Neural Commerce Platform

TechRetail was losing customers to competitors with better product recommendations. Their existing rule-based recommendation engine had a 2% click-through rate and was built on stale batch data updated weekly. The engineering team had no ML expertise in-house and needed a production system in under four months.

We built a real-time personalization layer on top of their existing Shopify storefront using a fine-tuned GPT-4 embedding model paired with a vector similarity search engine. Customer browsing events streamed into an AWS Kinesis pipeline, updating user preference vectors in near-real time. The recommendation API was deployed as a serverless Lambda function with a 40ms p99 latency target.

Within eight weeks of launch, conversion rate on recommended products rose 35% compared to the legacy engine. Average order value increased by 18% as customers discovered complementary items. The system handles 200K daily active users with zero cold-start degradation and costs 60% less to operate than the previous vendor solution.