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Multi-Tower Architecture
Captures user, item and context signals for superior relevance.
Multi-Tower Content Recommendation
Personalisation that drives engagement and revenue.
How it works
- 1
Collect Signals
Ingest clicks, dwell time and contextual features in real time.
- 2
Train Towers
Multi-tower model learns user, item and context embeddings.
- 3
Serve & Test
Real-time ranking API delivers recommendations with live A/B metrics.
Impact numbers
Typical outcomes our clients see
25%
CTR uplift
18%
Session length increase
12%
Revenue per visit
Why organisations choose this solution
Generic “most popular” feeds ignore user interests, limiting engagement and monetisation potential.
Key capabilities
What makes this accelerator stand out
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Vector Embeddings Store
User & item embeddings stored in a high-performance vector database for millisecond retrieval.
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Real-Time Features
Updates embeddings within seconds of user activity.
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Online A/B Testing
Built-in framework to measure lift and iterate quickly.
Frequently asked questions
Does it support cold-start items?
Yes – metadata vectors and hybrid retrieval cover new items until engagement builds.