Shaped is a purpose-built platform that turns raw, real-time behavioral data\u2014clicks, views, purchases, searches\u2014into high-performance ranking, recommendation and discovery experiences. Instead of generic vector databases or black-box SaaS, it gives product and data teams a single system to connect event streams, automatically build multi-objective models and serve ultra-low-latency, deeply-personalized results anywhere in their application.<\/p>\n
Key features
\n1. Real-time behavioral understanding \u2013 ingests Kafka, Kinesis, Segment, Snowflake, BigQuery or S3 events, updates user and item embeddings within seconds and guarantees < 25 ms p99 serving latency through a Rust-based inference core.
\n2. Multi-objective optimization \u2013 combines multiple business goals (CTR, revenue, margin, seller diversity, freshness) in one model and lets teams weight or A\/B them on the fly without retraining.
\n3. No-code + full-code workflows \u2013 point-and-click connectors for Shopify, Spotify, YouTube, Netflix-style UIs, plus open Python SDK and declarative YAML configs for custom feature engineering and model architectures.
\n4. Production-grade MLOps \u2013 automates feature stores, cold-start handling, online\/offline evaluation, shadow deployments and auto-scaling Kubernetes endpoints so data scientists ship once and never babysit.
\n5. First-class personalization APIs \u2013 drop-in replacement for Elasticsearch, Solr or Postgres ranking; returns ordered rows, JSON or rendered widgets with explainability scores for every recommendation.
\n6. Enterprise security \u2013 SOC 2 Type II, single-tenant VPC, row-level access controls, PII redaction and GDPR delete endpoints.
\n7. Transparent pricing \u2013 usage-based on events ingested and API calls served, with a forever-free developer tier and no hidden model-training surcharges.<\/p>\n
Shaped is used by marketplaces, retailers, social apps and media companies to increase conversion 18\u201335 % and cut engineering time from months to days, all without shipping internal ML infrastructure.<\/p>","protected":false},"author":1,"template":"","listivo_14":[434],"listivo_8605":"","listivo_8606":[""],"class_list":["post-12739","listivo_listing","type-listivo_listing","status-publish","hentry","listivo_14-ai-other-tools","listivo_8605-freemium","listivo_8606-web"],"listivo_145":["https:\/\/www.cogainav.com\/wp-content\/uploads\/2025\/09\/Shaped-Recommendations-and-Search.webp"],"listivo_8661":"https:\/\/www.shaped.ai\/","_links":{"self":[{"href":"https:\/\/www.cogainav.com\/it\/wp-json\/wp\/v2\/listings\/12739","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.cogainav.com\/it\/wp-json\/wp\/v2\/listings"}],"about":[{"href":"https:\/\/www.cogainav.com\/it\/wp-json\/wp\/v2\/types\/listivo_listing"}],"author":[{"embeddable":true,"href":"https:\/\/www.cogainav.com\/it\/wp-json\/wp\/v2\/users\/1"}],"version-history":[{"count":2,"href":"https:\/\/www.cogainav.com\/it\/wp-json\/wp\/v2\/listings\/12739\/revisions"}],"predecessor-version":[{"id":12742,"href":"https:\/\/www.cogainav.com\/it\/wp-json\/wp\/v2\/listings\/12739\/revisions\/12742"}],"wp:attachment":[{"href":"https:\/\/www.cogainav.com\/it\/wp-json\/wp\/v2\/media?parent=12739"}],"wp:term":[{"taxonomy":"listivo_14","embeddable":true,"href":"https:\/\/www.cogainav.com\/it\/wp-json\/wp\/v2\/listivo_14?post=12739"},{"taxonomy":"listivo_8605","embeddable":true,"href":"https:\/\/www.cogainav.com\/it\/wp-json\/wp\/v2\/listivo_8605?post=12739"},{"taxonomy":"listivo_8606","embeddable":true,"href":"https:\/\/www.cogainav.com\/it\/wp-json\/wp\/v2\/listivo_8606?post=12739"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}