Recommendations

Recommendation engines shouldn't cost an enterprise budget.

Behavior-based recommendations personalize the next product, comparison, or recovery message from what each visitor actually did. Free to start — with the AI doing the heavy lifting.

Try Tradly Memory How it works

The landscape

Where recommendation engines live today

The field splits between enterprise platforms and feature-matching libraries.

Option	Strengths	The catch
Enterprise engines (Nosto, Algolia, Recombee, Dynamic Yield)	Scale and polish	Contracts, catalogs, and integration projects
Open-source libraries	Control, no SaaS fee	You build the pipeline and the model ops
Rule-based merchandising	Simple, predictable	Static — can't learn from each visitor
Tradly Memory (free to start)	Behavior-based personalization	Reaches full power as behavior accumulates

The difference

From 'similar items' to 'the right next step'

Classic engines match product-to-product: buy this, see similar. Behavior-based recommendations match person-to-next-step: this returning visitor searched, compared, and stalled — offer the comparison, the reassurance, or the recovery.

Intent, not just co-visits

When recommendations are driven by on-site behavior — searches, product views, repeated sessions, cart actions — they get better with every visit and need no manual catalog tagging.

Recommend on any surface

The same per-visitor context can drive product recommendations, personalized banners, next-best-action messaging, and agent answers through MCP.

What you get

Start free, personalize from day one

Behavior-first recommendations

Personalized to what each visitor did.

Intent signals

Searches, comparisons, stalls — the signals that predict buying.

Agent-ready memory

The same context feeds your AI stack via MCP.

The trade-off

What 'free' asks in return

Every free option trades something — enterprise engines trade setup cost, libraries trade engineering time, rule-based systems trade personalization depth. Tradly Memory, free to start, trades a little ramp-up: recommendations improve as behavior accumulates in your tenant. In exchange you get privacy-first, tenant-isolated personalization with no catalog migration and no ML team.

Recommend the next step, not just the next product

Free to start: behavior-based AI recommendations, tenant-isolated and privacy-first.

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