RapidFire AI has launched an open source toolkit that lets enterprises test multiple RAG configurations in parallel.
RapidFire AI has introduced an open source software package, RapidFire AI RAG, designed to streamline and accelerate Retrieval-Augmented Generation pipeline development. The release positions open-source experimentation infrastructure, rather than foundation models, as the core differentiator in enterprise AI.
RapidFire AI RAG extends the company’s “hyperparallel experimentation framework”, enabling developers to test and evaluate different chunking strategies, retrieval techniques, and prompting schemes simultaneously. These steps are traditionally conducted sequentially, often resulting in slow and fragmented evaluation cycles. Running them in parallel shortens experimentation time and strengthens the reliability of performance insights.
The challenge for enterprise teams lies in determining which configurations lead to trustworthy and consistent outcomes. As Madison May, Chief Technology Officer of Indico Data Solutions Inc., noted: “In enterprise AI, the hard part is not building the pipeline; it is knowing which combination of retrieval, chunking and prompts delivers trustworthy answers.”
Jack Norris, Co-founder and Chief Executive Officer of RapidFire AI, added: “They are not going to drive differentiation from the models, which are basically commodities. It is how they best leverage their data.”
Arun Kumar, Co-founder and Chief Technology Officer of RapidFire AI, highlighted the complexity of RAG optimisation: “People just brush under the carpet that there are these gazillion knobs in RAG… Every one of those interacts in nontrivial ways and can affect your evaluation metrics.”
RapidFire AI RAG supports real-time experiment control and dynamic resource allocation across GPUs, integrates with LangChain, and works with models from OpenAI, Anthropic and Hugging Face. It is available now via pip install rapidfireai-rag, with more than 1000 downloads recorded since soft launch. The company plans premium and SaaS offerings following this open source release.
