Behavioral Signal Tracking
Views, searches, comparisons, and dwell time are tracked and batched client-side — never triggering a model call on every click.
Platform
TrailMind
A behavioral recommendation engine — grounded, evidence-based picks that adapt as behavior evolves.
TrailMind watches the trail a user leaves through a catalog — searches, views, comparisons — and reasons about where they're headed next. Recommendations are retrieved and generated fresh from that evidence, refreshing as behavior evolves, instead of leaning on static, one-size-fits-all rankings.
Capabilities
Recommendations your users can check
Every pick traces back to the exact behavior that produced it — never a black box.
Grounded Recommendations
Every recommendation ships with the exact evidence behind it — the activity that produced it, not a canned, generic suggestion.
Adaptive Retrieval Pipeline
A multi-stage retrieval and generation pipeline reranks and refines results, retrying automatically when a match comes back weak.
Feedback-Driven Reranking
Thumbs up/down feedback re-ranks future retrieval — scoped to genuinely similar queries, so one bad match can't wrongly suppress a good result.
Scheduled Digests
A recurring job re-runs personalized recommendations on a schedule and delivers them without waiting for the next visit.
Full Observability
Every recommendation run traces end to end, with cost, token, and latency rolled up for every trigger.
Want recommendations your users actually trust?
We'd love to show you how TrailMind grounds every recommendation in real behavior.
Book a demo