Can I show different recommendations to different customer segments?

Yes. Feed strategies can be segmented by LTV tier, purchase history, or any Intempt segment. The Lifecycle Marketer agent serves the right feed per segment without a separate build for every placement.

Does Recommend work for digital products, not just physical goods?

Yes. The recommendation engine works for any catalog with a product ID: software plans, digital content, courses, and more.

Can I suppress certain products from recommendations?

Yes. Business rules let you exclude out-of-stock items, low-margin SKUs, or any product by ID or category. Those rules are guardrails the agent cannot recommend around.

Product Recommendations

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.

Start for free

You're showing everyone the same products

0.0on G2

10B+events

500M+users

7+years profitable

50+enterprises

Every visitor sees what they'll actually buy.

One behavioral model. Every placement. Every channel.

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.

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.

Customer LTV scoring and churn prevention

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 the full list

In the words of50+ live tenants.

“We were losing visitors before they signed up. Intempt's personalized experiences changed that - we started meeting people where they were instead of guessing. Once they're in, Intempt's automated email takes over and keeps the relationship moving. Acquisition and retention finally feel like one connected motion instead of two separate problems.”

Jim Stromberg

CEO, StockInvest

“Intempt helped us turn real browsing and purchase signals into personalized experiences that drive repeat buying. We finally have one system that sees the whole customer journey.”

Eric Gardner

COO, FieldsUSA

“With Intempt, we built a signal-led pipeline driven by real behaviors. Follow-ups are triggered by intent signals instead of timelines, so we only focus on users who are truly engaging.”

Tadas Kertenis

Co-founder, Hoperfy

Frequently askedquestions.

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How long before recommendations get accurate?

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

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.