Recommendations, without a separate vendor.

Collaborative filtering, frequently-bought-together, trending, and similarity

Different placements need different logic. A product page wants "frequently bought together." A homepage wants "trending now." An empty cart wants "similar to what you usually buy." younifyd runs all four as first-class strategies against the same engine, so you pick the right one per placement instead of forcing one algorithm to do every job.

Switch a placement's strategy as a configuration change — no re-platforming, no new vendor evaluation.

4recommendation strategies, same engine

Merchandisers can still make the call

An algorithm doesn't know you're discontinuing a line or need to clear specific stock this week. Manual overrides let a merchandiser pin, exclude, or force a specific recommendation without touching the underlying model — the same philosophy as search merchandising, applied to recommendations.

no redeployfor manual recommendation overrides

Learns from what shoppers click and buy, not just the catalogue

Every impression, click, and conversion on a recommendation is logged, feeding an analytics layer that shows which strategy and which placement is actually driving revenue — not just which one looks reasonable in a demo. Recommendations improve from real behaviour over time instead of staying static from the day you launched them.

built inimpression/click/conversion tracking

No separate catalogue export, no second source of truth

The recommendations engine reads from the same Data Tables already powering your workflows, rather than requiring a separate product feed export and sync job to keep in sync. One less integration to build, one less place for your catalogue to drift out of date.

0separate catalogue syncs to maintain

Recommendations and search, sharing signal instead of operating blind to each other

Recommendations can optionally enrich the same search index that powers Search & Discovery — so a shopper's search behaviour can inform what gets recommended, and vice versa, instead of running as two disconnected systems that happen to sit on the same platform.

sharedsignal between search and recommendations

Recalculation happens off to the side, never in the request path

Recomputing trending products or similarity scores across a large catalogue is real work — younifyd runs it as a background worker, completely separate from the live request path that serves recommendations to shoppers. A full recalculation never shows up as latency on a product page.

0mslatency added to live storefront requests