LLAMAINDEX

Jerry Liu had the right background for this. He worked in machine learning at Uber, spent time at Quora, and did a stint at the quant hedge fund Two Sigma.

In November 2022 he started a side project called GPT Index. The idea was to help developers feed their own documents into a language model so it could answer questions about them.

ChatGPT launched weeks later and demand went vertical. He renamed it LlamaIndex, teamed up with Simon Suo, and turned the project into a company in 2023.

LlamaIndex solves one problem. A language model is smart, but it has never seen your company's contracts, PDFs, or internal wiki.

LlamaIndex is the bridge. Developers use it to feed private data into a model so the AI can answer from real documents instead of guessing.

That approach has a nickname, RAG, which just means retrieval-augmented generation. The open-source framework is free.

The money comes from LlamaCloud and LlamaParse, paid services that do the messy document work for companies at scale.

LlamaIndex is the open-source framework for connecting language models to your data. LlamaParse is the paid star, a tool that turns messy PDFs, slides, and tables into clean data an AI can actually use.

LlamaCloud is the managed platform for running all of this in production. LlamaHub is a library of ready-made connectors that pull data from hundreds of sources.

LlamaParse is the piece enterprises pay for.

Focus was the strategy. While LangChain tried to do everything, LlamaIndex went deep on one thing, connecting AI to your data.

That clarity won over developers who found the bigger frameworks bloated. The open-source project pulled in a huge community.

Then LlamaParse gave them a paid reason to stick around, because turning ugly PDFs into clean data is a problem every enterprise has. Solve one painful thing extremely well, then charge for the hard version.

LlamaIndex lives in a crowded, fast-moving lane. LangChain is right next door doing similar work with more funding.

The model makers keep adding native ways to connect data, which chips at the reason frameworks exist. And RAG itself is a moving target as models get bigger memories.

Staying the go-to tool means shipping faster than both the giants above them and the open-source crowd below them.

Greylock Partners led an $8.5 million seed round in 2023, an early vote of confidence when RAG was still a niche idea. In March 2025 Norwest Venture Partners led a $19 million Series A, with Greylock coming back in.

That brings total funding to about $27.5 million. The money is going toward the team and the push to make LlamaCloud enterprise-ready.