Recommendation engines drive 30%+ of revenue at companies like Amazon and Netflix. Our recommendation engines bring that personalization layer to mid-market commerce, content, and SaaS without data science overhead.
Recommendation engines is a key service from demelos for enterprise teams ready to ship AI to production.
According to Forrester analytics research, enterprise AI adoption continues to accelerate across mid-market and Fortune 500 companies.
AI recommendation engines that learn what each individual customer wants and surface it at the right moment — boosting revenue per user, conversion rates, and retention.
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Most e-commerce, content, and SaaS sites show every visitor essentially the same thing. The homepage doesn’t know if you’re a first-time buyer or a 10-year customer. The product page suggests bestsellers, not what’s actually right for YOU. The email blast goes to everyone.
Meanwhile, the businesses crushing the competition — Amazon, Netflix, Spotify, Stitch Fix — make every customer feel known. The products you see, the content suggested, the timing of every nudge, all personalized. This isn’t magic. It’s recommendation engines.
The good news: recommendation engines are dramatically easier to build now. Modern AI lets us learn customer preferences from behavior, even with limited data, and serve recommendations in real time. The lift on revenue per user is typically 15-40%.
Book a free 30-minute AI Audit. We’ll identify 1-3 specific opportunities with clear ROI estimates. No pitch, no slides.
Book Your Free Audit →We build custom recommendation engines tuned to your business — not off-the-shelf algorithms but systems that understand the specifics of what you sell, what your customers care about, and what ‘good’ looks like in your context.
Then we wire them into every touchpoint that matters — homepage, product pages, search results, emails, push notifications, in-app — so each customer sees what’s most likely to convert THEM.
Every system we build has measurable success criteria from day one. We don’t ship pretty UIs that no one uses — we ship outcomes.
Your team gets a working solution that fits the way you actually run, with full documentation, training, and our team on call when something needs tuning.
We map what signals you’re already capturing (or could be) — clicks, purchases, time spent, search queries, returns.
We pick the right algorithms for your data and goals — collaborative filtering, content-based, hybrid, contextual bandits.
Homepage, product pages, search, email, app — anywhere you can serve a personalized recommendation.
We measure incremental revenue from recommendations vs. a control. Numbers don’t lie.
Product recommendations, search ranking, cross-sell bundles, personalized email merchandising, retargeting ad creative.
Article suggestions, video recommendations, content discovery, paywall optimization, push notification timing.
Feature suggestions, in-app onboarding flows, expansion opportunities, content recommendations within the product.
Two-sided personalization — buyers see relevant inventory, sellers see relevant buyers.
Most AI projects deliver one-time gains. Ours compound. As more of your operations run through AI systems, the data they collect makes them sharper week over week.
Six months in, you’ll be running circles around competitors who are still doing the work the old way.
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Not off-the-shelf. Each model is trained on your data and tuned for your business goals.
Recommendations update with every interaction — they get smarter as the customer browses.
We measure incremental revenue from recommendations. Typically 15-40% lift on revenue per user.
Less than you think. Modern AI can produce useful recommendations from a few thousand interactions. More data = better recommendations, but you can start small.
Yes — we use cold-start strategies (popularity, content-based) for new users and switch to personalized as they engage.
Yes — Shopify, BigCommerce, Magento, custom platforms, headless setups. We work with what you have.
Incremental revenue from recommendation surfaces, A/B tested against your current setup. We report monthly with hard numbers.
Done well, no — they feel understood. We help calibrate aggressiveness so personalization feels useful, not invasive.
Initial recommendations live in 4-6 weeks. Full lift typically visible within 90 days as the model learns from real traffic.
Recommendation engines drive 30%+ of revenue at companies like Amazon and Netflix. Our recommendation engines bring that same personalization layer to mid-market commerce, content, and SaaS — without the data science team overhead.
Modern recommendation engines combine collaborative filtering, content-based ranking, and LLM context. Our recommendation engines integrate with your CDP and analytics stack, delivering measurable lift within 60 days.
Related: Predictive analytics · Content engines · Free audit · AI strategy
Recommendation engines drive 30%+ of revenue at companies like Amazon and Netflix. Our recommendation engines bring that same personalization layer to mid-market commerce, content, and SaaS — without the data science team overhead.
Modern recommendation engines combine collaborative filtering, content-based ranking, and LLM context. Our recommendation engines integrate with your CDP and analytics stack, delivering measurable lift within 60 days.
Related: Predictive analytics · Content engines · Free audit · AI strategy
