Almeta ML

Almeta ML focuses on a single promise: predict what each customer will do next and act on it in real time. Rather than relying only on browsing history, the platform blends individual behavior (views, searches, cart activity, purchases, returns) with your business priorities such as profit margins, inventory by location, delivery times, promotions and seasonal patterns. That combination lets merchandisers steer recommendations toward goals like higher average order value, faster inventory turnover or stronger margins, without hand-tuning every rule.

Deployment is designed to be lightweight. Teams integrate through a web tag, pre-built connectors or a REST API, typically within one to two weeks, and can then run several recommendation models in parallel — each tuned to a different objective like revenue, margin, click-through rate or conversion rate. Campaigns and audiences can be shared across multiple storefronts or brands, and recommendations refresh automatically as prices, stock levels and session behavior change.

The cold-start problem is addressed directly: for brand-new products with no click data, Almeta ML analyzes images, descriptions and specifications to classify items by visual and textual similarity and group them with comparable existing products. First-time visitors are matched using product attributes, live session signals and geographic context, so personalization works in the very first session instead of after a two-week learning period. Pricing is usage-based and starts at $99 per month, with a 14-day free trial, and the company states it does not collect personally identifiable customer data unless you choose to include it.