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// Case study 09 · AI / MLOps
RapidFire AI
Test and optimise AI agents, many at once.
Runs hundreds of agentic-app configurations in parallel across preprocessing, retrieval, prompts and models to reach better eval metrics faster.
Core AI productML Infrastructure · LLMOps
https://www.rapidfire.ai/
2 months → 2 days
Case study
The problem
Tuning RAG and agent pipelines one experiment at a time takes months and wastes compute.
The solution
Start wide, stop weak runs early, clone the strongest and launch new variants mid-flight on existing resources.
How AI is used
Hyper-parallel experimentation for fine-tuning and RAG/agent pipelines with PyTorch, Hugging Face, LangChain and MLflow tracking.
// AI pipeline · live
How data flows through the model
01
Define config grid
02
Launch runs in parallel
03
Early stopping
04
Clone & mutate best
05
Converge on winner
// AI capability profile
// Key features
- Hundreds of agent versions at once
- Interactive control ops mid-run
- RAG & fine-tuning support
- Free Colab quick-start
// Tech stack
PythonPyTorchHugging FaceLangChainMLflowFramer site
Details verified against the live website in November 2026.