0.0on G2Recommendations 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.
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.
Use cases built for the metrics that matter.
Three outcomes teams measure from day one.
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
Teams love it
They stopped stitching tools andhired one system.
StockInvest
“Acquisition and retention finally feel like one connected motion instead of two separate problems.”
Jim Stromberg
CEO, StockInvest
FieldsUSA“We finally have one system that sees the whole customer journey.”
Eric Gardner
COO, FieldsUSA
Hoperfy“With Intempt, we built a signal-led pipeline driven by real behaviors.”
Tadas Kertenis
Co-founder, Hoperfy
Frequently askedquestions.
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).