AI for online retail
that doubles your conversion rate

Product Q&A Bot (RAG)

Answers about a product from a real source: description, specification, reviews.

• Catalogue and review indexing
• Product comparison in conversation
• "Who is this best for?" as an answer
• Semantic search instead of keywords

Personalization Engine

Recommendations driven by behaviour, not just "customers also bought".

• Real-time behavioural signals
• Collaborative + content-based
• Explainable recs ("because you viewed…")
• Built-in A/B testing

Cart Recovery Agent

An AI agent that sends a personal message at the right moment for that buyer.

• Identifies why the cart was abandoned
• Tailored offer (coupon, size, alternative)
• WhatsApp / email / SMS together
• Measurable ROI per campaign

Smart Returns & Sizing

Fewer returns through size recommendation and understanding why items come back.

• Sizing recommendations
• Automatic catalogue of return reasons
• An alternative offered before the return
• Insight for the catalogue and for buyers

Multilingual Storefront

One store, five languages, no localisation team.

• Automatic translation of product descriptions
• Brand terminology preserved
• Automatic RTL/LTR
• Customer service in every language

Fraud & Chargeback AI

Suspicious orders identified before they ship.

• Device fingerprinting
• Velocity checks
• Fraud pattern detection
• Automatic chargeback representment

Shopify / WooCommerce

An official app or plugin, deployed in one click and attached to your existing admin menu.

Magento / Salesforce Commerce

Deep integration over REST/GraphQL, with multi-store and B2B catalogue support.

Custom .NET / Node Stack

If your stack is custom, we build a dedicated SDK or webhooks with full documentation.

Smart Sizing & Returns Reduction

What the industry reports: sizing recommendations built on collaborative signals plus fit data cut returns by 10-25% in tagged categories (Forrester Apparel Retail 2024).

Where we come in: designing the fit-vector model from sales and returns data, integrating it in the cart before checkout, and an A/B framework that proves the lift before a full rollout.

• Model based on historical returns, so no cold start
• Built-in A/B testing: proof before scale
• Per-brand calibration, not "one model fits all"
• Built for multi-currency and multi-market

Cart Recovery Agent

What the industry reports: AI-driven cart recovery with channel and timing personalization brings back 20-40% of abandoned carts against 3-8% for a generic email campaign (Baymard 2024 cart abandonment study).

Where we come in: a classifier for the reason behind the abandonment (price, availability, UX friction), channel choice (WhatsApp, email, popup) from behavioural signals, ROI tracking per campaign.

• Channel chosen from behaviour, not "all channels at once"
• WhatsApp integration where permitted (GDPR-safe)
• An attribution model, so we know whether it worked
• Backoff logic: no five messages to the same customer

Product Q&A RAG

What the industry reports: product Q&A over catalogue and reviews answers 70-85% of questions without an agent and lifts conversion by 10-20% on product pages (Bain Conversational Commerce 2024).

Where we come in: RAG over the catalogue, reviews and spec sheets, with the source cited (no inventing "blue" when there is no blue), and escalation to an agent only when confidence is low.

• No hallucinations: answers only from a verified source
• Real-time inventory awareness
• Multi-language (HE/EN/RU/AR)
• Integration with Shopify, WooCommerce, Magento, custom .NET