Retellect detects wrong product selections at the lane and speeds up checkout flow — reducing shrinkage, staff interventions and customer friction on the checkouts.
At self-checkout, shoppers pick loose items like fruit and vegetables from a list on the screen. Sometimes the wrong item is chosen — by mistake, like peaches selected as apricots, or on purpose, like expensive alcohol rung up as cheap potatoes. The self-checkout system has no way to tell. That is the loss Retellect is designed to detect.
StrongPoint
Overkill
Retellect starts with the core layer: item recognition and picklist loss prevention. From there, we tune the system around your checkout process, product catalogue and store data — then extend into additional scenarios where the signal is clear and the value is measurable.
We identify the checkout flows, product groups and loss patterns where Retellect can create the most value.
We test the recognition layer in your real checkout environment: your products, camera setup, checkout events and store conditions.
We track recognition accuracy, false positives, wrong-pick patterns, staff interventions, checkout flow impact and operational value.
Additional checkout scenarios are co-developed around your highest-value loss or flow problems, where the data supports a measurable outcome.
Retellect and StrongPoint are running a proof of concept with one of the largest retail chains in the Baltics — bringing AI capabilities to existing self-checkout lanes across several Vilnius stores.
Read articleRetellect received its first investment from Overkill Ventures Fund following the Accelerator Program — accelerating go-to-market for retail checkout AI.
Read articleA practical conversation about your stores — where Retellect can help, where it can't, and what a clear next step looks like for your team.
