Papr Graph transforms semantic embeddings into graph-native embeddings with one API call. It encodes temporal, topical, and other dimensions within any embedding, helping agents retrieve answers based on correctness, not just semantic closeness.
We built Papr Graph after seeing AI agents fail in production. The model wasn't the problem — retrieval was. Multi-hop questions, versioned policies, relational data — flat vector search breaks on all of it.
Vector search ranks by semantic closeness. But closeness ≠ correctness. A doc saying "aspirin reduces heart attack risk" and one saying "aspirin causes stomach bleeding" rank nearly identical — they're both about aspirin. For an agent making a recommendation, that's the difference between helpful and harmful.
Papr Graph is a graph-native embedding that sits between your existing embeddings and your agent. It encodes structured signals — topic, time, intent, entities, anything you define — directly into your embedding, so ranking reflects meaning in context, not just surface similarity. It's model-agnostic, works with whatever embeddings you're already using.
We saw Papr Graph improve existing embeddings on MTEB (coding, scifact, finance tasks) by 5-20%. On Stanford STaRK (MAG synthesized 10% dataset), Papr Graph leads retrieval models with 92% hit@5 accuracy.
Getting started is free. Keep your existing stack. Add our plugin. Drop graph-native ranking into your current retrieval flow with one API call.
I like the idea of retrieving based on correctness not just similarity. How do you evaluate that, like do you have benchmarks showing fewer hallucinated citations or better grounded answers?
About Papr Graph on Product Hunt
“Upgrade to graph-native vector embeddings”
Papr Graph launched on Product Hunt on May 19th, 2026 and earned 97 upvotes and 4 comments, placing #18 on the daily leaderboard. Papr Graph transforms semantic embeddings into graph-native embeddings with one API call. It encodes temporal, topical, and other dimensions within any embedding, helping agents retrieve answers based on correctness, not just semantic closeness.
Papr Graph was featured in API (98.2k followers), Developer Tools (512.7k followers) and Artificial Intelligence (468.9k followers) on Product Hunt. Together, these topics include over 173.1k products, making this a competitive space to launch in.
Who hunted Papr Graph?
Papr Graph was hunted by fmerian. 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 Papr Graph stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
fmerian
Hello everyone. I’m Amir, founder of Papr.
We built Papr Graph after seeing AI agents fail in production. The model wasn't the problem — retrieval was. Multi-hop questions, versioned policies, relational data — flat vector search breaks on all of it.
Vector search ranks by semantic closeness. But closeness ≠ correctness. A doc saying "aspirin reduces heart attack risk" and one saying "aspirin causes stomach bleeding" rank nearly identical — they're both about aspirin. For an agent making a recommendation, that's the difference between helpful and harmful.
Papr Graph is a graph-native embedding that sits between your existing embeddings and your agent. It encodes structured signals — topic, time, intent, entities, anything you define — directly into your embedding, so ranking reflects meaning in context, not just surface similarity. It's model-agnostic, works with whatever embeddings you're already using.
We saw Papr Graph improve existing embeddings on MTEB (coding, scifact, finance tasks) by 5-20%. On Stanford STaRK (MAG synthesized 10% dataset), Papr Graph leads retrieval models with 92% hit@5 accuracy.
Getting started is free. Keep your existing stack. Add our plugin. Drop graph-native ranking into your current retrieval flow with one API call.