02Our Recommendation Engine Process
From Data Review to a Live Personalized Product Recommendation Engine
You see what data the engine uses before we build it.
05Projects We've Built
Ecommerce Projects We Have Built
A selection of Shopify and ecommerce builds shipped for clients, including personalization and product discovery work.
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LET'S DISCUSS
We had a different kind of problem. People were visiting the store, adding products to their carts, and then disappearing before they finished the order. We had tried a few apps and made changes here and there, but nothing really seemed to solve the bigger issue. The team went through the shopping experience with us and pointed out things we hadn't really noticed ourselves. They cleaned up the cart and checkout flow, made the mobile experience much smoother, and helped us make the store feel easier to shop. I’m not going to pretend we fixed everything overnight, but it finally feels like the store is helping the sale instead of getting in the way of it.
Ethan Parker
Founder of Elite Academy
The mobile experience is probably my favourite part of the new site. We had spent a lot of time making sure the desktop version looked right, and the team kept pushing us to think about how everything would actually feel on a phone too. They were right. The navigation is clean, the interactions feel natural, and everything we’ve tested so far works exactly as it should. They also went through the links, forms, integrations, and other details more than once before launch, which I really appreciated. And even after going live, we could still reach out when something came up. The whole process felt friendly, organised, and surprisingly straightforward.
James Whitmore
Head of Marketing of Lumen Outdoor
We had a pretty specific idea of what we wanted our website to become, but I wasn’t sure how easily we could explain all of it to a development team. There were quite a few moving parts, and we were working toward a deadline, so I expected a few things to get lost along the way. That never really happened. They understood what we were trying to achieve, kept us updated throughout the process, and got everything ready within the agreed timeline. The final site feels much easier to navigate, works beautifully on mobile, and is noticeably smoother for our customers. It was a huge relief seeing the original idea actually turn into the website we had in mind.
Michael Bennett
Founder & CEO of Northbridge Consulting
07Work Culture
Why Brands Trust Us With Recommendations
Recommending an out-of-stock product costs a sale, so our engine checks inventory first. These are the habits we bring to every personalized product recommendation engine.
Fits your catalog size
We scope the build to what your data can support.
Uses data you have
We connect to behavior and purchase data already collected.
Never shows sold-out items
Recommendations are filtered by live inventory.
Quiz answers count too
Preference data can feed the engine.
Measured on sales
We track the effect on revenue, not just clicks.
Honest scoping
We tell you what your data can and cannot do.
08Overview
Should I use a recommendation engine app or a custom build for my catalog?
Recommendation engines use collaborative filtering (similar shoppers' purchases), content-based filtering (what a shopper liked), or a hybrid. Apps like Rebuy or Nosto deploy fast but keep behavioral data on their platform. Vendor claims of 26-31% attributed revenue conflate correlation with lift; McKinsey's estimate is a more defensible 5-15% revenue lift from personalization. A custom build wires logic to your catalog, inventory, and quiz data.
- Service
- Product recommendation engine: collaborative/content-based/hybrid logic, live inventory filtering, quiz + browsing data integration
- Starting price
- From $4,999
- Typical timeline
- 4 to 8 weeks
- Platform
- Shopify, WooCommerce, BigCommerce, and custom stores
- Built with
- Collaborative and content-based filtering logic, live catalog/inventory feed, quiz and browsing-behavior data integration
- Experience
- 10+ years and 250+ shipped projects
- Post-launch support
- Ongoing maintenance retainer or one-time handoff
- Communication
- Calls in US, UK, and AU business hours
Who it's for
Stores with a catalog too large to browse well
You have 400 or more SKUs and shoppers regularly leave without finding something relevant to them.
Brands wanting recommendations that respect stock
You're tired of recommending products that are actually out of stock and losing the sale anyway.
Stores that already collect quiz or behavior data
You have quiz answers, browsing history, or purchase data sitting unused that could power better recommendations.
Teams who want to own their recommendation data
You don't want your shopper behavior data locked inside a SaaS app's platform.
Stores that want honest lift numbers
You want a realistic estimate of conversion and AOV impact, not an inflated vendor claim.
Not a fit for
You want a guided questionnaire that asks shoppers direct questions rather than inferring preferences from behavior. Our Product Finder Quiz Development page covers that.
You want a pricing or quote tool, not a product-matching engine. Our Quote Calculator Development page covers that.
You have a small catalog under roughly 100 SKUs where manual merchandising or a low-cost app already works fine.
09Pricing
Transparent Starting Prices for a Product Recommendation Engine
These are starting points in USD. Your final price depends on your catalog size and which data sources feed the engine, and you get a fixed proposal after a free scoping call.
11The Right Technology for Your Project
Technology Stack
We build an AI product recommendation engine that uses browsing behavior, purchase history or quiz answers, connected to your live inventory.
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How does a product recommendation engine work?
It uses collaborative filtering, what similar shoppers bought, content-based filtering, what a shopper already viewed or liked, or a hybrid of both, to automatically surface relevant products instead of showing the same catalog to everyone.
Custom engine or app: which fits my catalog?
Apps like Rebuy or Nosto deploy faster and cost less upfront, but your behavioral data usually stays on their platform. A custom engine fits once you want to own that data, need it wired to quiz answers or live inventory, or have catalog logic an app can't handle.
How much does a recommendation engine cost?
A scoped recommendation engine that connects rules-based or hybrid logic to your catalog and inventory starts from $4,999. A full enterprise-scale machine learning platform is a different order of investment, often $70,000 or more, and isn't what most stores actually need.
Can recommendations use live inventory so nothing out of stock is shown?
Yes, the engine checks live inventory before surfacing a recommendation so out-of-stock products are automatically excluded.
Can it use quiz answers and browsing behavior?
Yes, it can combine quiz answers, browsing history, and purchase data as inputs, so recommendations get more accurate the more signals it has to work with.
How do I measure lift in conversion and AOV?
We set up tracking that separates recommendation-attributed revenue from real incremental lift, so you're looking at actual impact rather than an inflated vendor-style metric.
How long does a build take?
Most builds take 4 to 8 weeks depending on catalog size, how many data sources feed the logic, and how much historical data needs cleaning first.
Can it work with a large catalog of 400 or more SKUs?
Yes, larger catalogs are actually where a recommendation engine earns its keep, since manual merchandising becomes impractical well before 400 SKUs.