Overview
AIMon is an LLM App improvement platform that helps you:
- Evaluate,
- Continuously monitor,
- Troubleshoot, and
- Improve your LLM Apps
Our Team
Our team comprises of ML engineers, researchers, and product leaders from Netflix, Appdynamics, Stanford AI Lab, and NYU who are building ground up innovation focused on reliable Enterprise adoption of LLMs.
Who is AIMon for?
AIMon is built for pioneering LLM Application builders focused on increasing accuracy and quality of output in their RAG LLM apps. This can include ML engineers, researchers, front-end and back-end engineers, product managers, and security engineers working on LLM projects such as chatbots or summarization apps.
What AIMon is not
- AIMon does not call your LLMs to evaluate your outputs. We build our own models.
- AIMon is not an LLM provider. Neither do we host LLMs for our customers.
- AIMon is not a prompt management or an LLM IDE tool.
- AIMon is not well-suited for closed-book LLM apps. We work well in enterprise use-cases where contextual enrichment such as RAGs is used.
Our Goal
We are laser-focused on optimizing your LLM Apps and help you ship them with supreme confidence. That is why we work very closely with our LLM App builders and help them achieve their goals of instrumenting, discovering, and fixing quality problems with their specific LLM apps. Here is a quick way to understand the common ways in which our customers use us:
Detectors
At the heart of AIMon's platform are our proprietary detectors. Here are the highlights:
- Our Hallucination Detector (HDM-1) beats all commercial hallucination detectors.
- HDM-1 beats gpt-4o-mini and gpt-4-turbo on industry standard benchmarks.
- Our Instruction Adherence model achieves 87% accuracy on a custom IFEval based benchmark dataset.
Your LLM App and AIMon
Here is a reference architecture of how AIMon supports your LLM App Development. AIMon components are shown in Yellow. You can use our SDKs to instrument your app with AIMon either real-time or asyncronously. AIMon then detects issues with your LLM app and pinpoints root causes behind your LLM hallucinations.
AIMon components are shown in Yellow.
Let us take a close look at the AIMon components now. The following diagram describes how data is sent from your app to AIMon and how AIMon can help improve your LLM app.
AIMon high-level architecture.
What's next?
- Go to the Quick Start guide to get started with AIMon.
- Read about the different concepts of the AIMon Platform using the Glossary.
- Check out example applications that use AIMon's detectors: