AI-Powered E-Commerce

AI Product Recommendation Engines

Product blocks that pick themselves from live behaviour and catalog data: related items, frequently bought together, recently viewed, and personalized rows for each shopper, all inside rules you set for margin and stock.

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AI Product Recommendation Engines

Hand-picked related products do not scale

Manually curated related products work for a small catalog and then quietly rot: items go out of stock, new products never get linked, and seasonal picks stay up all year. Shoppers notice, and the blocks stop earning their space on the page. A recommendation engine keeps those slots filled from current data, but it only helps if it respects real constraints like margin, stock cover, and the products you never want to push.

What we build

  • Recommendation blocks for the pages that matter: home, product, cart, and post-purchase
  • Model types matched to each slot: similar items, frequently bought together, and personalized-for-you rows
  • A rules layer for margin floors, stock thresholds, brand exclusions, and manual pins
  • Fallbacks for new products and first-time visitors with no history
  • An events pipeline that captures views, searches, add-to-cart, and purchases cleanly
  • A/B testing so each block revenue contribution is measured, not assumed

How the build runs

  1. 1

    Audit data and slots

    2–3 days

    We check the quality of your catalog and event data and agree which page slots the engine will fill.

  2. 2

    Wire the events

    3–5 days

    View, search, cart, and purchase events are captured consistently, since the model is only as good as this feed.

  3. 3

    Fit and tune models

    1 week

    Each slot gets the model type that suits it, tuned against your history and checked for obvious failure cases.

  4. 4

    Apply your rules

    2–3 days

    Margin, stock, and exclusion rules are layered on top so recommendations respect merchandising decisions.

  5. 5

    Test and hand over

    3–5 days

    Blocks roll out as A/B tests, and you get a dashboard showing what each one contributes.

How we work

How we deliver

  1. 1

    Discovery & Scoping

    1-3 days

    We start by understanding the actual problem and your existing systems, not just the brief.

  2. 2

    Architecture & Plan

    2-4 days

    We map the technical approach and integration points before any code gets written.

  3. 3

    Build

    1-8 weeks, scoped to your project

    AI-augmented development that moves fast without skipping review.

  4. 4

    Test & Launch

    2-3 days

    Real testing against real scenarios before it touches a live user.

  5. 5

    Tune & Support

    Ongoing

    We monitor and refine in the weeks after launch. This isn't a handoff and disappear.

What you get out of it

  • Recommendation slots that stay current without manual upkeep
  • Larger average orders where the frequently-bought-together logic fits
  • New and out-of-stock products handled sensibly instead of surfaced badly
  • A measured read on which blocks earn their place and which do not

Questions we get about this

Usually the product page, the cart, and a post-purchase or order-confirmation slot, with an optional row on the homepage for returning visitors. We start with the slots most likely to pay off for your catalog and add from there.

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