Ingest features, generate embeddings, and compute similarity with user intent baked in. Built for low latency scoring and rapid iteration on ranking logic.
High‑throughput embedding service with optional normalization and max‑length controls. Ideal for retrieval, clustering, and semantic filters.
Blend user likes/dislikes with item vectors via centroid scoring. Defensible math, simple knobs, production‑ready latencies.
Optional market and activity features (liquidity, volume, holders) for richer ranking under drift.