Insights
We build state-of-the-art RAG
infrastructure and applications
D-Star builds state-of-the-art RAG infrastructure and applications
If you need to build an LLM application that connects to external data sources, we're probably the best in the world to do it for you.
Zach and Nick McCormick
We're brothers who have been working together for our entire careers. We began by raising a few million dollars for a quant hedge fund right out of college. We ran that for five years before starting Superpowered AI (YC S22). Superpowered was one of the first RAG-as-a-service providers, so we've been building RAG systems since the GPT-3 days.
Our Offerings
Implementation
Advisory
Pushing the State-of-the-Art in RAG Performance
We love pushing the state-of-the-art in RAG performance. We've developed quite a bit of novel technology that improves accuracy for RAG applications. We're also big believers in open-source software, so we open-source a lot of the core RAG tech we develop.
dsRAG
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Stay up-to-date with our latest thoughts on RAG and LLM technologies.
FinanceBench is one of the best and most challenging RAG benchmarks available. It contains a corpus of 368 financial documents (mostly 10-Ks) and 150 questions, which are designed to be the sort of real-world questions a financial analyst might want to ask
Zach McCormick
Co-founder
We can think of the questions a user might ask over a document or set of documents as falling on a spectrum based on the length of the context string required to accurately answer the question. Let’s call this the optimal context length. On one end of the spectrum we have factoid question answering.
Zach McCormick
Co-founder
Many of the problems developers face with RAG come down to this: Individual chunks don’t contain sufficient context to be properly used by the retrieval system or the LLM. This leads to the inability to answer seemingly simple questions and, more worryingly, hallucinations.
Zach McCormick
Co-founder
Our Proven Approach
Frequently Asked Questions