Spykio: Upgrade your RAG with Knowledge Graphs! 🚀 Smarter than vector search – get truly relevant document results. Understands context & intent for higher accuracy. No chunking needed. Simple API/SDKs (Node, Py). Try Spykio now.

Hey everyone! Thrilled to be launching Spykio on Product Hunt today! We created Spykio to solve a problem we faced constantly: vector databases are good, but often not smart enough for sophisticated LLM applications. They tell you what's similar, but struggle when you need what's actually relevant. Spykio takes a different approach using dynamic knowledge graphs. It analyzes your documents (PDFs, DOCX, etc. – no chunking needed!) to understand the underlying concepts and connections. When you query, it uses this graph to find information based on semantic understanding, not just vector distance. Take for example law; a prompt based similarity search will not find the applicable law for your question. The goal? More accurate results, less frustration tuning RAG pipelines, and ultimately, better AI applications. We've focused on making it super easy to integrate via our SDKs (Node, Python, TS) or REST API. Curious to know: Are you currently using RAG? What are the biggest challenges? How important is retrieval accuracy vs. speed for your application? (We offer modes for both!) What kind of documents are you working with? Ask us anything! We're here all day to chat and answer questions. Looking forward to your feedback! 😊

As an added bonus; we added in a simple feature where you can share Chatbots based on your custom Data.
happy to hear some feedback :D

This is called gold mate keep it up!

@kshitij_mishra4 Thank you so much, we worked long and hard on this one :D

This is a killer direction—vector ≠ relevance, especially in nuanced domains. How does Spykio handle evolving context across multiple documents? Curious if the graph updates in real time or via scheduled reprocessing.

@shreyans_assistiv

Hi there, the knowledge graph part is a bit of a simplified term. it does not use a traditional approach like GraphRag; but instead we finetune a model on the fly to act as your index of the container. the drawback: Uploading documents can take a rather long time, and if you suddenly upload documents that are vastly different from your initial documents, the entire model requires a refresh (this can be done through the dashboard) the benefit: really accurate retrieval and still pretty fast! happy to chat more :D

Honestly, Spykio is a game-changer. At my company, we've been building legal AI-tools using RAG. We noticed RAG does allow the AI to understand some law, but wouldn't be able to actually retrieve the right law and answer questions accurately based on relevant law. So we looked towards Spykio to solve this, and guess what? It did ;)

Spykio

Vectordbs show what’s similar. We tell you what’s relevant

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