment, RudderStack, mParticle, and reverse-ETL tools like Hightouch are common sources we read from. If your identity and event data already live in a CDP or warehouse, we build the personalization layer on top of that rather than asking you to re-plumb your data.

• Do you build recommendation systems, or just rules-based targeting?+

  Both, and we’ll be honest about which one earns its keep. Plenty of “personalization” wins come from well-structured rules and segments. When the catalogue or content library is large enough to justify it, we build collaborative-filtering or embedding-based recommenders with cold-start handling and diversity controls so the results don’t collapse into the same few items.

• How do you prove personalization is actually lifting conversion?+

  With holdouts. We build experiment assignment and a persistent control group so you can read incremental lift against a real baseline instead of comparing personalized users to everyone else. If a feature isn’t beating its holdout, you’ll see it. That visibility is part of what we ship.

• How do you handle consent and privacy for profiling?+

  Consent state from your CMP gates which signals are allowed to feed the model, and withdrawing consent stops profiling immediately rather than on the next batch run. Opt-out and “do not sell/share” signals are enforced through segmentation and decisioning so an opted-out visitor gets the default experience everywhere. The controls live in the data path, not in a policy document.