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Instant RAG API for AI Apps
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Add long-term memory to your AI applications with a single API call—no vector database setup, chunking logic, or embedding management required.
Added Dec 2, 2025
13 signals
The Problem
Developers building AI side projects and MVPs repeatedly waste 10-20 hours setting up vector databases (Pinecone, pgvector), writing document chunking logic, managing embedding API calls, and building retrieval queries. This tedious boilerplate prevents rapid idea validation and forces developers to become infrastructure experts instead of focusing on their core product.
Potential Solution
A managed API service that provides instant RAG capabilities. Developers send documents (PDFs, web URLs, text) to a single endpoint; the service handles automatic chunking, embedding generation, vector storage, and semantic retrieval. Query with one API call and get cited results—fully managed, automatically optimized for cost and performance.
Why Now?
The explosion of LLM applications has made RAG a standard requirement, but infrastructure setup remains the #1 bottleneck for developers moving from prototype to production. As the AI app ecosystem matures, developers demand plug-and-play solutions over DIY infrastructure.
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