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Intempt
A striped lighthouse on a rock sending beams of light over the waves.

Show what they want to buy next.

The Lifecycle Marketer agent builds a feed per visitor from your live customer context - the same behavior your analytics already tracks, with no export to a second tool. Lifts AOV instead of adding noise.

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G2
0.0on G2
10B+events
500M+users
7+years profitable
50+enterprises

Recommendations trained on your customers' behavior.

Intempt reads every product view, add-to-cart, and purchase to build a behavioral model specific to your catalog and your customers. The AI recommendations update in real time as behavior changes, not once a day. The model runs as a standing skill, not a data-science project you have to staff.

  • Views, carts and purchasesThe three events that matter map in without custom work.
  • Model updates in real timeRe-fits as behaviour changes, with no retraining job to schedule.
  • Catalog via Shopify or APISync the store directly or push the catalog over the API.
Recommendations trained on your customers' behavior

Match the recommendation to the moment.

A homepage widget, a cart upsell, and a post-purchase email each need different logic. You pick the strategy per placement and A/B test it before committing. That choice is the strategy layer, and the Lifecycle Marketer agent handles the ranking underneath it.

  • Four built-in strategiesMost Popular, Recently Viewed, Purchased Together and User Affinity.
  • Image similarityFor catalogs where what it looks like matters more than what it is tagged.
  • Test the strategy per placementRun two strategies against each other on the same slot before either ships.
Match the recommendation to the moment

Wherever your customers are.

Intempt serves recommendations wherever the moment is right: on-site widgets, inside lifecycle email journeys, in post-purchase flows, and via API. Each placement runs as its own play on one behavioral model, so there is no handoff between an on-site tool and an email tool to keep the two in sync. It is the same customer context every Blu agent reads.

  • JavaScript embedDrop the widget onto the page with a snippet.
  • Blocks inside journey emailThe same recommendations render in the email, personalized per recipient.
  • REST APICall it directly when the placement is not a widget.
Serve recommendations wherever your customers are

Use cases built for the metrics that matter.

Three outcomes teams measure from day one.

RFM tiers, churn risk and a journey triggered when the tier changes

Wired into the toolsyour team already opens.

Slack, Stripe, Twilio, SendGrid, Gmail, Google Calendar, Firebase, Apache Kafka, AWS. Blu Agent operates them for you without a browser tab.

See every integration
JavaScript
Node JS
Apple
Stripe
Gmail
Google Calendar
Google Meet
Twilio
SendGrid
Slack
Amazon SES
Webhook
Apache Kafka
Amazon S3
Firebase Cloud Messaging

Teams love it

They stopped stitching tools andhired one system.

StockInvest

Acquisition and retention are one connected motion. Personalizing the path before signup only pays off when the same system carries the user through onboarding afterward.

Anonymous traffic was not turning into registered users, and acquisition and retention were run as two separate problems. We ran Experiences on the anonymous visitor flow to put the right content and CTAs in front of each visitor, then handed new signups to automated Journeys for onboarding and deeper engagement.

455,396

users instrumented

Read the full research
FieldsUSA

Judge experiments on revenue per visitor instead of conversion rate. A variant can win on conversions and still lose on revenue, and repeat buying only shows up when the whole journey is in one view.

High-intent shoppers browsed a large, specialized outdoor catalog, and browsing, product interest and purchase history sat in different places. We unified them into one customer view, then ran real-time personalization, intent-based segmentation and timely follow-ups on top of it, judging experiments on revenue per visitor.

Read the full research
Hoperfy

Build the pipeline on observed signals. When follow-ups trigger on intent, the team spends its time on the users who are genuinely engaging.

Static lead lists and timeline-based follow-ups were hiding who was actually engaging. We ran a signal-led outbound pipeline where follow-ups fire on real behavior: outbound replies, onboarding progress and product usage.

Read the full research

Frequently askedquestions.

With sufficient catalog events, views, adds-to-cart, purchases, most customers see meaningful personalization within 1 to 2 weeks.

Sources

* CUPED (Controlled-experiment Using Pre-Experiment Data) achieves 7-45% variance reduction depending on covariate selection. Deng et al. (Microsoft Research, 2013); Eppo, Statsig documentation (2024-2025).

Intempt Marketing

Your catalog working smarter, starting this week.

Connect your catalog, drop a widget, and start serving personalized recommendations in minutes. No engineering required, no analyst to staff it, and the exclusion rules you set hold on every feed.

Start for free