Stop gluing Pinecone, Redis, and Python scripts together. ShapedQL is the SQL engine for relevance - powering "For You" feeds, Search, and RAG memory in minutes. It compiles simple SQL into real-time ranking pipelines that retrieve, filter, score, and reorder results based on live user behavior. Replace thousands of lines of infra with 30 lines of SQL. With native multi-modal embeddings and automated MLOps, ShapedQL helps you build real-time decisions, not just document retrieval.
I'm Tullie, the founder and CEO of Shaped. Previously I was a researcher at Meta AI, leading several ML teams including one focused on Instagram Reels and Ads video ranking. I also created PyTorchVideo and was a core contributor to Pytorch Lightning.
We built ShapedQL because we realized that while retrieval has become easier (thanks to Vector DBs), ranking and relevance are still incredibly hard.
Most engineering teams we talk to are stuck maintaining a "Frankenstein" stack. To build a "For You" feed or give an AI Agent personalized memory, they have to glue together a vector database, a feature store (like Redis), a reranking service, and thousands of lines of Python spaghetti code.
We built ShapedQL to turn that "house of cards" into a single interface.
ShapedQL is a domain-specific SQL dialect that compiles down to a high-performance, multi-stage ranking pipeline. With a single query, you can define the four stages of modern relevance: 1. Retrieve: Fetch candidates from multiple sources (Hybrid Search, Collaborative Filtering, Trending). 2. Filter: Apply hard constraints (e.g., "in stock" or "within 50 miles"). 3. Score: Rank results using real-time ML models (optimizing for clicks, purchases, or watch time). 4. Reorder: Enforce diversity so your users (or Agents) don't see the same 5 items repeatedly.
We're seeing teams reduce 2,000+ lines of maintenance code down to ~30 lines of ShapedQL, while shipping features like "Cart Upsell" or "Agent Memory" in days instead of months.
If you're not a fan of SQL you can also choose from Python or Typescript SDK's.
I'd love to hear your feedback and answer any questions about the syntax or how it works under the hood! 🚀
Congratulations on the new launch! We’re actually working on a travel AI project. I’ve sent it to the team to check out.
Does Shaped support custom real-time feature injections during the ranking process, or does it rely solely on pre-indexed data?
Feature freshness vs latency is the classic trap. Most teams pick caching and accept day-old features, or go streaming and deal with Kafka/Flink complexity. Shaped's unified data layer handling both batch and real-time in a single schema is the interesting move here... curious how much of that freshness gap it actually closes in practice.
Replacing thousands of lines of infra code with just 30 lines of SQL sounds too good to be true, but it would definitely save weeks of development time. I wonder how flexible that SQL actually is like, can it handle really complex custom scoring rules, or are you limited to their pre-built ranking logic?
Honestly, trying to glue Redis, Pinecone, and Python scripts together is such a headache, so seeing a SQL engine handle all that 'relevance' work is a huge relief. I’m really curious if it actually handles the real-time ranking as fast as they claim, especially when you’ve got millions of users triggering live behavior.
Congrats on the launch @tullie_murrell ! How do teams actually iterate on relevance once they’re on ShapedQL? Is the biggest win faster experimentation, or just not having to touch infra every time they tweak ranking logic?
This is sick - more teams able to build better feeds. Are you able to share any feeds Shaped is powering that you're particularly proud of?
Hey Tullie, that Frankenstein stack image is painfully accurate. Was there a specific team you talked to where you saw them drowning in that glue code and thought this is way too complicated for what should be a solved problem?
Real-time retrieval and ranking tends to break at scale on feature freshness and training serving skew when event volume spikes and backfills happen.
Best practice is strict offline to online feature parity with a streaming fed online feature store plus impression logging for eval and safe shadow or canary rollouts.
How does ShapedQL handle feature definitions and model versioning plus A B testing while keeping low p99 latency across retrieve filter score reorder stages?
About ShapedQL on Product Hunt
“The SQL engine for search, feeds, and AI agents”
ShapedQL launched on Product Hunt on January 27th, 2026 and earned 213 upvotes and 22 comments, placing #5 on the daily leaderboard. Stop gluing Pinecone, Redis, and Python scripts together. ShapedQL is the SQL engine for relevance - powering "For You" feeds, Search, and RAG memory in minutes. It compiles simple SQL into real-time ranking pipelines that retrieve, filter, score, and reorder results based on live user behavior. Replace thousands of lines of infra with 30 lines of SQL. With native multi-modal embeddings and automated MLOps, ShapedQL helps you build real-time decisions, not just document retrieval.
ShapedQL was featured in Software Engineering (42.3k followers), Developer Tools (511k followers) and Artificial Intelligence (466.2k followers) on Product Hunt. Together, these topics include over 158.6k products, making this a competitive space to launch in.
Who hunted ShapedQL?
ShapedQL was hunted by Garry Tan. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how ShapedQL stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Garry Tan
Hi Product Hunt! 👋
I'm Tullie, the founder and CEO of Shaped. Previously I was a researcher at Meta AI, leading several ML teams including one focused on Instagram Reels and Ads video ranking. I also created PyTorchVideo and was a core contributor to Pytorch Lightning.
We built ShapedQL because we realized that while retrieval has become easier (thanks to Vector DBs), ranking and relevance are still incredibly hard.
Most engineering teams we talk to are stuck maintaining a "Frankenstein" stack. To build a "For You" feed or give an AI Agent personalized memory, they have to glue together a vector database, a feature store (like Redis), a reranking service, and thousands of lines of Python spaghetti code.
We built ShapedQL to turn that "house of cards" into a single interface.
ShapedQL is a domain-specific SQL dialect that compiles down to a high-performance, multi-stage ranking pipeline. With a single query, you can define the four stages of modern relevance:
1. Retrieve: Fetch candidates from multiple sources (Hybrid Search, Collaborative Filtering, Trending).
2. Filter: Apply hard constraints (e.g., "in stock" or "within 50 miles").
3. Score: Rank results using real-time ML models (optimizing for clicks, purchases, or watch time).
4. Reorder: Enforce diversity so your users (or Agents) don't see the same 5 items repeatedly.
We're seeing teams reduce 2,000+ lines of maintenance code down to ~30 lines of ShapedQL, while shipping features like "Cart Upsell" or "Agent Memory" in days instead of months.
If you're not a fan of SQL you can also choose from Python or Typescript SDK's.
I'd love to hear your feedback and answer any questions about the syntax or how it works under the hood! 🚀