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At Shaped, we believe that while retrieval (finding 1,000 items) is largely a solved problem, relevance (finding the best 10) is still an infrastructure nightmare.

Today, we are officially launching ShapedQL, a declarative SQL language and real-time engine designed to collapse the entire ranking and retrieval stack into a single query.

The Problem: The "Frankenstein" Stack

Most engineering teams today are forced to maintain what we call a "Frankenstein stack."

To build a high-quality "For You" feed, a personalized search bar, or an AI agent with long-term memory, you typically have to glue together a dozen fragmented tools:

A Vector Database (like Pinecone) for semantic retrieval.
A Search Engine (like Elasticsearch) for keyword matching.
A Feature Store (like Redis) to hold user session data.
Thousands of lines of Python "spaghetti code" to handle business logic, filtering, and re-ranking.

The result is a "house of cards." It’s stateless, slow to iterate on, and impossible to debug. When a user asks, "Why was this item ranked first?" engineers usually don’t have an answer.

The Solution: From Documents to Decisions

ShapedQL was built to move the industry from document retrieval to real-time decisions.

Unlike traditional search engines that are stateless by design, ShapedQL treats "User Context" as a first-class citizen. It doesn’t just look for items that are similar to a query; it finds items that a specific user is most likely to engage with right now.

We’ve collapsed the entire relevance lifecycle into a 4-stage pipeline that you can define in a single SQL query:

Retrieve: Fetch candidates from multiple sources (Hybrid Search, Social Graphs, or Trending lists).
Filter: Apply hard business constraints (e.g., "only show items in stock and under $200").
Score: Rank results using real-time machine learning models optimized for your business goals (Clicks, Conversions, or Watch Time).
Reorder: Optimize the final list for Diversity and Exploration, ensuring the user experience stays fresh and avoids repetition.

ShapedQL in Action

Here is what a modern discovery feed looks like in ShapedQL. This replaces roughly 2,000 lines of traditional backend infrastructure:

SELECT video_id, creator_name
FROM 
  trending_videos(),                   -- 1. Global popularity
  following_network("$user_id")        -- 2. Social graph
WHERE 
  NOT previously_watched("$user_id")   -- 3. Stateful filtering
ORDER BY 
  p_watch_time(user, item)             -- 4. ML-powered scoring
REORDER BY 
  diversity(creator_name)              -- 5. List-wise optimization

More than just a Query Language

ShapedQL isn't just a syntax; it’s an end-to-end platform that automates the heavy lifting of data engineering and MLOps:

Real-Time Connectors: Sync data from Snowflake, BigQuery, Kafka, or Segment in milliseconds.
Generative Enrichment: Use Gemini-powered LLMs to automatically tag images, clean messy product descriptions, and normalize data on the fly.
Automated MLOps: Shaped continuously trains and fine-tunes your ranking models based on live user behavior, so you never have to manage a training pipeline again.

Real-World Impact

We’ve already seen the power of this approach with our customers. By migrating their legacy search infrastructure to Shaped, one customer was able to replace a massive, 3000 elastic search codebase of rules with a 30 line ShapedQL query.

The result? An 11% lift in search conversions and a 10x increase in experimentation velocity. They can now test new ranking theories in minutes, not weeks.

We’re live on Product Hunt!

Today is a huge milestone for the team. We are sharing ShapedQL with the world on Product Hunt and we'd love your support.

Check out our new Free Playground (no login required) to see the engine in action and join the discussion:

👉 https://www.producthunt.com/products/shaped/launches/shapedql
👉 https://playground.shaped.ai/

We can't wait to see what you build.

Introducing ShapedQL: The SQL Engine for Search, Feeds, and AI Agents

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Dnyaneshwar Shekade