Predictive Behavioral Engine

A fast-growing e-commerce platform needed to move beyond static product listings and generic "customers also bought" suggestions. They required a predictive behavioral engine that could model customer intent across home feeds, product pages, category browsing, and search — driving conversion at every touchpoint.

The targets were aggressive: p95 latency under 200ms, support for 20+ distinct behavioral prediction strategies, multi-tenant isolation for marketplace sellers, and real-time event tracking to continuously improve model quality.

We architected a stateless microservice on FastAPI that leverages Neo4j as a unified graph database and vector store. By combining collaborative filtering, behavioral pattern analysis, and vector similarity search within a single Cypher query engine, we eliminated the complexity of managing separate services.

Daily GDS FastRP embeddings and materialized kNN similarity edges reduce query latency from seconds to milliseconds. A multi-tier Redis caching strategy with differentiated TTLs per endpoint type achieves 70–95% cache hit rates — all deployed on Fly.io with autoscaling from Singapore to US-West.