AI-Powered Recommendation Engine
Show customers what they want before they know it
Technologies we work with
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The Problem
Your e-commerce platform, media site, or content app serves the same experience to every visitor—while competitors deliver personalized recommendations that lift average order value and double engagement. Manual curation can't scale to thousands of SKUs, and rule-based merchandising can't adapt to individual behavior in real time. Shoppers leave without finding what they wanted. Revenue stays on the table.
- Genuinely relevant recommendations, not random suggestions
- Measurable lift in average order value or engagement
- Cold-start handling for new users and products
- A/B testing framework for continuous improvement
The Solution
E-commerce brands, marketplaces, media platforms, and content apps with a catalog exceeding 500 SKUs or a content library exceeding 1,000 items—and at least 90 days of user interaction data to learn from. Teams ready to move from manual curation to AI-driven personalization that improves with every click.
- User-item interaction data pipeline
- Collaborative and content-based hybrid model
- Real-time API with sub-50ms latency
- A/B testing integration
See it in action
2 min overview
Why Businesses Choose Our Solutions
Purpose-built solutions designed to deliver measurable, real-world results
Measurable Revenue Lift, Not Vague Personalization
We baseline your current average order value, CTR, and conversion rate before deployment. Every recommendation model is evaluated against your pre-existing metrics—you see the exact commercial lift delivered, not a demo dashboard.
Relevant From the Very First Visit
Cold-start logic uses product attributes, categories, and trending signals to serve new users meaningful recommendations immediately. You don't wait 6 months for interaction history before the system starts working.
Your Business Rules Stay in Control
Promotions, inventory clearance, seasonal campaigns, and brand partnerships can boost or bury items without retraining the model. The algorithm handles default ranking—your merchandising team controls the exceptions.
Scales to Millions of Items Without Degrading
Built on approximate nearest-neighbor search and precomputed embeddings, recommendation latency stays under 50ms whether you have 1,000 or 10 million products. No rearchitecting required as your catalog and user base grow.
Key Features
Everything You Need to Manage & Grow
Core capability
Collaborative Filtering Engine
Learns from user-item interactions across your entire catalog — 'customers like you also bought' logic that improves automatically as your user base and purchase history grow.
Core capability
Real-Time Personalization
Recommendations computed on the current session, cart contents, and live browsing context — no stale daily-batch suggestions that miss what the user is looking at right now.
Cold-Start Handling
Content-based fallback uses product attributes (category, price, tags) when a user or SKU has no interaction history — new users and new products get meaningful recommendations from day one.
A/B Testing Framework
Run controlled experiments on algorithm variants, placement logic, and ranking weights. Measure lift in CTR, average order value, and revenue-per-visitor with statistical confidence before promoting a winner.
Explainable Recommendations
Every suggestion surfaces a human-readable reason ('Because you viewed X', 'Trending in your category') — transparent logic that builds user trust and consistently lifts click-through rates.
Merchandising Controls
Override the ML model with manual boost and bury rules for promotions, inventory clearance, brand partnerships, and seasonal campaigns — business logic layered cleanly on top of the algorithm.
Do these capabilities fit your use case? Let's map out your project.
How It Works
From kickoff to live in 3 clear steps
Day 1
Discovery Call
30-minute call to understand your goals, current setup, and success criteria. We come prepared — no generic questionnaires.
Day 2–3
Custom Proposal
Tailored scope, pricing, and delivery milestones within 48 hours. You review and approve before anything begins.
Ongoing
Build & Launch
Hands-on delivery with weekly checkpoints. We don't ship until you're confident — then we stay on for support.
Success Stories
Personalization that pays for itself
Real outcomes from real clients
+22%
Avg. order value
E-Commerce Store
Increased average order value by 23% within 60 days of deploying personalised product recommendations on product and cart pages.
3×
Click-through on recs
Streaming Platform
Boosted average session watch time by 41% by replacing a popularity-based feed with a context-aware recommendation model per user segment.
+18%
Repeat purchase rate
B2B Marketplace
Raised supplier-to-buyer match rates by 37%, reducing the average time-to-first-transaction for new buyers from 11 days to 4 days.
Want results like these? Start a conversation.
Ready to show every customer exactly what they want?
Let's build a solution tailored to your business.
Frequently Asked Questions
How long does implementation take?
Most projects go live within 4–8 weeks depending on scope. After our discovery call you'll receive a timeline with exact milestones.
What happens after the project launches?
Every project includes post-launch support. We monitor, fix, and optimize — you're not left on your own after delivery.
Can I start with a smaller scope and expand later?
Yes. We design every solution to be modular — start with what you need now and scale as your business grows.
