Carbon AI: Connect external data to LLMs

Imagine you’re building AI agents with access to user data and have started looking for ways to improve responses beyond passing the user’s context into the prompt à la prompt engineer.

Introduction to RAG

You begin to experiment with RAG (Retrieval Augmented Generation) and start to chunk and vectorize some PDFs, then load them into your vector database. You’re excited about the results as the LLM responses start to look more contextually accurate.

But you run into an issue. Most user data you want to access is stuck in various file formats and within 3rd-party integrations like Notion and Google Drive.

The Challenge

Having users download and convert all their content into PDFs introduces too much friction. You realize the need now to set up an entire ingestion pipeline, managing everything from OAuth flows to custom parsers and scheduled syncs.

This isn’t what you signed up for – you wanted to build a GenAI app, not an ETL pipeline.

The Solution

This is where Carbon comes in. Carbon handles the heavy lifting on the ingestion side and makes building RAG into your GenAI app simple.

They built a high availability, high throughput, and low latency retrieval pipeline for you to leverage out of the box.

Below is a sample flow that shows how their customers, like AskAI, TypingMind, and Jenni.ai, integrate Carbon with the rest of their GenAI stack.

Carbon works with whatever vector store you already use, or you can rely on their in-house highly-performant vector database (<15 ms latency).