Papr.ai
Predictive memory and context intelligence API for AI Agents
166 followers
Predictive memory and context intelligence API for AI Agents
166 followers
Ranked #1 on Stanford’s STaRK benchmark with 91%+ retrieval accuracy and <100ms, Papr unifies RAG + memory in one API that reduces AI hallucinations and powers personalized agents. Papr’s predictive engine links and structures context into a vector index + knowledge graph you can query with GraphQL or natural language—great for agents and analytics UIs. With built-in ACLs and permission controls, data stays private and multi-tenant. Available in open-source for a local version or cloud edition.
This is the 4th launch from Papr.ai. View more
Papr Graph
Launching today
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.
Free
Launch Team / Built With
Papr.ai
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.
@amirkabbara Hi Amir, congrats on the launch. Isn't this more an issue of poor semantic structuring at the embed stage? Also, how do you automate the process of understanding the context of each vertical?