Case study
Transforming e-commerce with AI precision
A fast-growing retailer wanted recommendations that felt personal without slowing the site down or relying on guesswork. We built and shipped a system that did both.
The challenge
Shoppers saw generic suggestions that rarely matched intent. Conversion stalled, and the existing rules engine was impossible to maintain as the catalog grew.
Our solution
Personalized suggestions
A recommendation model trained on live behavior, refreshed continuously.
Dynamic pricing signals
Real-time demand inputs to support smarter promotions.
Predictive insight
Early churn and restock signals surfaced where merchandisers work.
Before
- Generic, rules-based suggestions
- Stalled conversion rate
- High maintenance overhead
After
- Behavior-aware recommendations
- +38% revenue per session
- Self-updating, low-maintenance
Technologies used
- TensorFlow
- Python
- Vector search
- Real-time streaming
- Cloud inference
“ASoc shipped something our last vendor quoted half a year for. The lift was obvious within weeks, and the team explained every decision in plain terms.”
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