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.

AI Personalization Engine Development

We build real-time content and offer personalization layers, recommendation engines, and segmentation models for marketing-AI products. Low-latency inference, honest data controls, architected to scale.

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What we build

Real-time content personalization

Per-visitor page, hero, and email-block personalization decided at request time, with a control path that falls back to the default experience the instant the model is uncertain.

Offer decision engines

A mix of rules and a model that picks the offer, discount, or nudge most likely to convert a given user, with eligibility limits and margin floors built in.

Segmentation & audience models

Behavioural and lifecycle segment models, lookalike scoring, and audience builders that stay in sync with the traits your downstream tools already trust.

Recommendation systems

Collaborative-filtering and embedding-based recommenders for content, products, and articles, with cold-start handling and diversity/recency controls.

Real-time inference & feature serving

A feature store and low-latency serving layer that resolves a visitor’s signals and returns a decision inside the latency budget a page render allows.

A/B/n testing & lift measurement

Experiment assignment, holdout groups, and incremental-lift reporting so personalization is measured against a real baseline instead of assumed to be working.

Marketing data that resists wishful thinking

Reference architecture

Event & Profile Ingest
Feature Store
Segmentation + Decisioning
Real-time Inference API
Content / Offer Delivery
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.

Integrations shipped across 22+ partners.

CDPs & identity

Segment
RudderStack
mParticle
Hightouch

Product & behavioural analytics

Mixpanel
Amplitude
PostHog
Google Analytics 4

Experimentation & flags

LaunchDarkly
Statsig
GrowthBook
Optimizely

Feature store & serving

Feast
Tecton
Redis
DynamoDB

ML platforms

SageMaker
Vertex AI
Custom Python pipelines

AI providers

OpenAI
Anthropic
OpenRouter

Which engagement fits

Project Build

For standing up the core engine: feature store, decisioning, real-time inference, and experiment tracking. Delivered in a focused 8–14 week scope.

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Embedded Squad

For evolving models, adding recommendation features, and tuning lift over time. A dedicated team that ships alongside yours as the product grows.

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Tech Audit

For assessing an existing personalization stack: a 5-day diagnostic of latency, data plumbing, model health, and whether the lift is measured at all.

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Compliance considerations

Compliance posture for AI personalization builds. Status reflects how we engineer consent and data controls into your platform. Regulatory ownership stays with your team.
Standard	Status	What we ship
GDPR / UK GDPR	Compliant	Lawful-basis handling, data subject rights, and retention controls across the profile store and feature pipeline. See /compliance/gdpr.
CCPA / CPRA	Compliant	Consumer opt-out and “do not sell/share” signals wired through segmentation and decisioning so an opted-out visitor is served the default experience everywhere.
Consent & preference enforcement	Compliant	Consent state (via your CMP) gates which signals feed the model. Profiling stops the moment consent is withdrawn, not on the next batch.
Automated decision-making fairness	In progress	For offer and eligibility decisions we build per-decision explainability and recommend a bias review of the segment features. The engineering is ours, the policy call is yours.

Free

AI Marketing Stack Audit

A review of your personalization platform’s architecture, inference latency, data plumbing, and whether the lift you’re reporting is measured against a real holdout.

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Data analyst working at a workstation with multiple data views

Attribution, personalisation, and scale in production

Marketing AI products live or die on data quality, latency, and whether the numbers hold up under real traffic. We build for the dashboard that a CMO opens at 8am on Monday.

Frequently asked questions

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.

Building an AI personalization product? Schedule a meeting.

We’ve shipped the feature stores, real-time inference, and experiment tracking that personalization teams run in production. Tell us what you’re building.

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