AIMon is a full-cycle LLM app accuracy platform that provides real-time hallucination detection and remediation, ensuring adherence to user instructions and improving context quality. By optimizing LLM outputs through continuous monitoring and evaluation, AIMon addresses issues of hallucination, conciseness, and completeness across various model providers.
Funding
$2.3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
TVFounders
Product
Problem
Large language model (LLM) applications often suffer from inaccuracies, including hallucinations, instruction deviations, and incomplete information, which negatively impact user experience and safety. Current methods for detecting these issues are often slow, expensive, and lack the accuracy needed for reliable deployment.
Solution
AIMon provides a full-cycle LLM application accuracy platform that enables real-time hallucination detection and remediation, ensuring adherence to user instructions and improving context quality. The platform offers continuous monitoring and evaluation to optimize LLM outputs across various model providers. By identifying and addressing issues such as hallucinations, conciseness, and completeness, AIMon helps developers build more deterministic and reliable LLM applications. The platform can be deployed on-premise or hosted in the cloud, and it integrates seamlessly with any model provider or framework.
Target Audience
AIMon is designed for LLM application builders, ML engineers, researchers, and front-end or back-end engineers focused on increasing the accuracy and quality of their RAG retrieval and LLM outputs.
Features
- Hallucination Detection Model (HDM-2) identifies sentence and passage-level hallucinations with GPT-4 level accuracy at a fraction of the cost and latency.
- Instruction Adherence model checks if LLMs deviate from instructions with over 87% accuracy.
- Context Quality detector identifies context issues to troubleshoot and fix root causes of LLM hallucinations.
- Detectors for conciseness, completeness and toxicity.
- Unified API for evaluating LLM apps and testing the impact of different datasets, vector DBs, or LLM models on key metrics.
- SDKs in Python and Typescript for real-time or asynchronous instrumentation.
- Continuous monitoring capabilities to track LLM app performance in production.