I’m one of the founders of Shaped. We’ve spent the last few years building relevance infrastructure.
Over the time we've noticed a convergence happening: The stack for Search (usually Elastic/OpenSearch) and the stack for Ranking (Vectors + Feature Stores) are merging. Maintaining two stacks for the same math is inefficient.
We just launched Shaped 2.0 to collapse this stack. It treats Relevance like a database problem:
1. Ingest: Connect to Snowflake/Postgres/Kafka.
2. Index: We handle the embeddings (ModernBERT, etc.) and statistical feature engineering.
3. Query: We built a SQL-like language (ShapedQL) to retrieve, filter, and rerank candidates in <50ms.
Instead of writing Python glue code to merge BM25 and Vector results, you can do it in a single query:
SELECT title, description
FROM
semantic_search("$param.query"),
keyword_search("$param.query")
ORDER BY
-- Combine semantic relevance (ColBERT) with a personalized model
I’m one of the founders of Shaped. We’ve spent the last few years building relevance infrastructure.
Over the time we've noticed a convergence happening: The stack for Search (usually Elastic/OpenSearch) and the stack for Ranking (Vectors + Feature Stores) are merging. Maintaining two stacks for the same math is inefficient.
We just launched Shaped 2.0 to collapse this stack. It treats Relevance like a database problem:
1. Ingest: Connect to Snowflake/Postgres/Kafka. 2. Index: We handle the embeddings (ModernBERT, etc.) and statistical feature engineering. 3. Query: We built a SQL-like language (ShapedQL) to retrieve, filter, and rerank candidates in <50ms.
Instead of writing Python glue code to merge BM25 and Vector results, you can do it in a single query:
SELECT title, description FROM semantic_search("$param.query"), keyword_search("$param.query")
ORDER BY -- Combine semantic relevance (ColBERT) with a personalized model
We have a free tier ($300 credits/no-cc) if you want to try the SQL abstraction yourself.
Try it here: https://console.shaped.ai/register