Predict.Engage.Immerse.
Predict
→Suggest genres, categories, and groups to personalise your sports experience
Recommendations
→Add a personalised “For You” section to your product
Personalise
→Let your customers get to the items they want faster with personalised page layouts
Engage
→Make your product more engaging with personalised music recommendations
Find Similar UsersUsers with shared interests discover content they love
Discover Similar ItemsRecommend items based on past interactions
Personalized 'For You' FeedAI-curated feeds tailored to each user
Dynamic Homepage LayoutsOptimize layouts based on engagement
Highlight Trending ContentShowcase popular & emerging content
User-Based FilteringFilter recommendations based on user preferences
Context-Aware AIAdjust recommendations based on time & location
Real-Time UpdatesRecommendations that evolve instantly
Features
Effortless Scaling
Built to handle any traffic on your personalisation experiences.
Plug & Play
Spend time on your content, not tweaking your personalisation.
Minimal Configuration
Scale to millions of users. No complex configuration needed.
Enterprise-ready Security
Best in-class security practice to keep your data safe.
Advanced AI Models
Leverage state-of-the-art AI models for smarter recommendations.
Pricing
Starter
Free
Up to 10k monthly requests
$0.05 per additional request
- 1 Model
- Short term data storage
- High-level usage analytics
- Contexts
Most Popular
Pro
$0/month
Up to 500k monthly requests
$0.05 per additional request
Add-ons (optional):
20x models (+$3500)
Long term data storage (+$90)
Analytics (+$200)
Contexts (+$700)
Dedicated Event Stream (+$1300)
Enterprise
Custom
1,000,000+ monthly requests
$0.02 per additional request
- Everything in Pro plus:
- Custom solutions
- Dedicated support
From the Blog
More →
Product Strategy
The Case Against Over-Optimizing Recommendation Engines
Why overly precise recommendation models can harm user satisfaction and what to do instead.
Product Strategy
Why Recommendation Engines Are Core to Modern Apps
A look at how recommendation systems power user engagement, with examples from TikTok and beyond.
Engineering
Understanding Vectors and Embeddings in Machine Learning
A plain-language explanation of how vectors and embeddings power modern ML recommendation systems.
Contextually relevant results. Any scenario. The right context. From advertising to recommendations. Your imagination.