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Retail Intelligence Platform

Client
Leading business supplier in Canada
Role
Sole engineer and architect
Status
In production, ongoing
Stack
FastAPI, ASP.NET Core, React, PostgreSQL, LLM APIs, Magento 2, Odoo

The situation

The store runs a large catalog across Canada and the US. Hundreds of thousands of pages and products meant SEO, pricing and supplier updates could not keep up by hand, while orders flowed into Odoo.

I started with SEO and indexing, then automated each area in turn as the client asked for the next.

What I built

System map: seven data sources, including Magento and Odoo, feed a platform core of rule engine with AI, job engine and publishing rules, which pushes changes to Magento and produces insights and dashboards. INPUTS PLATFORM CORE OUTPUTS Google Search Console Sitemap and crawl Magento catalog Odoo ERP Competitor prices Supplier feed Store analytics Rule engine + AI Job engine Publishing rulesauto or approval Changes to Magentocontent · meta · prices · products Insights and BI dashboardschannels · pages · orders System map: seven data sources, including Magento and Odoo, feed a platform core of rule engine with AI, job engine and publishing rules, which pushes changes to Magento and produces insights and dashboards. INPUTS Search Console Sitemap and crawl Magento catalog Odoo ERP Competitor prices Supplier feed Store analytics PLATFORM CORE Rule engine + AI Job engine Publishing rules: auto or approval Changes toMagento Insights andBI dashboards
Seven sources in, one core, two outputs. Changes go live automatically within set rules, or after approval.

SEO diagnosis

The problem
Far too many pages to check and fix SEO and indexing by hand.
How it works
Pulls every URL from Search Console, sitemaps, Magento and a crawl. Rules and AI diagnose each page and rank the fixes.
Result
SEO and indexing improve automatically, without manual page-by-page audits.

Content enrichment

The problem
Many products had thin or missing descriptions and metadata.
How it works
AI drafts SEO-ready descriptions and metadata from each product’s own data.
Result
Content publishes to Magento automatically by rule, or after review.

Pricing intelligence

The problem
Setting margins by hand across a huge catalog, with no view of competitor prices.
How it works
Calculates margins from costs, scans competitor prices and analyzes each product’s market position.
Result
A solid margin on every product. Price changes go live within your limits, or after approval.

AI store assistant

The problem
In a large catalog, shoppers struggle to find the right product.
How it works
Shoppers describe what they need. The AI searches the catalog, shows details and adds items to the cart.
Result
Shoppers find and buy without endless browsing.

Supplier integration

The problem
Supplier product data was typed into Magento by hand.
How it works
Learns each supplier’s data flow and collects it through APIs, feeds, crawling or scraping, then matches it to store products.
Result
New and updated supplier products reach Magento automatically.

Conversion intelligence

The problem
No clear view of which channels bring buyers, or where visitors drop off.
How it works
Tracks every interaction in order: page views, product views, cart actions, checkouts and orders, by channel.
Result
See where visitors come from, what they buy, how many buy and the revenue each channel brings.

How AI is used safely

  • Rules handle the common cases first. AI handles only what the rules can’t decide.

  • Each type of change has a rule: publish automatically within safe limits, or wait for a person to approve.

  • Rate limits protect the live store from heavy crawls and bulk updates.

  • Every push is audit-logged, with login and multi-store access control.

Have a store with the same problems?

Tell me about your systems and what you’d like to improve. I reply within one working day.

Book a 30-min technical call