Most QSR and retail teams already know upselling works, but Odoo's native Point of Sale gives cashiers almost nothing to act on. The base POS supports optional products on product.template and combo choices in restaurant mode, but those are static, manually curated lists — they don't learn from what is actually in the current cart, they don't change by daypart, and they never reach a self-service kiosk in a suggestive way. The result is inconsistent upselling that depends entirely on how well each cashier remembers the promo of the week, and a kiosk flow that takes the order without ever asking "want to make it a combo?"

ECOSIRE builds a dedicated POS module that adds a real-time recommendation engine on top of your existing catalog. On the backend we add Odoo models (models.Model) that store recommendation rules, affinity pairs, and per-daypart configuration, with @api.depends computed scores and a service layer that reads historical pos.order / pos.order.line data to derive frequently-bought-together associations (a market-basket / co-occurrence pass you can re-run as a scheduled ir.cron automated action). The POS front end is extended through OWL components and patches to the point-of-sale JavaScript so that, as products are added to the current order, the screen surfaces contextual add-on and combo cues to the cashier — and the same engine drives suggestive prompts on the self-order kiosk screen. Suggestions can be weighted by time of day, and optionally by a weather signal pulled through a guarded outbound call, so cold-drink or hot-item prompts shift with conditions. Everything respects Odoo access control: recommendation configuration is governed by ir.model.access.csv plus record rules so store managers edit rules while cashiers only consume them, and all analytics honor multi-company boundaries.

Because it is native Odoo, the module ships as a proper addon with its own __manifest__.py declaring dependencies on point_of_sale (and pos_restaurant where combos apply), migration-safe schema, XML/OWL views for the configuration screens, and a QWeb "upsell performance" report so you can see attach-rate and incremental revenue per suggestion. Recommendation data is exposed over the standard XML-RPC / JSON-RPC API so external analytics or a headless kiosk can read the same suggestions the POS uses. We target Community and Enterprise on Odoo 17.0, 18.0, and 19.0 — the recommendation logic and OWL components are written against your exact version so nothing depends on undocumented internals, and we flag any Enterprise-only touchpoints (such as advanced kiosk/self-order screens) up front during scoping.

This is a build-to-order engagement, not an instant apps.odoo.com download. After a short scoping call we confirm your Odoo version, edition, POS/kiosk topology, and which upsell behaviors matter most, then produce a fixed scope. Typical delivery is one working week from confirmed scope. We develop against a staging copy of your database, run UAT with your team, and only then install on production with a documented rollback path. You receive the full source, the git repository, technical and user documentation, a training session, and a post-go-live support window — so the engine keeps performing as your menu and traffic patterns change.