An AI chatbot that knows every page of your docs.
Lexora ingests your PDFs and text, builds a vector index, and serves a chatbot that retrieves the right passages — with citations. No hallucinations on what's in your data.
Page-level retrieval
Chunks are tagged with their source page. Answers cite back to file + page, so users can verify or click through.
Multi-document support
Stack multiple PDFs in one knowledge base. Top-5 chunks retrieved across all files before generation.
Distributed embeddings
BGE-M3 (1024-dim) embeddings generated on Lexora's network. Best-in-class multilingual retrieval for free.
Live updates, no retraining
Upload a new PDF, the chatbot starts using it immediately. No fine-tuning cycle, no batch jobs.
Lexora vs Self-hosted RAG stack
Side-by-side breakdown of what matters.
Feature
Lexora
Self-hosted RAG stack
Setup time
60 seconds
Days / weeks
Vector DB
Managed
Run / pay yourself
Embedding model
BGE-M3 hosted
Self-host or OpenAI
Citations
Built-in
Custom code
Inference billing
Per token
GPU rentals
Free tier
Yes — $1 + KB
Cloud bills from day 1
Stop building RAG from scratch.
Lexora handles extraction, chunking, embeddings, retrieval, and citation generation. You handle the product.
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