Real personalisation. Recommender that converts. Ops that scale.
We apply AI in Spanish retail and ecommerce for native recommender, demand forecasting, automated support and product content. Without depending on generic add-ons that never know your real catalogue.
Standard plugins (Shopify, WooCommerce) do "clients who bought X also bought Y" and little more. They don't know your business or margin.
Without demand forecasting based on history + seasonality + events, there are always missing SKUs and excess SKUs. Both kill margin.
Black Friday, sales, Christmas. Support teams overwhelmed just when there are more sales and worse post-sales experience.
Repetitive descriptions, unpolished photos, little depth. Organic SEO suffers and so does conversion.
Not a list of technologies. Concrete processes where AI delivers measurable euros or hours from the first month.
Based on your catalogue, purchase history, per-SKU margins and business objectives. Not a plugin — a model built for you.
Weekly demand prediction per product and point of sale using history, seasonality and external variables (weather, events).
Answers on availability, sizes, shipping, tracking, returns. Executes actions (size change, cancellation) without escalating to human.
Unique descriptions per SKU based on technical specs, use cases and target keywords. Automatic image retouching with consistent style.
Size change, tracking, return, incident. Resolution without human agent on repetitive queries.
Client clusters based on real behaviour, not demographics. Automatic campaign activation per segment with generative AI.
Automation of picking, incidents, post-sales support with access to real systems.
Info → 02Copy, creatives, attribution, segmentation with own data generation.
Info → 03Recommender, forecasting, basket analysis, price elasticity.
Info → 04Automated support with real backend action execution.
Info →Invented cases with metrics consistent with our real ranges, until a client authorises publishing their own.
Custom model that optimises not just for relevance but for margin and rotation. Different recommendations on home, product page and cart.
SKU prediction with external variables. Automatic replenishment recommended to buyer for review and approval.
Bot with API access to stock, sizes, shipping and tracking. Executes changes and cancellations. Escalates only complex cases.
Our pilots incorporate from design the regulatory requirements that apply to the retail sector in Spain:
Depends on volume. A standard plugin is fine on small catalogues (<500 SKUs) or when purchase history is short. Beyond a certain size, the plugin leaves a lot of ROI on the table because it doesn't know your margin, seasonality or commercial strategy.
A proprietary recommender trains on your catalogue, history and the variables you define (margin, rotation, category synergies). Can optimise by business metric, not just "relevance".
Rule of thumb: if you do >€500k/year online, a proprietary recommender pays back in 4-8 months. Below that, stick with plugin and invest budget in traffic.
The chatbot connects via API to your backend: catalogue, stock, orders, tracking, return policy. It never invents data: if it doesn't have access to information, it escalates to human.
Sensitive actions (cancellations with high amount, out-of-policy returns, refunds) always require human confirmation or fall outside bot scope. Policy is defined case by case with you at pilot start.
Conversations are audited by random sample and low-confidence cases. If the bot gives an incorrect answer, it's labelled and retrained.
Proprietary recommender: 6-10 weeks depending on catalogue complexity and history quality. Typical cost €15-35k.
Per-SKU forecasting: 6-8 weeks for a pilot in a category or channel. Scales to rest of catalogue in 2-4 weeks per batch. Typical cost €12-25k.
Post-sales chatbot with backend access: 5-7 weeks. Typical cost €10-20k.
Yes. We integrate via API with the main platforms: Shopify, Shopify Plus, Prestashop, Magento, Adobe Commerce, WooCommerce, Salesforce Commerce Cloud, VTEX and proprietary platforms. For legacy systems we use webhook integration layer.
Integration is designed in scoping phase (week 1-2 of pilot). If your platform isn't on the list, we verify available endpoints and confirm pilot viability before signing.
Free 30-min diagnosis. We come out with 3-5 use cases prioritised by ROI for your specific retailer.
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